diff --git a/404.html b/404.html index 2f9087e..ff19883 100644 --- a/404.html +++ b/404.html @@ -8,8 +8,8 @@ Page not found (404) • anndataR - - + + diff --git a/LICENSE-text.html b/LICENSE-text.html index 7d973fb..300b448 100644 --- a/LICENSE-text.html +++ b/LICENSE-text.html @@ -1,5 +1,5 @@ -License • anndataR +License • anndataR Skip to contents diff --git a/LICENSE.html b/LICENSE.html index 7d6e6fe..1c57058 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -1,5 +1,5 @@ -MIT License • anndataR +MIT License • anndataR Skip to contents diff --git a/articles/anndataR.html b/articles/anndataR.html index 39dce68..75cee58 100644 --- a/articles/anndataR.html +++ b/articles/anndataR.html @@ -8,8 +8,8 @@ Using anndataR • anndataR - - + + @@ -210,7 +210,7 @@

Session info#> other attached packages: #> [1] SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0 #> [3] Biobase_2.66.0 GenomicRanges_1.58.0 -#> [5] GenomeInfoDb_1.42.0 IRanges_2.40.0 +#> [5] GenomeInfoDb_1.42.1 IRanges_2.40.1 #> [7] S4Vectors_0.44.0 BiocGenerics_0.52.0 #> [9] MatrixGenerics_1.18.0 matrixStats_1.4.1 #> [11] SeuratObject_5.0.2 sp_2.1-4 @@ -223,15 +223,15 @@

Session info#> [10] bookdown_0.41 fastmap_1.2.0 jsonlite_1.8.9 #> [13] Matrix_1.7-1 BiocManager_1.30.25 httr_1.4.7 #> [16] spam_2.11-0 UCSC.utils_1.2.0 codetools_0.2-20 -#> [19] textshaping_0.4.0 jquerylib_0.1.4 abind_1.4-8 +#> [19] textshaping_0.4.1 jquerylib_0.1.4 abind_1.4-8 #> [22] cli_3.6.3 crayon_1.5.3 rlang_1.1.4 -#> [25] XVector_0.46.0 parallelly_1.39.0 future.apply_1.11.3 +#> [25] XVector_0.46.0 parallelly_1.41.0 future.apply_1.11.3 #> [28] DelayedArray_0.32.0 cachem_1.1.0 yaml_2.3.10 #> [31] S4Arrays_1.6.0 tools_4.4.2 parallel_4.4.2 #> [34] GenomeInfoDbData_1.2.13 globals_0.16.3 R6_2.5.1 #> [37] lifecycle_1.0.4 zlibbioc_1.52.0 fs_1.6.5 #> [40] htmlwidgets_1.6.4 ragg_1.3.3 desc_1.4.3 -#> [43] progressr_0.15.0 pkgdown_2.1.1 bslib_0.8.0 +#> [43] progressr_0.15.1 pkgdown_2.1.1 bslib_0.8.0 #> [46] Rcpp_1.0.13-1 systemfonts_1.1.0 xfun_0.49 #> [49] knitr_1.49 htmltools_0.5.8.1 rmarkdown_2.29 #> [52] dotCall64_1.2 compiler_4.4.2 diff --git a/articles/design.html b/articles/design.html index abcd14b..033debb 100644 --- a/articles/design.html +++ b/articles/design.html @@ -8,8 +8,8 @@ Design • anndataR - - + + @@ -85,9 +85,9 @@

Design

Ideally, this package will be a complete replacement for all of these packages, and will be the go-to package for working with AnnData files in R.

-
-

Desired feature list -

+
+

Desired feature list +

  • Provide an R6 class to work with AnnData objects in R (either in-memory or on-disk).
    • @@ -96,9 +96,9 @@

    Desired feature list -

    Class diagram -

    +
    +

    Class diagram +

    Here is a diagram of the main R6 classes provided by the package:

    Notation:

    @@ -119,15 +119,14 @@

    Class diagram -

    Feature tracking -

    -

    The following tables show the status of the implementation of each -feature in the package:

    -

    -

    AnnData +

    Feature tracking

    +

    The following tables show the status of the implementation of each +feature in the package:

    +
    +

    AnnData +

    @@ -175,9 +174,9 @@

    AnnData

    -
    -

    HDF5AnnData -

    +
    +

    HDF5AnnData +

    @@ -274,9 +273,9 @@

    HDF5AnnData -

    InMemoryAnnData -

    +
    +

    InMemoryAnnData +

    @@ -373,9 +372,9 @@

    InMemoryAnnData

    -
    -

    Seurat -

    +
    +

    Seurat +

    @@ -472,9 +471,9 @@

    Seurat

    -
    -

    SingleCellExperiment -

    +
    +

    SingleCellExperiment +

    @@ -570,6 +569,114 @@

    SingleCellExperiment +

    Known issues +

    +
    +

    Issue: Integers are being converted to floats. +

    +
      +
    • Affected backend: HDF5AnnData +
      • +
    • Affected slot(s): X, layers, +obsp, varp, obsm, +varm +
      • +
    • Affected dtype(s): integer_csparse, +integer_rsparse, integer_matrix +
      • +
    • Probable cause: read
      • +
    • To investigate: TRUE
      • +
    • To fix: TRUE
      • +
    +
    +

    Error message +

    +
    Failure (test-roundtrip-obspvarp.R:111:5): Writing an AnnData with obsp and varp 'integer_csparse' works
+a$dtype (`actual`) not equal to b$dtype (`expected`).
+
+`class(actual)`:   "numpy.dtypes.Float64DType" "numpy.dtype" "python.builtin.object"
+`class(expected)`: "numpy.dtypes.Int64DType"   "numpy.dtype" "python.builtin.object"
    +
    +
    +

    Proposed solution +

    +

    Debug and fix

    +
    +
    +
    +

    Issue: Python nd.arrays have a dimension while R vectors do +not. +

    +
      +
    • Affected backend: HDF5AnnData +
      • +
    • Affected slot(s): obsm, varm +
      • +
    • Affected dtype(s): boolean_array, +categorical, categorical_missing_values, +categorical_ordered, +categorical_ordered_missing_values, +dense_array, integer_array, +nullable_boolean_array, +nullable_integer_array, string_array +
      • +
    • Probable cause: reticulate
      • +
    • To investigate: TRUE
      • +
    • To fix: TRUE
      • +
    +
    +

    Error message +

    +
    adata_r$varm[[name]] (`actual`) not equal to py_to_r(py_get_item(adata_py$varm, name)) (`expected`).
+
+`dim(actual)` is absent
+`dim(expected)` is an integer vector (20)
    +
    +
    +

    Proposed solution +

    +

    Debug and fix

    +
    +
    +
    +

    Issue: R vectors don’t have a dimension. +

    +
      +
    • Affected backend: HDF5AnnData +
      • +
    • Affected slot(s): obsm, varm +
      • +
    • Affected dtype(s): boolean_array, +categorical, categorical_missing_values, +categorical_ordered, +categorical_ordered_missing_values, +dense_array, integer_array, +nullable_boolean_array, +nullable_integer_array, string_array +
      • +
    • Probable cause: write
      • +
    • To investigate: TRUE
      • +
    • To fix: TRUE
      • +
    +
    +

    Error message +

    +
    Error in `if (found_dim != expected_dim) {
+    stop("dim(", label, ")[", i, "] should have shape: ", expected_dim, 
+        ", found: ", found_dim, ".")
+}`: argument is of length zero
    +
    +
    +

    Proposed solution +

    +

    The input checking function for obsm and varm should allow the object +to be a vector of the correct length instead of only a matrix or a data +frame.

    +
    +
    diff --git a/articles/index.html b/articles/index.html index b9c690b..280ffff 100644 --- a/articles/index.html +++ b/articles/index.html @@ -1,5 +1,5 @@ -Articles • anndataR +Articles • anndataR Skip to contents diff --git a/authors.html b/authors.html index c70140b..2f087b9 100644 --- a/authors.html +++ b/authors.html @@ -1,5 +1,5 @@ -Authors and Citation • anndataR +Authors and Citation • anndataR Skip to contents diff --git a/deps/data-deps.txt b/deps/data-deps.txt index 8aa49e2..ca4dfd9 100644 --- a/deps/data-deps.txt +++ b/deps/data-deps.txt @@ -2,8 +2,8 @@ - - + + diff --git a/deps/font-awesome-6.5.2/css/all.css b/deps/font-awesome-6.5.2/css/all.css new file mode 100644 index 0000000..151dd57 --- /dev/null +++ b/deps/font-awesome-6.5.2/css/all.css @@ -0,0 +1,8028 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, 0)); + transform: rotate(var(--fa-rotate-angle, 0)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-film::before { + content: "\f008"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-circle-exclamation::before { + content: "\f06a"; } + +.fa-exclamation-circle::before { + content: "\f06a"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-circle-chevron-down::before { + content: "\f13a"; } + +.fa-chevron-circle-down::before { + content: "\f13a"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-sitemap::before { + content: "\f0e8"; } + +.fa-circle-dollar-to-slot::before { + content: "\f4b9"; } + +.fa-donate::before { + content: "\f4b9"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-comment-slash::before { + content: "\f4b3"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-square-check::before { + content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-gamepad::before { + content: "\f11b"; } + +.fa-circle-dot::before { + content: "\f192"; } + +.fa-dot-circle::before { + content: "\f192"; } + +.fa-face-dizzy::before { + content: "\f567"; } + +.fa-dizzy::before { + content: "\f567"; } + +.fa-egg::before { + content: "\f7fb"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-futbol::before { + content: "\f1e3"; } + +.fa-futbol-ball::before { + content: "\f1e3"; } + +.fa-soccer-ball::before { + content: "\f1e3"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-lock::before { + content: "\f023"; } 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"\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-mitten::before { + content: "\f7b5"; } + +.fa-person-rays::before { + content: "\e54d"; } + +.fa-users::before { + content: "\f0c0"; } + +.fa-eye-slash::before { + content: "\f070"; } + +.fa-flask-vial::before { + content: "\e4f3"; } + +.fa-hand::before { + content: "\f256"; } + +.fa-hand-paper::before { + content: "\f256"; } + +.fa-om::before { + content: "\f679"; } + +.fa-worm::before { + content: "\e599"; } + +.fa-house-circle-xmark::before { + content: "\e50b"; } + +.fa-plug::before { + content: "\f1e6"; } + +.fa-chevron-up::before { + content: "\f077"; } + +.fa-hand-spock::before { + content: "\f259"; } + +.fa-stopwatch::before { + content: "\f2f2"; } + +.fa-face-kiss::before { + content: "\f596"; } + +.fa-kiss::before { + content: "\f596"; } + 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content: "\f0d0"; } + +.fa-dog::before { + content: "\f6d3"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-moon::before { + content: "\f186"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-cheese::before { + content: "\f7ef"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-music::before { + content: "\f001"; } + +.fa-code-commit::before { + content: "\f386"; } + +.fa-temperature-low::before { + content: "\f76b"; } + +.fa-person-biking::before { + content: "\f84a"; } + +.fa-biking::before { + content: "\f84a"; } + +.fa-broom::before { + content: "\f51a"; } + +.fa-shield-heart::before { + content: "\e574"; } + +.fa-gopuram::before { + content: "\f664"; } + +.fa-earth-oceania::before { + content: "\e47b"; } + +.fa-globe-oceania::before { + content: "\e47b"; } + +.fa-square-xmark::before { + content: "\f2d3"; } + +.fa-times-square::before { + content: "\f2d3"; } + +.fa-xmark-square::before { + content: "\f2d3"; } + +.fa-hashtag::before { + content: "\23"; } + +.fa-up-right-and-down-left-from-center::before { + content: "\f424"; } + +.fa-expand-alt::before { + content: "\f424"; } + +.fa-oil-can::before { + content: "\f613"; } + +.fa-t::before { + content: "\54"; } + +.fa-hippo::before { + content: "\f6ed"; } + +.fa-chart-column::before { + content: "\e0e3"; } + +.fa-infinity::before { + content: "\f534"; } + +.fa-vial-circle-check::before { + content: "\e596"; } + +.fa-person-arrow-down-to-line::before { + content: "\e538"; } + +.fa-voicemail::before { + content: "\f897"; } + +.fa-fan::before { + content: "\f863"; } + +.fa-person-walking-luggage::before { + content: "\e554"; } + +.fa-up-down::before { + content: "\f338"; } + +.fa-arrows-alt-v::before { + content: "\f338"; } + +.fa-cloud-moon-rain::before { + content: "\f73c"; } + +.fa-calendar::before { + content: "\f133"; } + +.fa-trailer::before { + content: "\e041"; } + +.fa-bahai::before { + content: "\f666"; } + 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} + +.fa-fire-alt::before { + content: "\f7e4"; } + +.fa-tornado::before { + content: "\f76f"; } + +.fa-file-circle-plus::before { + content: "\e494"; } + +.fa-book-quran::before { + content: "\f687"; } + +.fa-quran::before { + content: "\f687"; } + +.fa-anchor::before { + content: "\f13d"; } + +.fa-border-all::before { + content: "\f84c"; } + +.fa-face-angry::before { + content: "\f556"; } + +.fa-angry::before { + content: "\f556"; } + +.fa-cookie-bite::before { + content: "\f564"; } + +.fa-arrow-trend-down::before { + content: "\e097"; } + +.fa-rss::before { + content: "\f09e"; } + +.fa-feed::before { + content: "\f09e"; } + +.fa-draw-polygon::before { + content: "\f5ee"; } + +.fa-scale-balanced::before { + content: "\f24e"; } + +.fa-balance-scale::before { + content: "\f24e"; } + +.fa-gauge-simple-high::before { + content: "\f62a"; } + +.fa-tachometer::before { + content: "\f62a"; } + +.fa-tachometer-fast::before { + content: "\f62a"; } + +.fa-shower::before { + content: "\f2cc"; } + +.fa-desktop::before { + content: "\f390"; } + +.fa-desktop-alt::before { + content: "\f390"; } + +.fa-m::before { + content: "\4d"; } + +.fa-table-list::before { + content: "\f00b"; } + +.fa-th-list::before { + content: "\f00b"; } + +.fa-comment-sms::before { + content: "\f7cd"; } + +.fa-sms::before { + content: "\f7cd"; } + +.fa-book::before { + content: "\f02d"; } + +.fa-user-plus::before { + content: "\f234"; } + +.fa-check::before { + content: "\f00c"; } + +.fa-battery-three-quarters::before { + content: "\f241"; } + +.fa-battery-4::before { + content: "\f241"; } + +.fa-house-circle-check::before { + content: "\e509"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-diagram-successor::before { + content: "\e47a"; } + +.fa-truck-arrow-right::before { + content: "\e58b"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-person-falling::before { + content: "\e546"; } + +.fa-image-portrait::before { + content: "\f3e0"; } + +.fa-portrait::before { + content: "\f3e0"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-dungeon::before { + content: "\f6d9"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-money-bill-1-wave::before { + content: "\f53b"; } + +.fa-money-bill-wave-alt::before { + content: "\f53b"; } + +.fa-life-ring::before { + content: "\f1cd"; } + +.fa-hands::before { + content: "\f2a7"; } + +.fa-sign-language::before { + content: "\f2a7"; } + +.fa-signing::before { + content: "\f2a7"; } + +.fa-calendar-day::before { + content: "\f783"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-arrows-up-down::before { + content: "\f07d"; } + +.fa-arrows-v::before { + content: "\f07d"; } + +.fa-face-grimace::before { + content: "\f57f"; } + +.fa-grimace::before { + content: "\f57f"; } + +.fa-wheelchair-move::before { + content: "\e2ce"; } + +.fa-wheelchair-alt::before { + content: "\e2ce"; } + +.fa-turn-down::before { + content: "\f3be"; } + +.fa-level-down-alt::before { + content: "\f3be"; } + +.fa-person-walking-arrow-right::before { + content: "\e552"; } + +.fa-square-envelope::before { + content: "\f199"; } + +.fa-envelope-square::before { + content: "\f199"; } + +.fa-dice::before { + content: "\f522"; } + +.fa-bowling-ball::before { + content: "\f436"; } + +.fa-brain::before { + content: "\f5dc"; } + +.fa-bandage::before { + content: "\f462"; } + +.fa-band-aid::before { + content: "\f462"; } + +.fa-calendar-minus::before { + content: "\f272"; } + +.fa-circle-xmark::before { + content: "\f057"; } + +.fa-times-circle::before { + content: "\f057"; } + +.fa-xmark-circle::before { + content: "\f057"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-earth-asia::before { + content: "\f57e"; } + +.fa-globe-asia::before { + content: "\f57e"; } + +.fa-id-card-clip::before { + content: "\f47f"; } + +.fa-id-card-alt::before { + content: "\f47f"; } + +.fa-magnifying-glass-plus::before { + content: "\f00e"; } + +.fa-search-plus::before { + content: "\f00e"; } + +.fa-thumbs-up::before { + content: "\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-pixiv:before { + content: "\e640"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + +.fa-js-square:before { + content: "\f3b9"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-jxl:before { + content: "\e67b"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-brave:before { + content: "\e63c"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: 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+.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + 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} + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; 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} + +.fa.fa-themeisle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official:before { + content: "\f2b3"; } + +.fa.fa-google-plus-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-circle:before { + content: "\f2b3"; } + +.fa.fa-font-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa:before { + content: "\f2b4"; } + +.fa.fa-handshake-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-handshake-o:before { + content: "\f2b5"; } + +.fa.fa-envelope-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-envelope-open-o:before { + content: "\f2b6"; } + +.fa.fa-linode { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-address-book-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-book-o:before { + content: "\f2b9"; } + +.fa.fa-vcard:before { + content: "\f2bb"; } + +.fa.fa-address-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-card-o:before { + content: "\f2bb"; } + +.fa.fa-vcard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-vcard-o:before { + content: "\f2bb"; } + +.fa.fa-user-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-circle-o:before { + content: "\f2bd"; } + +.fa.fa-user-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-o:before { + content: "\f007"; } + +.fa.fa-id-badge { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-drivers-license:before { + content: "\f2c2"; } + +.fa.fa-id-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-id-card-o:before { + content: "\f2c2"; } + +.fa.fa-drivers-license-o 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content: "\f2dc"; } + +.fa.fa-superpowers { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpexplorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-meetup { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } diff --git a/deps/font-awesome-6.5.2/css/v4-shims.min.css b/deps/font-awesome-6.5.2/css/v4-shims.min.css new file mode 100644 index 0000000..09baf5f --- /dev/null +++ b/deps/font-awesome-6.5.2/css/v4-shims.min.css @@ -0,0 +1,6 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 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eecf0d6..8bd6d8a 100644 --- a/news/index.html +++ b/news/index.html @@ -1,5 +1,5 @@ -Changelog • anndataR +Changelog • anndataR Skip to contents diff --git a/pkgdown.yml b/pkgdown.yml index a7ec8d8..cb4b0d2 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -4,7 +4,7 @@ pkgdown_sha: ~ articles: anndataR: anndataR.html design: design.html -last_built: 2024-11-15T10:09Z +last_built: 2024-12-19T19:13Z urls: reference: https://anndataR.data-intuitive.com/reference article: https://anndataR.data-intuitive.com/articles diff --git a/reference/AnnData.html b/reference/AnnData.html index 85e1774..f74c295 100644 --- a/reference/AnnData.html +++ b/reference/AnnData.html @@ -1,5 +1,5 @@ -Create an in-memory AnnData object. — AnnData • anndataR +Create an in-memory AnnData object. — AnnData • anndataR Skip to contents diff --git a/reference/anndataR-package.html b/reference/anndataR-package.html index 356b061..9da8abd 100644 --- a/reference/anndataR-package.html +++ b/reference/anndataR-package.html @@ -1,5 +1,5 @@ -anndataR: AnnData interoperability in R — anndataR-package • anndataRanndataR: AnnData interoperability in R — anndataR-package • anndataRConvert a Seurat object to an AnnData object — from_Seurat • anndataRConvert a Seurat object to an AnnData object — from_Seurat • anndataRExamples#> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric #> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' #> This message will be shown once per session -#> 10:09:28 UMAP embedding parameters a = 0.9922 b = 1.112 -#> 10:09:28 Read 200 rows and found 10 numeric columns -#> 10:09:28 Using Annoy for neighbor search, n_neighbors = 30 -#> 10:09:28 Building Annoy index with metric = cosine, n_trees = 50 +#> 19:13:16 UMAP embedding parameters a = 0.9922 b = 1.112 +#> 19:13:16 Read 200 rows and found 10 numeric columns +#> 19:13:16 Using Annoy for neighbor search, n_neighbors = 30 +#> 19:13:16 Building Annoy index with metric = cosine, n_trees = 50 #> 0% 10 20 30 40 50 60 70 80 90 100% #> [----|----|----|----|----|----|----|----|----|----| #> * @@ -326,14 +326,14 @@

    Examples#> * #> * #> | -#> 10:09:28 Writing NN index file to temp file /tmp/RtmpePOFm4/file17c22df9ca6b -#> 10:09:28 Searching Annoy index using 1 thread, search_k = 3000 -#> 10:09:28 Annoy recall = 100% -#> 10:09:28 Commencing smooth kNN distance calibration using 1 thread +#> 19:13:16 Writing NN index file to temp file /tmp/RtmpajMDBu/file24665b142657 +#> 19:13:16 Searching Annoy index using 1 thread, search_k = 3000 +#> 19:13:17 Annoy recall = 100% +#> 19:13:17 Commencing smooth kNN distance calibration using 1 thread #> with target n_neighbors = 30 -#> 10:09:29 Initializing from normalized Laplacian + noise (using RSpectra) -#> 10:09:29 Commencing optimization for 500 epochs, with 6160 positive edges -#> 10:09:29 Optimization finished +#> 19:13:17 Initializing from normalized Laplacian + noise (using RSpectra) +#> 19:13:17 Commencing optimization for 500 epochs, with 6160 positive edges +#> 19:13:18 Optimization finished from_Seurat(obj) #> AnnData object with n_obs × n_vars = 200 × 100 #> obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA' diff --git a/reference/from_SingleCellExperiment.html b/reference/from_SingleCellExperiment.html index 52e83cf..f462665 100644 --- a/reference/from_SingleCellExperiment.html +++ b/reference/from_SingleCellExperiment.html @@ -1,5 +1,5 @@ -Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment • anndataRConvert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment • anndataR Skip to contents @@ -153,7 +153,6 @@

    Examples#> The following objects are masked from ‘package:matrixStats’: #> #> anyMissing, rowMedians -#> Warning: replacing previous import ‘S4Arrays::makeNindexFromArrayViewport’ by ‘DelayedArray::makeNindexFromArrayViewport’ when loading ‘SummarizedExperiment’ #> #> Attaching package: ‘SummarizedExperiment’ #> The following object is masked from ‘package:Seurat’: diff --git a/reference/generate_dataset.html b/reference/generate_dataset.html index 24a35a8..1b469cd 100644 --- a/reference/generate_dataset.html +++ b/reference/generate_dataset.html @@ -1,5 +1,5 @@ -Generate a dataset — generate_dataset • anndataR +Generate a dataset — generate_dataset • anndataR Skip to contents diff --git a/reference/index.html b/reference/index.html index 45492a7..b4ec6f3 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,5 +1,5 @@ -Package index • anndataR +Package index • anndataR Skip to contents diff --git a/reference/read_h5ad.html b/reference/read_h5ad.html index 5c34941..8b9aed7 100644 --- a/reference/read_h5ad.html +++ b/reference/read_h5ad.html @@ -1,5 +1,5 @@ -Read H5AD — read_h5ad • anndataR +Read H5AD — read_h5ad • anndataR Skip to contents diff --git a/reference/to_Seurat.html b/reference/to_Seurat.html index ca95590..3f0309f 100644 --- a/reference/to_Seurat.html +++ b/reference/to_Seurat.html @@ -1,5 +1,5 @@ -Convert a Seurat object to an AnnData object — to_Seurat • anndataRConvert a Seurat object to an AnnData object — to_Seurat • anndataRWrite H5AD — write_h5ad • anndataR +Write H5AD — write_h5ad • anndataR Skip to contents diff --git a/search.json b/search.json index b45a5f6..e5e1169 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://anndataR.data-intuitive.com/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 Robrecht Cannoodt, Luke Zappia, Martin Morgan, Louise Deconinck Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Using anndataR","text":"Install using:","code":"if (!require(\"pak\", quietly = TRUE)) { install.packages(\"pak\") } pak::pak(\"scverse/anndataR\")"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"usage","dir":"Articles","previous_headings":"","what":"Usage","title":"Using anndataR","text":"’s quick example use {anndataR}. First, download h5ad file. Read h5ad file memory: Read h5ad file disk: View structure: Access AnnData slots:","code":"library(anndataR) h5ad_path <- system.file(\"extdata\", \"example.h5ad\", package = \"anndataR\") adata <- read_h5ad(h5ad_path) adata <- read_h5ad(h5ad_path, to = \"HDF5AnnData\") adata #> AnnData object with n_obs × n_vars = 50 × 100 #> obs: 'Float', 'FloatNA', 'Int', 'IntNA', 'Bool', 'BoolNA', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'leiden' #> var: 'String', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm' #> obsm: 'X_pca', 'X_umap' #> varm: 'PCs' #> layers: 'counts', 'csc_counts', 'dense_X', 'dense_counts' #> obsp: 'connectivities', 'distances' dim(adata$X) #> [1] 50 100 adata$obs[1:5, 1:6] #> Float FloatNA Int IntNA Bool BoolNA #> Cell000 42.42 NaN 0 NA FALSE FALSE #> Cell001 42.42 42.42 1 42 TRUE NA #> Cell002 42.42 42.42 2 42 TRUE TRUE #> Cell003 42.42 42.42 3 42 TRUE TRUE #> Cell004 42.42 42.42 4 42 TRUE TRUE adata$var[1:5, 1:6] #> String n_cells_by_counts mean_counts log1p_mean_counts pct_dropout_by_counts total_counts #> Gene000 String0 44 1.94 1.078410 12 97 #> Gene001 String1 42 2.04 1.111858 16 102 #> Gene002 String2 43 2.12 1.137833 14 106 #> Gene003 String3 41 1.72 1.000632 18 86 #> Gene004 String4 42 2.06 1.118415 16 103"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"interoperability","dir":"Articles","previous_headings":"Usage","what":"Interoperability","title":"Using anndataR","text":"Convert AnnData object SingleCellExperiment object: Convert AnnData object Seurat object:","code":"sce <- adata$to_SingleCellExperiment() sce #> class: SingleCellExperiment #> dim: 100 50 #> metadata(0): #> assays(5): X counts csc_counts dense_X dense_counts #> rownames(100): Gene000 Gene001 ... Gene098 Gene099 #> rowData names(11): String n_cells_by_counts ... dispersions dispersions_norm #> colnames(50): Cell000 Cell001 ... Cell048 Cell049 #> colData names(11): Float FloatNA ... log1p_total_counts leiden #> reducedDimNames(0): #> mainExpName: NULL #> altExpNames(0): obj <- adata$to_Seurat() obj #> An object of class Seurat #> 500 features across 50 samples within 5 assays #> Active assay: RNA (100 features, 0 variable features) #> 2 layers present: counts, data #> 4 other assays present: counts, csc_counts, dense_X, dense_counts"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"manually-create-an-object","dir":"Articles","previous_headings":"Usage","what":"Manually create an object","title":"Using anndataR","text":"","code":"adata <- AnnData( X = matrix(rnorm(100), nrow = 10), obs = data.frame( cell_type = factor(rep(c(\"A\", \"B\"), each = 5)) ), var = data.frame( gene_name = paste0(\"gene_\", 1:10) ) ) adata #> AnnData object with n_obs × n_vars = 10 × 10 #> obs: 'cell_type' #> var: 'gene_name'"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Using anndataR","text":"","code":"sessionInfo() #> R version 4.4.2 (2024-10-31) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0 #> [3] Biobase_2.66.0 GenomicRanges_1.58.0 #> [5] GenomeInfoDb_1.42.0 IRanges_2.40.0 #> [7] S4Vectors_0.44.0 BiocGenerics_0.52.0 #> [9] MatrixGenerics_1.18.0 matrixStats_1.4.1 #> [11] SeuratObject_5.0.2 sp_2.1-4 #> [13] BiocStyle_2.34.0 #> #> loaded via a namespace (and not attached): #> [1] sass_0.4.9 future_1.34.0 generics_0.1.3 #> [4] SparseArray_1.6.0 lattice_0.22-6 listenv_0.9.1 #> [7] digest_0.6.37 evaluate_1.0.1 grid_4.4.2 #> [10] bookdown_0.41 fastmap_1.2.0 jsonlite_1.8.9 #> [13] Matrix_1.7-1 BiocManager_1.30.25 httr_1.4.7 #> [16] spam_2.11-0 UCSC.utils_1.2.0 codetools_0.2-20 #> [19] textshaping_0.4.0 jquerylib_0.1.4 abind_1.4-8 #> [22] cli_3.6.3 crayon_1.5.3 rlang_1.1.4 #> [25] XVector_0.46.0 parallelly_1.39.0 future.apply_1.11.3 #> [28] DelayedArray_0.32.0 cachem_1.1.0 yaml_2.3.10 #> [31] S4Arrays_1.6.0 tools_4.4.2 parallel_4.4.2 #> [34] GenomeInfoDbData_1.2.13 globals_0.16.3 R6_2.5.1 #> [37] lifecycle_1.0.4 zlibbioc_1.52.0 fs_1.6.5 #> [40] htmlwidgets_1.6.4 ragg_1.3.3 desc_1.4.3 #> [43] progressr_0.15.0 pkgdown_2.1.1 bslib_0.8.0 #> [46] Rcpp_1.0.13-1 systemfonts_1.1.0 xfun_0.49 #> [49] knitr_1.49 htmltools_0.5.8.1 rmarkdown_2.29 #> [52] dotCall64_1.2 compiler_4.4.2"},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"desired-feature-list","dir":"Articles","previous_headings":"","what":"Desired feature list","title":"Design","text":"Provide R6 class work AnnData objects R (either -memory -disk). Read/write *.h5ad files natively Convert /SingleCellExperiment objects Convert /Seurat objects","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"class-diagram","dir":"Articles","previous_headings":"","what":"Class diagram","title":"Design","text":"diagram main R6 classes provided package: Notation: X: Matrix - variable X type Matrix *X: Matrix - variable X abstract to_SingleCellExperiment(): SingleCellExperiment - function to_SingleCellExperiment returns object type SingleCellExperiment *to_SingleCellExperiment() - function to_SingleCellExperiment abstract","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"feature-tracking","dir":"Articles","previous_headings":"","what":"Feature tracking","title":"Design","text":"following tables show status implementation feature package:","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://anndataR.data-intuitive.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robrecht Cannoodt. Author, maintainer. rcannood Luke Zappia. Author. lazappi Martin Morgan. Author. mtmorgan Louise Deconinck. Author. LouiseDck Danila Bredikhin. Contributor. gtca Isaac Virshup. Contributor. ivirshup Brian Schilder. Contributor. bschilder Chananchida Sang-aram. Contributor. csangara Data Intuitive. Funder, copyright holder. Chan Zuckerberg Initiative. Funder.","code":""},{"path":"https://anndataR.data-intuitive.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cannoodt R, Zappia L, Morgan M, Deconinck L (2024). anndataR: AnnData interoperability R. R package version 0.99.0, https://github.com/scverse/anndataR, https://scverse.org/anndataR.","code":"@Manual{, title = {anndataR: AnnData interoperability in R}, author = {Robrecht Cannoodt and Luke Zappia and Martin Morgan and Louise Deconinck}, year = {2024}, note = {R package version 0.99.0, https://github.com/scverse/anndataR}, url = {https://scverse.org/anndataR}, }"},{"path":"https://anndataR.data-intuitive.com/index.html","id":"anndatar-an-r-package-for-working-with-anndata-objects","dir":"","previous_headings":"","what":"AnnData interoperability in R","title":"AnnData interoperability in R","text":"{anndataR} aims make AnnData format first-class citizen R ecosystem, make easy work AnnData files R, either directly converting SingleCellExperiment Seurat object. {anndataR} scverse community project maintained Data Intuitive, fiscally sponsored Chan Zuckerberg Initiative.","code":""},{"path":"https://anndataR.data-intuitive.com/index.html","id":"features-of-anndatar","dir":"","previous_headings":"","what":"Features of {anndataR}","title":"AnnData interoperability in R","text":"Provide R6 class work AnnData objects R (either -memory -disk). Read/write *.h5ad files natively Convert /SingleCellExperiment objects Convert /Seurat objects [!WARNING] package still experimental stage, may work expected. can find status development anndataR feature tracking page website. Please report issues encounter.","code":""},{"path":"https://anndataR.data-intuitive.com/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"AnnData interoperability in R","text":"can install development version {anndataR} like : need install additional dependencies, depending task want perform. read/write *.h5ad files, install hdf5r:install.packages(\"hdf5r\") convert /SingleCellExperiment objects, install SingleCellExperiment:BiocManager::install(\"SingleCellExperiment\") convert /Seurat objects, install SeuratObject:install.packages(\"SeuratObject\") Alternatively, can install suggested dependencies :","code":"# install.packages(\"pak\") pak::pak(\"scverse/anndataR\") pak::pak(\"scverse/anndataR\", dependencies = TRUE)"},{"path":"https://anndataR.data-intuitive.com/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"AnnData interoperability in R","text":"best way get started {anndataR} explore package vignettes (available https://anndatar.data-intuitive.com/articles/). Getting started: introduction package features.vignette(\"anndataR\", package = \"anndataR\") Design: overview design package.vignette(\"design\", package = \"anndataR\")","code":""},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an in-memory AnnData object. — AnnData","title":"Create an in-memory AnnData object. — AnnData","text":"information functionality AnnData object, see anndataR-package.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an in-memory AnnData object. — AnnData","text":"","code":"AnnData( X = NULL, obs = NULL, var = NULL, layers = NULL, obsm = NULL, varm = NULL, obsp = NULL, varp = NULL, uns = NULL, shape = shape )"},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an in-memory AnnData object. — AnnData","text":"X Either NULL observation × variable matrix dimensions consistent obs var. obs Either NULL data.frame columns containing information observations. NULL, n_obs×0 data frame automatically generated. var Either NULL data.frame columns containing information variables. NULL, n_vars×0 data frame automatically generated. layers Either NULL named list, element observation × variable matrix dimensions consistent obs var. obsm obsm slot used store multi-dimensional annotation arrays. must either NULL named list, element matrix n_obs rows arbitrary number columns. varm varm slot used store multi-dimensional annotation arrays. must either NULL named list, element matrix n_vars rows arbitrary number columns. obsp obsp slot used store sparse multi-dimensional annotation arrays. must either NULL named list, element sparse matrix dimension length n_obs. varp varp slot used store sparse multi-dimensional annotation arrays. must either NULL named list, element sparse matrix dimension length n_vars. uns uns slot used store unstructured annotation. must either NULL named list. shape Shape tuple (#observations, #variables). Can provided X obs var provided.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an in-memory AnnData object. — AnnData","text":"-memory AnnData object.","code":""},{"path":[]},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an in-memory AnnData object. — AnnData","text":"","code":"adata <- AnnData( X = matrix(1:12, nrow = 3, ncol = 4), obs = data.frame( row.names = paste0(\"obs\", 1:3), n_counts = c(1, 2, 3), n_cells = c(1, 2, 3) ), var = data.frame( row.names = paste0(\"var\", 1:4), n_cells = c(1, 2, 3, 4) ) ) adata #> AnnData object with n_obs × n_vars = 3 × 4 #> obs: 'n_counts', 'n_cells' #> var: 'n_cells'"},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":null,"dir":"Reference","previous_headings":"","what":"anndataR: AnnData interoperability in R — anndataR-package","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"AnnData object. class can either -memory AnnData (InMemoryAnnData) HDF5-backed AnnData (HDF5AnnData). AnnData object stores data matrix X together annotations observations obs (obsm, obsp) variables var (varm, varp). Additional layers data can stored layers unstructured annotations uns.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"functions-that-can-be-used-to-create-anndata-objects","dir":"Reference","previous_headings":"","what":"Functions that can be used to create AnnData objects","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"AnnData(): Create -memory AnnData object. read_h5ad(): Read HDF5-backed AnnData file disk. from_SingleCellExperiment(): Convert SingleCellExperiment object AnnData object. from_Seurat(): Convert Seurat object AnnData object.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"an-anndata-object-has-the-following-slots","dir":"Reference","previous_headings":"","what":"An AnnData object has the following slots","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Access using $ operator. Example: adata$obs. X: matrix observations variables. obs: data frame observations. var: data frame variables. obs_names: Names observations (alias rownames(obs)). var_names: Names variables (alias rownames(var)). layers: named list matrices dimensions X. obsm: named list matrices number rows obs. varm: named list matrices number rows var. obsp: named list sparse matrices number rows columns number observations. varp: named list sparse matrices number rows columns number variables. uns: named list unstructured annotations.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"an-anndata-object-has-the-following-methods","dir":"Reference","previous_headings":"","what":"An AnnData object has the following methods","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Access using $ operator. Example: adata$write_h5ad(). print(): Print summary AnnData object. shape(): Dimensions (observations x variables) AnnData object. n_obs(): Number observations AnnData object. n_vars(): Number variables AnnData object. obs_keys(): Column names obs. var_keys(): Column names var. layers_keys(): Element names layers. obsm_keys(): Element names obsm. varm_keys(): Element names varm. obsp_keys(): Element names obsp. varp_keys(): Element names varp. uns_keys(): Element names uns. write_h5ad(): Write AnnData object HDF5 file.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"conversion-methods","dir":"Reference","previous_headings":"","what":"Conversion methods","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Access using $ operator. Example: adata$to_Seurat(). to_SingleCellExperiment(): Convert SingleCellExperiment. to_Seurat(): Convert Seurat. to_InMemoryAnnData(): Convert InMemory AnnData. to_HDF5AnnData(): Convert HDF5 Backed AnnData.","code":""},{"path":[]},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Maintainer: Robrecht Cannoodt robrecht@data-intuitive.com (ORCID) (rcannood) Authors: Luke Zappia luke@lazappi.id.au (ORCID) (lazappi) Martin Morgan mtmorgan.bioc@gmail.com (ORCID) (mtmorgan) Louise Deconinck louise.deconinck@gmail.com (ORCID) (LouiseDck) contributors: Danila Bredikhin danila.bredikhin@embl.de (ORCID) (gtca) [contributor] Isaac Virshup (ORCID) (ivirshup) [contributor] Brian Schilder brian_schilder@alumni.brown.edu (ORCID) (bschilder) [contributor] Chananchida Sang-aram (ORCID) (csangara) [contributor] Data Intuitive info@data-intuitive.com [funder, copyright holder] Chan Zuckerberg Initiative [funder]","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Seurat object to an AnnData object — from_Seurat","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"from_Seurat() converts Seurat object AnnData object. one assay can converted time. Arguments used configure conversion. NULL, functions from_Seurat_guess_* used guess mapping.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"","code":"from_Seurat( seurat_obj, output_class = c(\"InMemoryAnnData\", \"HDF5AnnData\"), assay_name = NULL, x_mapping = NULL, layers_mapping = NULL, obsm_mapping = NULL, varm_mapping = NULL, obsp_mapping = NULL, varp_mapping = NULL, uns_mapping = NULL, ... )"},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"seurat_obj Seurat object converted. output_class Name AnnData class. Must one \"HDF5AnnData\" \"InMemoryAnnData\". assay_name name assay converted. NULL, default assay used (SeuratObject::DefaultAssay()). x_mapping mapping Seurat layer AnnData X slot. NULL, data copied X slot. layers_mapping named list mapping layer names names layers Seurat object. item list must character vector length 1. See section \"$layers mapping\" details. obsm_mapping named list mapping reductions names reductions Seurat object. item list must vector length 2. See section \"$obsm mapping\" details. varm_mapping named list mapping PCA loadings names PCA loadings Seurat object. item list must character vector length 1. See section \"$varm mapping\" details. obsp_mapping named list mapping graph names names graphs Seurat object. item list must character vector length 1. See section \"$obsp mapping\" details. varp_mapping named list mapping miscellaneous data names data Seurat object. item list must named list one two elements. See section \"$varp mapping\" details. uns_mapping named list mapping miscellaneous data names data Seurat object. item list must named list one two elements. See section \"$uns mapping\" details. ... Additional arguments passed generator function.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"AnnData object","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"information functionality AnnData object, see anndataR-package.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-x-mapping","dir":"Reference","previous_headings":"","what":"$X mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"mapping Seurat layer AnnData X slot. value must NULL character vector Seurat layer name copy. NULL, data copied X slot.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-layers-mapping","dir":"Reference","previous_headings":"","what":"$layers mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map AnnData layers Seurat layers. item list must character vector length 1. $X key maps X slot. Example: layers_mapping = list(counts = \"counts\", foo = \"bar\"). NULL, internal function from_Seurat_guess_layers used guess layer mapping follows: Seurat layers copied AnnData layers name. means AnnData X slot NULL (empty). want copy data X slot, must define layer mapping explicitly.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-obsm-mapping","dir":"Reference","previous_headings":"","what":"$obsm mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat reductions AnnData $obsm. item list must vector length 2, name corresponds name resulting $obsm slot, values correspond location data Seurat object. Example: obsm_mapping = list(pca = c(\"reductions\", \"pca\"), umap = c(\"reductions\", \"umap\")). NULL, internal function from_Seurat_guess_obsms used guess obsm mapping follows: Seurat reductions prefixed X_ copied AnnData $obsm.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-varm-mapping","dir":"Reference","previous_headings":"","what":"$varm mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat reduction loadings AnnData $varm. item list must character vector length 2, name corresponds name resulting $varm slot, value corresponds location data Seurat object. Example: varm_mapping = list(PCs = c(\"reductions\", \"pca\"). NULL, internal function from_Seurat_guess_varms used guess varm mapping follows: name PCA loadings copied name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-obsp-mapping","dir":"Reference","previous_headings":"","what":"$obsp mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat graphs AnnData $obsp. Example: obsp_mapping = list(nn = \"connectivities\"). NULL, internal function from_Seurat_guess_obsps used guess obsp mapping follows: Seurat graphs copied $obsp name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-varp-mapping","dir":"Reference","previous_headings":"","what":"$varp mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat miscellaneous data AnnData $varp. name item corresponds resulting $varp slot, value item must fector corresponds location data Seurat object. Example: varp_mapping = list(foo = c(\"misc\", \"foo\")). NULL, internal function from_Seurat_guess_varps used guess varp mapping follows: data mapped $varp.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-uns-mapping","dir":"Reference","previous_headings":"","what":"$uns mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat miscellaneous data AnnData uns. item list must character length 2. first element must \"misc\". second element name data corresponding slot. Example: uns_mapping = list(foo = c(\"misc\", \"foo\")). NULL, internal function from_Seurat_guess_uns used guess uns mapping follows: Seurat miscellaneous data copied uns name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"","code":"library(Seurat) #> Loading required package: SeuratObject #> Loading required package: sp #> ‘SeuratObject’ was built under R 4.4.0 but the current version is #> 4.4.2; it is recomended that you reinstall ‘SeuratObject’ as the ABI #> for R may have changed #> #> Attaching package: ‘SeuratObject’ #> The following objects are masked from ‘package:base’: #> #> intersect, t counts <- matrix(rbinom(20000, 1000, .001), nrow = 100) obj <- CreateSeuratObject(counts = counts) #> Warning: Data is of class matrix. Coercing to dgCMatrix. obj <- NormalizeData(obj) #> Normalizing layer: counts obj <- FindVariableFeatures(obj) #> Finding variable features for layer counts obj <- ScaleData(obj) #> Centering and scaling data matrix obj <- RunPCA(obj, npcs = 10L) #> PC_ 1 #> Positive: Feature72, Feature61, Feature35, Feature78, Feature52, Feature50, Feature66, Feature8, Feature14, Feature32 #> \t Feature70, Feature1, Feature56, Feature87, Feature19, Feature97, Feature7, Feature17, Feature86, Feature48 #> \t Feature81, Feature88, Feature43, Feature67, Feature36, Feature3, Feature26, Feature54, Feature40, Feature33 #> Negative: Feature9, Feature29, Feature4, Feature39, Feature15, Feature27, Feature34, Feature68, Feature62, Feature18 #> \t Feature22, Feature46, Feature10, Feature74, Feature49, Feature37, Feature12, Feature23, Feature95, Feature100 #> \t Feature64, Feature30, Feature90, Feature55, Feature47, Feature96, Feature5, Feature31, Feature60, Feature92 #> PC_ 2 #> Positive: Feature41, Feature89, Feature45, Feature98, Feature33, Feature44, Feature68, Feature91, Feature18, Feature59 #> \t Feature61, Feature86, Feature32, Feature4, Feature51, Feature42, Feature54, Feature67, Feature56, Feature21 #> \t Feature53, Feature70, Feature8, Feature31, Feature22, Feature14, Feature12, Feature76, Feature62, Feature58 #> Negative: Feature97, Feature13, Feature48, Feature69, Feature49, Feature82, Feature7, Feature5, Feature79, Feature93 #> \t Feature94, Feature84, Feature23, Feature78, Feature27, Feature40, Feature26, Feature50, Feature81, Feature46 #> \t Feature29, Feature10, Feature71, Feature72, Feature52, Feature88, Feature87, Feature9, Feature95, Feature16 #> PC_ 3 #> Positive: Feature3, Feature93, Feature2, Feature5, Feature11, Feature79, Feature66, Feature74, Feature50, Feature36 #> \t Feature78, Feature12, Feature45, Feature98, Feature51, Feature16, Feature83, Feature28, Feature27, Feature9 #> \t Feature8, Feature95, Feature33, Feature63, Feature21, Feature87, Feature40, Feature53, Feature85, Feature57 #> Negative: Feature58, Feature90, Feature46, Feature81, Feature44, Feature56, Feature69, Feature86, Feature37, Feature97 #> \t Feature92, Feature26, Feature88, Feature30, Feature96, Feature42, Feature14, Feature25, Feature71, Feature34 #> \t Feature70, Feature82, Feature23, Feature35, Feature13, Feature19, Feature80, Feature4, Feature29, Feature67 #> PC_ 4 #> Positive: Feature96, Feature11, Feature78, Feature59, Feature31, Feature57, Feature19, Feature80, Feature41, Feature4 #> \t Feature55, Feature44, Feature60, Feature90, Feature32, Feature50, Feature1, Feature14, Feature9, Feature77 #> \t Feature85, Feature28, Feature82, Feature99, Feature25, Feature8, Feature27, Feature62, Feature64, Feature29 #> Negative: Feature20, Feature18, Feature81, Feature100, Feature93, Feature10, Feature15, Feature74, Feature83, Feature33 #> \t Feature58, Feature6, Feature72, Feature89, Feature7, Feature35, Feature53, Feature37, Feature42, Feature43 #> \t Feature76, Feature46, Feature98, Feature73, Feature52, Feature88, Feature63, Feature49, Feature17, Feature22 #> PC_ 5 #> Positive: Feature77, Feature40, Feature9, Feature21, Feature38, Feature65, Feature80, Feature22, Feature72, Feature18 #> \t Feature50, Feature71, Feature1, Feature83, Feature82, Feature53, Feature67, Feature63, Feature90, Feature13 #> \t Feature4, Feature61, Feature56, Feature84, Feature43, Feature54, Feature19, Feature94, Feature37, Feature33 #> Negative: Feature30, Feature51, Feature86, Feature69, Feature45, Feature14, Feature3, Feature26, Feature57, Feature23 #> \t Feature99, Feature60, Feature66, Feature36, Feature15, Feature68, Feature85, Feature29, Feature78, Feature24 #> \t Feature16, Feature70, Feature42, Feature41, Feature48, Feature79, Feature10, Feature2, Feature49, Feature97 obj <- FindNeighbors(obj) #> Computing nearest neighbor graph #> Computing SNN obj <- RunUMAP(obj, dims = 1:10) #> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric #> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' #> This message will be shown once per session #> 10:09:28 UMAP embedding parameters a = 0.9922 b = 1.112 #> 10:09:28 Read 200 rows and found 10 numeric columns #> 10:09:28 Using Annoy for neighbor search, n_neighbors = 30 #> 10:09:28 Building Annoy index with metric = cosine, n_trees = 50 #> 0% 10 20 30 40 50 60 70 80 90 100% #> [----|----|----|----|----|----|----|----|----|----| #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> | #> 10:09:28 Writing NN index file to temp file /tmp/RtmpePOFm4/file17c22df9ca6b #> 10:09:28 Searching Annoy index using 1 thread, search_k = 3000 #> 10:09:28 Annoy recall = 100% #> 10:09:28 Commencing smooth kNN distance calibration using 1 thread #> with target n_neighbors = 30 #> 10:09:29 Initializing from normalized Laplacian + noise (using RSpectra) #> 10:09:29 Commencing optimization for 500 epochs, with 6160 positive edges #> 10:09:29 Optimization finished from_Seurat(obj) #> AnnData object with n_obs × n_vars = 200 × 100 #> obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA' #> var: 'vf_vst_counts_mean', 'vf_vst_counts_variance', 'vf_vst_counts_variance.expected', 'vf_vst_counts_variance.standardized', 'vf_vst_counts_variable', 'vf_vst_counts_rank', 'var.features', 'var.features.rank' #> obsm: 'X_pca', 'X_umap' #> varm: 'PCs' #> layers: 'counts', 'data', 'scale.data' #> obsp: 'connectivities', 'snn'"},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"from_SingleCellExperiment() converts SingleCellExperiment AnnData object.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"","code":"from_SingleCellExperiment( sce, output_class = c(\"InMemory\", \"HDF5AnnData\"), ... )"},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"sce object inheriting SingleCellExperiment. output_class Name AnnData class. Must one \"HDF5AnnData\" \"InMemoryAnnData\". ... Additional arguments passed generator function. See \"Details\" section information parameters","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"from_SingleCellExperiment() returns AnnData object (e.g., InMemoryAnnData) representing content sce.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"","code":"## construct an AnnData object from a SingleCellExperiment library(SingleCellExperiment) #> Loading required package: SummarizedExperiment #> Loading required package: MatrixGenerics #> Loading required package: matrixStats #> #> Attaching package: ‘MatrixGenerics’ #> The following objects are masked from ‘package:matrixStats’: #> #> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, #> colCounts, colCummaxs, colCummins, colCumprods, colCumsums, #> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, #> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, #> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, #> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, #> colWeightedMeans, colWeightedMedians, colWeightedSds, #> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, #> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, #> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, #> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, #> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, #> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, #> rowWeightedMads, rowWeightedMeans, rowWeightedMedians, #> rowWeightedSds, rowWeightedVars #> Loading required package: GenomicRanges #> Loading required package: stats4 #> Loading required package: BiocGenerics #> #> Attaching package: ‘BiocGenerics’ #> The following object is masked from ‘package:SeuratObject’: #> #> intersect #> The following objects are masked from ‘package:stats’: #> #> IQR, mad, sd, var, xtabs #> The following objects are masked from ‘package:base’: #> #> Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, #> as.data.frame, basename, cbind, colnames, dirname, do.call, #> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, #> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, #> pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table, #> tapply, union, unique, unsplit, which.max, which.min #> Loading required package: S4Vectors #> #> Attaching package: ‘S4Vectors’ #> The following object is masked from ‘package:utils’: #> #> findMatches #> The following objects are masked from ‘package:base’: #> #> I, expand.grid, unname #> Loading required package: IRanges #> #> Attaching package: ‘IRanges’ #> The following object is masked from ‘package:sp’: #> #> %over% #> Loading required package: GenomeInfoDb #> Loading required package: Biobase #> Welcome to Bioconductor #> #> Vignettes contain introductory material; view with #> 'browseVignettes()'. To cite Bioconductor, see #> 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'. #> #> Attaching package: ‘Biobase’ #> The following object is masked from ‘package:MatrixGenerics’: #> #> rowMedians #> The following objects are masked from ‘package:matrixStats’: #> #> anyMissing, rowMedians #> Warning: replacing previous import ‘S4Arrays::makeNindexFromArrayViewport’ by ‘DelayedArray::makeNindexFromArrayViewport’ when loading ‘SummarizedExperiment’ #> #> Attaching package: ‘SummarizedExperiment’ #> The following object is masked from ‘package:Seurat’: #> #> Assays #> The following object is masked from ‘package:SeuratObject’: #> #> Assays sce <- SingleCellExperiment( assays = list(counts = matrix(1:5, 5L, 3L)), colData = DataFrame(cell = 1:3), rowData = DataFrame(gene = 1:5) ) from_SingleCellExperiment(sce, \"InMemory\") #> AnnData object with n_obs × n_vars = 3 × 5 #> obs: 'cell' #> var: 'gene'"},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a dataset — generate_dataset","title":"Generate a dataset — generate_dataset","text":"Generate dataset different types columns layers","code":""},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a dataset — generate_dataset","text":"","code":"generate_dataset( n_obs = 10L, n_vars = 20L, x_type = \"numeric_matrix\", layer_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\"), obs_types = c(\"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), var_types = c(\"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), obsm_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\", \"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), varm_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\", \"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), obsp_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\"), varp_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\"), uns_types = c(\"scalar_character\", \"scalar_integer\", \"scalar_factor\", \"scalar_factor_ordered\", \"scalar_logical\", \"scalar_numeric\", \"scalar_character_with_nas\", \"scalar_integer_with_nas\", \"scalar_factor_with_nas\", \"scalar_factor_ordered_with_nas\", \"scalar_logical_with_nas\", \"scalar_numeric_with_nas\", \"vec_character\", \"vec_integer\", \"vec_factor\", \"vec_factor_ordered\", \"vec_logical\", \"vec_numeric\", \"vec_character_with_nas\", \"vec_integer_with_nas\", \"vec_factor_with_nas\", \"vec_factor_ordered_with_nas\", \"vec_logical_with_nas\", \"vec_numeric_with_nas\", \"df_character\", \"df_integer\", \"df_factor\", \"df_factor_ordered\", \"df_logical\", \"df_numeric\", \"df_character_with_nas\", \"df_integer_with_nas\", \"df_factor_with_nas\", \"df_factor_ordered_with_nas\", \"df_logical_with_nas\", \"df_numeric_with_nas\", \"mat_numeric_matrix\", \"mat_numeric_dense\", \"mat_numeric_csparse\", \"mat_numeric_rsparse\", \"mat_numeric_matrix_with_nas\", \"mat_numeric_dense_with_nas\", \"mat_numeric_csparse_with_nas\", \"mat_numeric_rsparse_with_nas\", \"mat_integer_matrix\", \"mat_integer_dense\", \"mat_integer_csparse\", \"mat_integer_rsparse\", \"mat_integer_matrix_with_nas\", \"mat_integer_dense_with_nas\", \"mat_integer_csparse_with_nas\", \"mat_integer_rsparse_with_nas\", \"list\"), example = FALSE, format = c(\"list\", \"AnnData\", \"SingleCellExperiment\", \"Seurat\") )"},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a dataset — generate_dataset","text":"n_obs Number observations generate n_vars Number variables generate x_type Type matrix generate X layer_types Types matrices generate layers obs_types Types vectors generate obs var_types Types vectors generate var obsm_types Types matrices generate obsm varm_types Types matrices generate varm obsp_types Types matrices generate obsp varp_types Types matrices generate varp uns_types Types objects generate uns example TRUE, types overridden small set types. useful documentations. format Object type output, one \"list\", \"AnnData\", \"SingleCellExperiment\", \"Seurat\".","code":""},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a dataset — generate_dataset","text":"Object containing generated dataset defined output","code":""},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a dataset — generate_dataset","text":"","code":"dummy <- generate_dataset() if (FALSE) { # \\dontrun{ dummy <- generate_dataset(format = \"AnnData\") dummy <- generate_dataset(format = \"SingleCellExperiment\") dummy <- generate_dataset(format = \"Seurat\") } # }"},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":null,"dir":"Reference","previous_headings":"","what":"Read H5AD — read_h5ad","title":"Read H5AD — read_h5ad","text":"Read data H5AD file","code":""},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read H5AD — read_h5ad","text":"","code":"read_h5ad( path, to = c(\"InMemoryAnnData\", \"HDF5AnnData\", \"SingleCellExperiment\", \"Seurat\"), mode = c(\"r\", \"r+\", \"a\", \"w\", \"w-\", \"x\"), ... )"},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read H5AD — read_h5ad","text":"path Path H5AD file read type object return. Must one : \"InMemoryAnnData\", \"HDF5AnnData\", \"SingleCellExperiment\", \"Seurat\" mode mode open HDF5 file. creates new file opens existing one read/write. r opens existing file reading. r+ opens existing file read/write. w creates file, truncating existing ones. w-/x synonyms, creating file failing already exists. ... Extra arguments provided adata$to_SingleCellExperiment() adata$to_Seurat(). See AnnData() information arguments functions. Note: update documentation r-lib/roxygen2#955 resolved.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read H5AD — read_h5ad","text":"object specified ","code":""},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read H5AD — read_h5ad","text":"","code":"h5ad_file <- system.file(\"extdata\", \"example.h5ad\", package = \"anndataR\") # Read the H5AD as a SingleCellExperiment object if (requireNamespace(\"SingleCellExperiment\", quietly = TRUE)) { sce <- read_h5ad(h5ad_file, to = \"SingleCellExperiment\") } # Read the H5AD as a Seurat object if (requireNamespace(\"SeuratObject\", quietly = TRUE)) { seurat <- read_h5ad(h5ad_file, to = \"Seurat\") } #> Warning: Data is of class dgRMatrix. Coercing to dgCMatrix. #> Warning: No columnames present in cell embeddings, setting to 'PC_1:38' #> Warning: No columnames present in cell embeddings, setting to 'umap_1:2'"},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Seurat object to an AnnData object — to_Seurat","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"to_Seurat() converts AnnData object Seurat object. one assay can converted time. Arguments used configure conversion. NULL, functions to_Seurat_guess_* used guess mapping.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"","code":"to_Seurat( adata, assay_name = \"RNA\", layers_mapping = NULL, reduction_mapping = NULL, graph_mapping = NULL, misc_mapping = NULL )"},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"adata AnnData object converted assay_name Name assay created (default: \"RNA\"). layers_mapping named list map AnnData layers Seurat layers. See section \"Layer mapping\" details. reduction_mapping named list map AnnData reductions Seurat reductions. item list must named list keys 'key', 'obsm', 'varm'. See section \"Reduction mapping\" details. graph_mapping named list map AnnData graphs Seurat graphs. item list must character vector length 1. See section \"Graph mapping\" details. misc_mapping named list map miscellaneous data names data Seurat object. See section \"Miscellaneous mapping\" details.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"Seurat object","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"layer-mapping","dir":"Reference","previous_headings":"","what":"Layer mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list map AnnData layers Seurat layers. item list must character vector length 1, values correspond names layers AnnData object, names correspond names layers resulting Seurat object. value NULL corresponds AnnData X slot. Example: layers_mapping = list(counts = \"counts\", data = NULL, foo = \"bar\"). NULL, internal function to_Seurat_guess_layers used guess layer mapping follows: AnnData layers copied Seurat layers name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"reduction-mapping","dir":"Reference","previous_headings":"","what":"Reduction mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list map AnnData $obsm $varm Seurat reductions. item list must named list keys 'key', 'obsm', 'varm'. Example: reduction_mapping = list(pca = list(key = \"PC_\", obsm = \"X_pca\", varm = \"PCs\")). NULL, internal function to_Seurat_guess_reductions used guess reduction mapping follows: $obsm items starting X_ copied name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"graph-mapping","dir":"Reference","previous_headings":"","what":"Graph mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list mapping graph names names graphs AnnData object. item list must character vector length 1. values correspond names graphs resulting Seurat object, names correspond names graphs AnnData object. Example: graph_mapping = list(nn = \"connectivities\"). NULL, internal function to_Seurat_guess_graphs used guess graph mapping follows: obsp named connectivities mapped nn. graphs starting connectivities_ stripped prefix copied name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"miscellaneous-mapping","dir":"Reference","previous_headings":"","what":"Miscellaneous mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list mapping miscellaneous data names data AnnData object. item list must vector one two elements. first element must one : 'X', 'layers', 'obs', 'obsm', 'obsp', 'var', 'varm', 'varp', 'uns'. second element name data corresponding slot. second element present, whole slot specified first element used. Example: misc_mapping = list(uns = \"uns\", varp_neighbors = c(\"varp\", \"neighbors\")). NULL, internal function to_Seurat_guess_misc used guess miscellaneous mapping follows: $uns defined, values $uns copied Seurat misc.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"","code":"ad <- AnnData( X = matrix(1:5, 3L, 5L), obs = data.frame(row.names = LETTERS[1:3], cell = 1:3), var = data.frame(row.names = letters[1:5], gene = 1:5) ) to_Seurat(ad) #> Warning: Data is of class matrix. Coercing to dgCMatrix. #> An object of class Seurat #> 5 features across 3 samples within 1 assay #> Active assay: RNA (5 features, 0 variable features) #> 1 layer present: counts"},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":null,"dir":"Reference","previous_headings":"","what":"Write H5AD — write_h5ad","title":"Write H5AD — write_h5ad","text":"Write H5AD file","code":""},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write H5AD — write_h5ad","text":"","code":"write_h5ad( object, path, compression = c(\"none\", \"gzip\", \"lzf\"), mode = c(\"w-\", \"r\", \"r+\", \"a\", \"w\", \"x\") )"},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write H5AD — write_h5ad","text":"object object write, either \"SingleCellExperiment\" \"Seurat\" object path Path file write compression compression algorithm use writing HDF5 file. Can one \"none\", \"gzip\" \"lzf\". Defaults \"none\". mode mode open HDF5 file. creates new file opens existing one read/write. r+ opens existing file read/write. w creates file, truncating existing ones w-/x synonyms creating file failing already exists.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write H5AD — write_h5ad","text":"path invisibly","code":""},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write H5AD — write_h5ad","text":"","code":"adata <- AnnData( X = matrix(1:5, 3L, 5L), layers = list( A = matrix(5:1, 3L, 5L), B = matrix(letters[1:5], 3L, 5L) ), obs = data.frame(row.names = LETTERS[1:3], cell = 1:3), var = data.frame(row.names = letters[1:5], gene = 1:5) ) h5ad_file <- tempfile(fileext = \".h5ad\") write_h5ad(adata, h5ad_file) # Write a SingleCellExperiment as an H5AD if (requireNamespace(\"SingleCellExperiment\", quietly = TRUE)) { ncells <- 100 counts <- matrix(rpois(20000, 5), ncol = ncells) logcounts <- log2(counts + 1) pca <- matrix(runif(ncells * 5), ncells) tsne <- matrix(rnorm(ncells * 2), ncells) sce <- SingleCellExperiment::SingleCellExperiment( assays = list(counts = counts, logcounts = logcounts), reducedDims = list(PCA = pca, tSNE = tsne) ) h5ad_file <- tempfile(fileext = \".h5ad\") write_h5ad(sce, h5ad_file) } # Write a Seurat as a H5AD if (requireNamespace(\"SeuratObject\", quietly = TRUE)) { # TODO: uncomment this code when the seurat converter is fixed # counts <- matrix(1:15, 3L, 5L) # dimnames(counts) <- list( # letters[1:3], # LETTERS[1:5] # ) # gene.metadata <- data.frame( # row.names = LETTERS[1:5], # gene = 1:5 # ) # obj <- SeuratObject::CreateSeuratObject(counts, meta.data = gene.metadata) # cell.metadata <- data.frame( # row.names = letters[1:3], # cell = 1:3 # ) # obj <- SeuratObject::AddMetaData(obj, cell.metadata) # # h5ad_file <- tempfile(fileext = \".h5ad\") # write_h5ad(obj, h5ad_file) } #> NULL"},{"path":[]},{"path":"https://anndataR.data-intuitive.com/news/index.html","id":"new-features-0-99-0","dir":"Changelog","previous_headings":"","what":"New features","title":"anndataR 0.99.0","text":"Update DESCRIPTION file reflect current release R version used Bio: R (>= 3.2.0) –> R (>= 4.3.0). Reformat NEWS file follow conventions. X layers obs obs_names var var_names HDF5AnnData InMemoryAnnData SingleCellExperiment Seurat","code":""}] +[{"path":"https://anndataR.data-intuitive.com/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 Robrecht Cannoodt, Luke Zappia, Martin Morgan, Louise Deconinck Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Using anndataR","text":"Install using:","code":"if (!require(\"pak\", quietly = TRUE)) { install.packages(\"pak\") } pak::pak(\"scverse/anndataR\")"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"usage","dir":"Articles","previous_headings":"","what":"Usage","title":"Using anndataR","text":"’s quick example use {anndataR}. First, download h5ad file. Read h5ad file memory: Read h5ad file disk: View structure: Access AnnData slots:","code":"library(anndataR) h5ad_path <- system.file(\"extdata\", \"example.h5ad\", package = \"anndataR\") adata <- read_h5ad(h5ad_path) adata <- read_h5ad(h5ad_path, to = \"HDF5AnnData\") adata #> AnnData object with n_obs × n_vars = 50 × 100 #> obs: 'Float', 'FloatNA', 'Int', 'IntNA', 'Bool', 'BoolNA', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'leiden' #> var: 'String', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm' #> obsm: 'X_pca', 'X_umap' #> varm: 'PCs' #> layers: 'counts', 'csc_counts', 'dense_X', 'dense_counts' #> obsp: 'connectivities', 'distances' dim(adata$X) #> [1] 50 100 adata$obs[1:5, 1:6] #> Float FloatNA Int IntNA Bool BoolNA #> Cell000 42.42 NaN 0 NA FALSE FALSE #> Cell001 42.42 42.42 1 42 TRUE NA #> Cell002 42.42 42.42 2 42 TRUE TRUE #> Cell003 42.42 42.42 3 42 TRUE TRUE #> Cell004 42.42 42.42 4 42 TRUE TRUE adata$var[1:5, 1:6] #> String n_cells_by_counts mean_counts log1p_mean_counts pct_dropout_by_counts total_counts #> Gene000 String0 44 1.94 1.078410 12 97 #> Gene001 String1 42 2.04 1.111858 16 102 #> Gene002 String2 43 2.12 1.137833 14 106 #> Gene003 String3 41 1.72 1.000632 18 86 #> Gene004 String4 42 2.06 1.118415 16 103"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"interoperability","dir":"Articles","previous_headings":"Usage","what":"Interoperability","title":"Using anndataR","text":"Convert AnnData object SingleCellExperiment object: Convert AnnData object Seurat object:","code":"sce <- adata$to_SingleCellExperiment() sce #> class: SingleCellExperiment #> dim: 100 50 #> metadata(0): #> assays(5): X counts csc_counts dense_X dense_counts #> rownames(100): Gene000 Gene001 ... Gene098 Gene099 #> rowData names(11): String n_cells_by_counts ... dispersions dispersions_norm #> colnames(50): Cell000 Cell001 ... Cell048 Cell049 #> colData names(11): Float FloatNA ... log1p_total_counts leiden #> reducedDimNames(0): #> mainExpName: NULL #> altExpNames(0): obj <- adata$to_Seurat() obj #> An object of class Seurat #> 500 features across 50 samples within 5 assays #> Active assay: RNA (100 features, 0 variable features) #> 2 layers present: counts, data #> 4 other assays present: counts, csc_counts, dense_X, dense_counts"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"manually-create-an-object","dir":"Articles","previous_headings":"Usage","what":"Manually create an object","title":"Using anndataR","text":"","code":"adata <- AnnData( X = matrix(rnorm(100), nrow = 10), obs = data.frame( cell_type = factor(rep(c(\"A\", \"B\"), each = 5)) ), var = data.frame( gene_name = paste0(\"gene_\", 1:10) ) ) adata #> AnnData object with n_obs × n_vars = 10 × 10 #> obs: 'cell_type' #> var: 'gene_name'"},{"path":"https://anndataR.data-intuitive.com/articles/anndataR.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Using anndataR","text":"","code":"sessionInfo() #> R version 4.4.2 (2024-10-31) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.5 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] SingleCellExperiment_1.28.1 SummarizedExperiment_1.36.0 #> [3] Biobase_2.66.0 GenomicRanges_1.58.0 #> [5] GenomeInfoDb_1.42.1 IRanges_2.40.1 #> [7] S4Vectors_0.44.0 BiocGenerics_0.52.0 #> [9] MatrixGenerics_1.18.0 matrixStats_1.4.1 #> [11] SeuratObject_5.0.2 sp_2.1-4 #> [13] BiocStyle_2.34.0 #> #> loaded via a namespace (and not attached): #> [1] sass_0.4.9 future_1.34.0 generics_0.1.3 #> [4] SparseArray_1.6.0 lattice_0.22-6 listenv_0.9.1 #> [7] digest_0.6.37 evaluate_1.0.1 grid_4.4.2 #> [10] bookdown_0.41 fastmap_1.2.0 jsonlite_1.8.9 #> [13] Matrix_1.7-1 BiocManager_1.30.25 httr_1.4.7 #> [16] spam_2.11-0 UCSC.utils_1.2.0 codetools_0.2-20 #> [19] textshaping_0.4.1 jquerylib_0.1.4 abind_1.4-8 #> [22] cli_3.6.3 crayon_1.5.3 rlang_1.1.4 #> [25] XVector_0.46.0 parallelly_1.41.0 future.apply_1.11.3 #> [28] DelayedArray_0.32.0 cachem_1.1.0 yaml_2.3.10 #> [31] S4Arrays_1.6.0 tools_4.4.2 parallel_4.4.2 #> [34] GenomeInfoDbData_1.2.13 globals_0.16.3 R6_2.5.1 #> [37] lifecycle_1.0.4 zlibbioc_1.52.0 fs_1.6.5 #> [40] htmlwidgets_1.6.4 ragg_1.3.3 desc_1.4.3 #> [43] progressr_0.15.1 pkgdown_2.1.1 bslib_0.8.0 #> [46] Rcpp_1.0.13-1 systemfonts_1.1.0 xfun_0.49 #> [49] knitr_1.49 htmltools_0.5.8.1 rmarkdown_2.29 #> [52] dotCall64_1.2 compiler_4.4.2"},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"desired-feature-list","dir":"Articles","previous_headings":"","what":"Desired feature list","title":"Design","text":"Provide R6 class work AnnData objects R (either -memory -disk). Read/write *.h5ad files natively Convert /SingleCellExperiment objects Convert /Seurat objects","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"class-diagram","dir":"Articles","previous_headings":"","what":"Class diagram","title":"Design","text":"diagram main R6 classes provided package: Notation: X: Matrix - variable X type Matrix *X: Matrix - variable X abstract to_SingleCellExperiment(): SingleCellExperiment - function to_SingleCellExperiment returns object type SingleCellExperiment *to_SingleCellExperiment() - function to_SingleCellExperiment abstract","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"feature-tracking","dir":"Articles","previous_headings":"","what":"Feature tracking","title":"Design","text":"following tables show status implementation feature package:","code":""},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":[]},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"issue-integers-are-being-converted-to-floats-","dir":"Articles","previous_headings":"Known issues","what":"Issue: Integers are being converted to floats.","title":"Design","text":"Affected backend: HDF5AnnData Affected slot(s): X, layers, obsp, varp, obsm, varm Affected dtype(s): integer_csparse, integer_rsparse, integer_matrix Probable cause: read investigate: TRUE fix: TRUE","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"error-message","dir":"Articles","previous_headings":"Known issues > Issue: Integers are being converted to floats.","what":"Error message","title":"Design","text":"","code":"Failure (test-roundtrip-obspvarp.R:111:5): Writing an AnnData with obsp and varp 'integer_csparse' works a$dtype (`actual`) not equal to b$dtype (`expected`). `class(actual)`: \"numpy.dtypes.Float64DType\" \"numpy.dtype\" \"python.builtin.object\" `class(expected)`: \"numpy.dtypes.Int64DType\" \"numpy.dtype\" \"python.builtin.object\""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"proposed-solution","dir":"Articles","previous_headings":"Known issues > Issue: Integers are being converted to floats.","what":"Proposed solution","title":"Design","text":"Debug fix","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"issue-python-nd-arrays-have-a-dimension-while-r-vectors-do-not-","dir":"Articles","previous_headings":"Known issues","what":"Issue: Python nd.arrays have a dimension while R vectors do not.","title":"Design","text":"Affected backend: HDF5AnnData Affected slot(s): obsm, varm Affected dtype(s): boolean_array, categorical, categorical_missing_values, categorical_ordered, categorical_ordered_missing_values, dense_array, integer_array, nullable_boolean_array, nullable_integer_array, string_array Probable cause: reticulate investigate: TRUE fix: TRUE","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"error-message-1","dir":"Articles","previous_headings":"Known issues > Issue: Python nd.arrays have a dimension while R vectors do not.","what":"Error message","title":"Design","text":"","code":"adata_r$varm[[name]] (`actual`) not equal to py_to_r(py_get_item(adata_py$varm, name)) (`expected`). `dim(actual)` is absent `dim(expected)` is an integer vector (20)"},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"proposed-solution-1","dir":"Articles","previous_headings":"Known issues > Issue: Python nd.arrays have a dimension while R vectors do not.","what":"Proposed solution","title":"Design","text":"Debug fix","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"issue-r-vectors-dont-have-a-dimension-","dir":"Articles","previous_headings":"Known issues","what":"Issue: R vectors don’t have a dimension.","title":"Design","text":"Affected backend: HDF5AnnData Affected slot(s): obsm, varm Affected dtype(s): boolean_array, categorical, categorical_missing_values, categorical_ordered, categorical_ordered_missing_values, dense_array, integer_array, nullable_boolean_array, nullable_integer_array, string_array Probable cause: write investigate: TRUE fix: TRUE","code":""},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"error-message-2","dir":"Articles","previous_headings":"Known issues > Issue: R vectors don’t have a dimension.","what":"Error message","title":"Design","text":"","code":"Error in `if (found_dim != expected_dim) { stop(\"dim(\", label, \")[\", i, \"] should have shape: \", expected_dim, \", found: \", found_dim, \".\") }`: argument is of length zero"},{"path":"https://anndataR.data-intuitive.com/articles/design.html","id":"proposed-solution-2","dir":"Articles","previous_headings":"Known issues > Issue: R vectors don’t have a dimension.","what":"Proposed solution","title":"Design","text":"input checking function obsm varm allow object vector correct length instead matrix data frame.","code":""},{"path":"https://anndataR.data-intuitive.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robrecht Cannoodt. Author, maintainer. rcannood Luke Zappia. Author. lazappi Martin Morgan. Author. mtmorgan Louise Deconinck. Author. LouiseDck Danila Bredikhin. Contributor. gtca Isaac Virshup. Contributor. ivirshup Brian Schilder. Contributor. bschilder Chananchida Sang-aram. Contributor. csangara Data Intuitive. Funder, copyright holder. Chan Zuckerberg Initiative. Funder.","code":""},{"path":"https://anndataR.data-intuitive.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cannoodt R, Zappia L, Morgan M, Deconinck L (2024). anndataR: AnnData interoperability R. R package version 0.99.0, https://github.com/scverse/anndataR, https://scverse.org/anndataR.","code":"@Manual{, title = {anndataR: AnnData interoperability in R}, author = {Robrecht Cannoodt and Luke Zappia and Martin Morgan and Louise Deconinck}, year = {2024}, note = {R package version 0.99.0, https://github.com/scverse/anndataR}, url = {https://scverse.org/anndataR}, }"},{"path":"https://anndataR.data-intuitive.com/index.html","id":"anndatar-an-r-package-for-working-with-anndata-objects","dir":"","previous_headings":"","what":"AnnData interoperability in R","title":"AnnData interoperability in R","text":"{anndataR} aims make AnnData format first-class citizen R ecosystem, make easy work AnnData files R, either directly converting SingleCellExperiment Seurat object. {anndataR} scverse community project maintained Data Intuitive, fiscally sponsored Chan Zuckerberg Initiative.","code":""},{"path":"https://anndataR.data-intuitive.com/index.html","id":"features-of-anndatar","dir":"","previous_headings":"","what":"Features of {anndataR}","title":"AnnData interoperability in R","text":"Provide R6 class work AnnData objects R (either -memory -disk). Read/write *.h5ad files natively Convert /SingleCellExperiment objects Convert /Seurat objects [!WARNING] package still experimental stage, may work expected. can find status development anndataR feature tracking page website. Please report issues encounter.","code":""},{"path":"https://anndataR.data-intuitive.com/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"AnnData interoperability in R","text":"can install development version {anndataR} like : need install additional dependencies, depending task want perform. read/write *.h5ad files, install hdf5r:install.packages(\"hdf5r\") convert /SingleCellExperiment objects, install SingleCellExperiment:BiocManager::install(\"SingleCellExperiment\") convert /Seurat objects, install SeuratObject:install.packages(\"SeuratObject\") Alternatively, can install suggested dependencies :","code":"# install.packages(\"pak\") pak::pak(\"scverse/anndataR\") pak::pak(\"scverse/anndataR\", dependencies = TRUE)"},{"path":"https://anndataR.data-intuitive.com/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"AnnData interoperability in R","text":"best way get started {anndataR} explore package vignettes (available https://anndatar.data-intuitive.com/articles/). Getting started: introduction package features.vignette(\"anndataR\", package = \"anndataR\") Design: overview design package.vignette(\"design\", package = \"anndataR\")","code":""},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an in-memory AnnData object. — AnnData","title":"Create an in-memory AnnData object. — AnnData","text":"information functionality AnnData object, see anndataR-package.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an in-memory AnnData object. — AnnData","text":"","code":"AnnData( X = NULL, obs = NULL, var = NULL, layers = NULL, obsm = NULL, varm = NULL, obsp = NULL, varp = NULL, uns = NULL, shape = shape )"},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an in-memory AnnData object. — AnnData","text":"X Either NULL observation × variable matrix dimensions consistent obs var. obs Either NULL data.frame columns containing information observations. NULL, n_obs×0 data frame automatically generated. var Either NULL data.frame columns containing information variables. NULL, n_vars×0 data frame automatically generated. layers Either NULL named list, element observation × variable matrix dimensions consistent obs var. obsm obsm slot used store multi-dimensional annotation arrays. must either NULL named list, element matrix n_obs rows arbitrary number columns. varm varm slot used store multi-dimensional annotation arrays. must either NULL named list, element matrix n_vars rows arbitrary number columns. obsp obsp slot used store sparse multi-dimensional annotation arrays. must either NULL named list, element sparse matrix dimension length n_obs. varp varp slot used store sparse multi-dimensional annotation arrays. must either NULL named list, element sparse matrix dimension length n_vars. uns uns slot used store unstructured annotation. must either NULL named list. shape Shape tuple (#observations, #variables). Can provided X obs var provided.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an in-memory AnnData object. — AnnData","text":"-memory AnnData object.","code":""},{"path":[]},{"path":"https://anndataR.data-intuitive.com/reference/AnnData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an in-memory AnnData object. — AnnData","text":"","code":"adata <- AnnData( X = matrix(1:12, nrow = 3, ncol = 4), obs = data.frame( row.names = paste0(\"obs\", 1:3), n_counts = c(1, 2, 3), n_cells = c(1, 2, 3) ), var = data.frame( row.names = paste0(\"var\", 1:4), n_cells = c(1, 2, 3, 4) ) ) adata #> AnnData object with n_obs × n_vars = 3 × 4 #> obs: 'n_counts', 'n_cells' #> var: 'n_cells'"},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":null,"dir":"Reference","previous_headings":"","what":"anndataR: AnnData interoperability in R — anndataR-package","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"AnnData object. class can either -memory AnnData (InMemoryAnnData) HDF5-backed AnnData (HDF5AnnData). AnnData object stores data matrix X together annotations observations obs (obsm, obsp) variables var (varm, varp). Additional layers data can stored layers unstructured annotations uns.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"functions-that-can-be-used-to-create-anndata-objects","dir":"Reference","previous_headings":"","what":"Functions that can be used to create AnnData objects","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"AnnData(): Create -memory AnnData object. read_h5ad(): Read HDF5-backed AnnData file disk. from_SingleCellExperiment(): Convert SingleCellExperiment object AnnData object. from_Seurat(): Convert Seurat object AnnData object.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"an-anndata-object-has-the-following-slots","dir":"Reference","previous_headings":"","what":"An AnnData object has the following slots","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Access using $ operator. Example: adata$obs. X: matrix observations variables. obs: data frame observations. var: data frame variables. obs_names: Names observations (alias rownames(obs)). var_names: Names variables (alias rownames(var)). layers: named list matrices dimensions X. obsm: named list matrices number rows obs. varm: named list matrices number rows var. obsp: named list sparse matrices number rows columns number observations. varp: named list sparse matrices number rows columns number variables. uns: named list unstructured annotations.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"an-anndata-object-has-the-following-methods","dir":"Reference","previous_headings":"","what":"An AnnData object has the following methods","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Access using $ operator. Example: adata$write_h5ad(). print(): Print summary AnnData object. shape(): Dimensions (observations x variables) AnnData object. n_obs(): Number observations AnnData object. n_vars(): Number variables AnnData object. obs_keys(): Column names obs. var_keys(): Column names var. layers_keys(): Element names layers. obsm_keys(): Element names obsm. varm_keys(): Element names varm. obsp_keys(): Element names obsp. varp_keys(): Element names varp. uns_keys(): Element names uns. write_h5ad(): Write AnnData object HDF5 file.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"conversion-methods","dir":"Reference","previous_headings":"","what":"Conversion methods","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Access using $ operator. Example: adata$to_Seurat(). to_SingleCellExperiment(): Convert SingleCellExperiment. to_Seurat(): Convert Seurat. to_InMemoryAnnData(): Convert InMemory AnnData. to_HDF5AnnData(): Convert HDF5 Backed AnnData.","code":""},{"path":[]},{"path":"https://anndataR.data-intuitive.com/reference/anndataR-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"anndataR: AnnData interoperability in R — anndataR-package","text":"Maintainer: Robrecht Cannoodt robrecht@data-intuitive.com (ORCID) (rcannood) Authors: Luke Zappia luke@lazappi.id.au (ORCID) (lazappi) Martin Morgan mtmorgan.bioc@gmail.com (ORCID) (mtmorgan) Louise Deconinck louise.deconinck@gmail.com (ORCID) (LouiseDck) contributors: Danila Bredikhin danila.bredikhin@embl.de (ORCID) (gtca) [contributor] Isaac Virshup (ORCID) (ivirshup) [contributor] Brian Schilder brian_schilder@alumni.brown.edu (ORCID) (bschilder) [contributor] Chananchida Sang-aram (ORCID) (csangara) [contributor] Data Intuitive info@data-intuitive.com [funder, copyright holder] Chan Zuckerberg Initiative [funder]","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Seurat object to an AnnData object — from_Seurat","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"from_Seurat() converts Seurat object AnnData object. one assay can converted time. Arguments used configure conversion. NULL, functions from_Seurat_guess_* used guess mapping.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"","code":"from_Seurat( seurat_obj, output_class = c(\"InMemoryAnnData\", \"HDF5AnnData\"), assay_name = NULL, x_mapping = NULL, layers_mapping = NULL, obsm_mapping = NULL, varm_mapping = NULL, obsp_mapping = NULL, varp_mapping = NULL, uns_mapping = NULL, ... )"},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"seurat_obj Seurat object converted. output_class Name AnnData class. Must one \"HDF5AnnData\" \"InMemoryAnnData\". assay_name name assay converted. NULL, default assay used (SeuratObject::DefaultAssay()). x_mapping mapping Seurat layer AnnData X slot. NULL, data copied X slot. layers_mapping named list mapping layer names names layers Seurat object. item list must character vector length 1. See section \"$layers mapping\" details. obsm_mapping named list mapping reductions names reductions Seurat object. item list must vector length 2. See section \"$obsm mapping\" details. varm_mapping named list mapping PCA loadings names PCA loadings Seurat object. item list must character vector length 1. See section \"$varm mapping\" details. obsp_mapping named list mapping graph names names graphs Seurat object. item list must character vector length 1. See section \"$obsp mapping\" details. varp_mapping named list mapping miscellaneous data names data Seurat object. item list must named list one two elements. See section \"$varp mapping\" details. uns_mapping named list mapping miscellaneous data names data Seurat object. item list must named list one two elements. See section \"$uns mapping\" details. ... Additional arguments passed generator function.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"AnnData object","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"information functionality AnnData object, see anndataR-package.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-x-mapping","dir":"Reference","previous_headings":"","what":"$X mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"mapping Seurat layer AnnData X slot. value must NULL character vector Seurat layer name copy. NULL, data copied X slot.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-layers-mapping","dir":"Reference","previous_headings":"","what":"$layers mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map AnnData layers Seurat layers. item list must character vector length 1. $X key maps X slot. Example: layers_mapping = list(counts = \"counts\", foo = \"bar\"). NULL, internal function from_Seurat_guess_layers used guess layer mapping follows: Seurat layers copied AnnData layers name. means AnnData X slot NULL (empty). want copy data X slot, must define layer mapping explicitly.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-obsm-mapping","dir":"Reference","previous_headings":"","what":"$obsm mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat reductions AnnData $obsm. item list must vector length 2, name corresponds name resulting $obsm slot, values correspond location data Seurat object. Example: obsm_mapping = list(pca = c(\"reductions\", \"pca\"), umap = c(\"reductions\", \"umap\")). NULL, internal function from_Seurat_guess_obsms used guess obsm mapping follows: Seurat reductions prefixed X_ copied AnnData $obsm.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-varm-mapping","dir":"Reference","previous_headings":"","what":"$varm mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat reduction loadings AnnData $varm. item list must character vector length 2, name corresponds name resulting $varm slot, value corresponds location data Seurat object. Example: varm_mapping = list(PCs = c(\"reductions\", \"pca\"). NULL, internal function from_Seurat_guess_varms used guess varm mapping follows: name PCA loadings copied name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-obsp-mapping","dir":"Reference","previous_headings":"","what":"$obsp mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat graphs AnnData $obsp. Example: obsp_mapping = list(nn = \"connectivities\"). NULL, internal function from_Seurat_guess_obsps used guess obsp mapping follows: Seurat graphs copied $obsp name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-varp-mapping","dir":"Reference","previous_headings":"","what":"$varp mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat miscellaneous data AnnData $varp. name item corresponds resulting $varp slot, value item must fector corresponds location data Seurat object. Example: varp_mapping = list(foo = c(\"misc\", \"foo\")). NULL, internal function from_Seurat_guess_varps used guess varp mapping follows: data mapped $varp.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"-uns-mapping","dir":"Reference","previous_headings":"","what":"$uns mapping","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"named list map Seurat miscellaneous data AnnData uns. item list must character length 2. first element must \"misc\". second element name data corresponding slot. Example: uns_mapping = list(foo = c(\"misc\", \"foo\")). NULL, internal function from_Seurat_guess_uns used guess uns mapping follows: Seurat miscellaneous data copied uns name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_Seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Seurat object to an AnnData object — from_Seurat","text":"","code":"library(Seurat) #> Loading required package: SeuratObject #> Loading required package: sp #> ‘SeuratObject’ was built under R 4.4.0 but the current version is #> 4.4.2; it is recomended that you reinstall ‘SeuratObject’ as the ABI #> for R may have changed #> #> Attaching package: ‘SeuratObject’ #> The following objects are masked from ‘package:base’: #> #> intersect, t counts <- matrix(rbinom(20000, 1000, .001), nrow = 100) obj <- CreateSeuratObject(counts = counts) #> Warning: Data is of class matrix. Coercing to dgCMatrix. obj <- NormalizeData(obj) #> Normalizing layer: counts obj <- FindVariableFeatures(obj) #> Finding variable features for layer counts obj <- ScaleData(obj) #> Centering and scaling data matrix obj <- RunPCA(obj, npcs = 10L) #> PC_ 1 #> Positive: Feature72, Feature61, Feature35, Feature78, Feature52, Feature50, Feature66, Feature8, Feature14, Feature32 #> \t Feature70, Feature1, Feature56, Feature87, Feature19, Feature97, Feature7, Feature17, Feature86, Feature48 #> \t Feature81, Feature88, Feature43, Feature67, Feature36, Feature3, Feature26, Feature54, Feature40, Feature33 #> Negative: Feature9, Feature29, Feature4, Feature39, Feature15, Feature27, Feature34, Feature68, Feature62, Feature18 #> \t Feature22, Feature46, Feature10, Feature74, Feature49, Feature37, Feature12, Feature23, Feature95, Feature100 #> \t Feature64, Feature30, Feature90, Feature55, Feature47, Feature96, Feature5, Feature31, Feature60, Feature92 #> PC_ 2 #> Positive: Feature41, Feature89, Feature45, Feature98, Feature33, Feature44, Feature68, Feature91, Feature18, Feature59 #> \t Feature61, Feature86, Feature32, Feature4, Feature51, Feature42, Feature54, Feature67, Feature56, Feature21 #> \t Feature53, Feature70, Feature8, Feature31, Feature22, Feature14, Feature12, Feature76, Feature62, Feature58 #> Negative: Feature97, Feature13, Feature48, Feature69, Feature49, Feature82, Feature7, Feature5, Feature79, Feature93 #> \t Feature94, Feature84, Feature23, Feature78, Feature27, Feature40, Feature26, Feature50, Feature81, Feature46 #> \t Feature29, Feature10, Feature71, Feature72, Feature52, Feature88, Feature87, Feature9, Feature95, Feature16 #> PC_ 3 #> Positive: Feature3, Feature93, Feature2, Feature5, Feature11, Feature79, Feature66, Feature74, Feature50, Feature36 #> \t Feature78, Feature12, Feature45, Feature98, Feature51, Feature16, Feature83, Feature28, Feature27, Feature9 #> \t Feature8, Feature95, Feature33, Feature63, Feature21, Feature87, Feature40, Feature53, Feature85, Feature57 #> Negative: Feature58, Feature90, Feature46, Feature81, Feature44, Feature56, Feature69, Feature86, Feature37, Feature97 #> \t Feature92, Feature26, Feature88, Feature30, Feature96, Feature42, Feature14, Feature25, Feature71, Feature34 #> \t Feature70, Feature82, Feature23, Feature35, Feature13, Feature19, Feature80, Feature4, Feature29, Feature67 #> PC_ 4 #> Positive: Feature96, Feature11, Feature78, Feature59, Feature31, Feature57, Feature19, Feature80, Feature41, Feature4 #> \t Feature55, Feature44, Feature60, Feature90, Feature32, Feature50, Feature1, Feature14, Feature9, Feature77 #> \t Feature85, Feature28, Feature82, Feature99, Feature25, Feature8, Feature27, Feature62, Feature64, Feature29 #> Negative: Feature20, Feature18, Feature81, Feature100, Feature93, Feature10, Feature15, Feature74, Feature83, Feature33 #> \t Feature58, Feature6, Feature72, Feature89, Feature7, Feature35, Feature53, Feature37, Feature42, Feature43 #> \t Feature76, Feature46, Feature98, Feature73, Feature52, Feature88, Feature63, Feature49, Feature17, Feature22 #> PC_ 5 #> Positive: Feature77, Feature40, Feature9, Feature21, Feature38, Feature65, Feature80, Feature22, Feature72, Feature18 #> \t Feature50, Feature71, Feature1, Feature83, Feature82, Feature53, Feature67, Feature63, Feature90, Feature13 #> \t Feature4, Feature61, Feature56, Feature84, Feature43, Feature54, Feature19, Feature94, Feature37, Feature33 #> Negative: Feature30, Feature51, Feature86, Feature69, Feature45, Feature14, Feature3, Feature26, Feature57, Feature23 #> \t Feature99, Feature60, Feature66, Feature36, Feature15, Feature68, Feature85, Feature29, Feature78, Feature24 #> \t Feature16, Feature70, Feature42, Feature41, Feature48, Feature79, Feature10, Feature2, Feature49, Feature97 obj <- FindNeighbors(obj) #> Computing nearest neighbor graph #> Computing SNN obj <- RunUMAP(obj, dims = 1:10) #> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric #> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' #> This message will be shown once per session #> 19:13:16 UMAP embedding parameters a = 0.9922 b = 1.112 #> 19:13:16 Read 200 rows and found 10 numeric columns #> 19:13:16 Using Annoy for neighbor search, n_neighbors = 30 #> 19:13:16 Building Annoy index with metric = cosine, n_trees = 50 #> 0% 10 20 30 40 50 60 70 80 90 100% #> [----|----|----|----|----|----|----|----|----|----| #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> | #> 19:13:16 Writing NN index file to temp file /tmp/RtmpajMDBu/file24665b142657 #> 19:13:16 Searching Annoy index using 1 thread, search_k = 3000 #> 19:13:17 Annoy recall = 100% #> 19:13:17 Commencing smooth kNN distance calibration using 1 thread #> with target n_neighbors = 30 #> 19:13:17 Initializing from normalized Laplacian + noise (using RSpectra) #> 19:13:17 Commencing optimization for 500 epochs, with 6160 positive edges #> 19:13:18 Optimization finished from_Seurat(obj) #> AnnData object with n_obs × n_vars = 200 × 100 #> obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA' #> var: 'vf_vst_counts_mean', 'vf_vst_counts_variance', 'vf_vst_counts_variance.expected', 'vf_vst_counts_variance.standardized', 'vf_vst_counts_variable', 'vf_vst_counts_rank', 'var.features', 'var.features.rank' #> obsm: 'X_pca', 'X_umap' #> varm: 'PCs' #> layers: 'counts', 'data', 'scale.data' #> obsp: 'connectivities', 'snn'"},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"from_SingleCellExperiment() converts SingleCellExperiment AnnData object.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"","code":"from_SingleCellExperiment( sce, output_class = c(\"InMemory\", \"HDF5AnnData\"), ... )"},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"sce object inheriting SingleCellExperiment. output_class Name AnnData class. Must one \"HDF5AnnData\" \"InMemoryAnnData\". ... Additional arguments passed generator function. See \"Details\" section information parameters","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"from_SingleCellExperiment() returns AnnData object (e.g., InMemoryAnnData) representing content sce.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/from_SingleCellExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a SingleCellExperiment object to an AnnData object — from_SingleCellExperiment","text":"","code":"## construct an AnnData object from a SingleCellExperiment library(SingleCellExperiment) #> Loading required package: SummarizedExperiment #> Loading required package: MatrixGenerics #> Loading required package: matrixStats #> #> Attaching package: ‘MatrixGenerics’ #> The following objects are masked from ‘package:matrixStats’: #> #> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, #> colCounts, colCummaxs, colCummins, colCumprods, colCumsums, #> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, #> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, #> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, #> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, #> colWeightedMeans, colWeightedMedians, colWeightedSds, #> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, #> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, #> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, #> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, #> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, #> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, #> rowWeightedMads, rowWeightedMeans, rowWeightedMedians, #> rowWeightedSds, rowWeightedVars #> Loading required package: GenomicRanges #> Loading required package: stats4 #> Loading required package: BiocGenerics #> #> Attaching package: ‘BiocGenerics’ #> The following object is masked from ‘package:SeuratObject’: #> #> intersect #> The following objects are masked from ‘package:stats’: #> #> IQR, mad, sd, var, xtabs #> The following objects are masked from ‘package:base’: #> #> Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, #> as.data.frame, basename, cbind, colnames, dirname, do.call, #> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted, #> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, #> pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table, #> tapply, union, unique, unsplit, which.max, which.min #> Loading required package: S4Vectors #> #> Attaching package: ‘S4Vectors’ #> The following object is masked from ‘package:utils’: #> #> findMatches #> The following objects are masked from ‘package:base’: #> #> I, expand.grid, unname #> Loading required package: IRanges #> #> Attaching package: ‘IRanges’ #> The following object is masked from ‘package:sp’: #> #> %over% #> Loading required package: GenomeInfoDb #> Loading required package: Biobase #> Welcome to Bioconductor #> #> Vignettes contain introductory material; view with #> 'browseVignettes()'. To cite Bioconductor, see #> 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'. #> #> Attaching package: ‘Biobase’ #> The following object is masked from ‘package:MatrixGenerics’: #> #> rowMedians #> The following objects are masked from ‘package:matrixStats’: #> #> anyMissing, rowMedians #> #> Attaching package: ‘SummarizedExperiment’ #> The following object is masked from ‘package:Seurat’: #> #> Assays #> The following object is masked from ‘package:SeuratObject’: #> #> Assays sce <- SingleCellExperiment( assays = list(counts = matrix(1:5, 5L, 3L)), colData = DataFrame(cell = 1:3), rowData = DataFrame(gene = 1:5) ) from_SingleCellExperiment(sce, \"InMemory\") #> AnnData object with n_obs × n_vars = 3 × 5 #> obs: 'cell' #> var: 'gene'"},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a dataset — generate_dataset","title":"Generate a dataset — generate_dataset","text":"Generate dataset different types columns layers","code":""},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a dataset — generate_dataset","text":"","code":"generate_dataset( n_obs = 10L, n_vars = 20L, x_type = \"numeric_matrix\", layer_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\"), obs_types = c(\"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), var_types = c(\"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), obsm_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\", \"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), varm_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\", \"character\", \"integer\", \"factor\", \"factor_ordered\", \"logical\", \"numeric\", \"character_with_nas\", \"integer_with_nas\", \"factor_with_nas\", \"factor_ordered_with_nas\", \"logical_with_nas\", \"numeric_with_nas\"), obsp_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\"), varp_types = c(\"numeric_matrix\", \"numeric_dense\", \"numeric_csparse\", \"numeric_rsparse\", \"numeric_matrix_with_nas\", \"numeric_dense_with_nas\", \"numeric_csparse_with_nas\", \"numeric_rsparse_with_nas\", \"integer_matrix\", \"integer_dense\", \"integer_csparse\", \"integer_rsparse\", \"integer_matrix_with_nas\", \"integer_dense_with_nas\", \"integer_csparse_with_nas\", \"integer_rsparse_with_nas\"), uns_types = c(\"scalar_character\", \"scalar_integer\", \"scalar_factor\", \"scalar_factor_ordered\", \"scalar_logical\", \"scalar_numeric\", \"scalar_character_with_nas\", \"scalar_integer_with_nas\", \"scalar_factor_with_nas\", \"scalar_factor_ordered_with_nas\", \"scalar_logical_with_nas\", \"scalar_numeric_with_nas\", \"vec_character\", \"vec_integer\", \"vec_factor\", \"vec_factor_ordered\", \"vec_logical\", \"vec_numeric\", \"vec_character_with_nas\", \"vec_integer_with_nas\", \"vec_factor_with_nas\", \"vec_factor_ordered_with_nas\", \"vec_logical_with_nas\", \"vec_numeric_with_nas\", \"df_character\", \"df_integer\", \"df_factor\", \"df_factor_ordered\", \"df_logical\", \"df_numeric\", \"df_character_with_nas\", \"df_integer_with_nas\", \"df_factor_with_nas\", \"df_factor_ordered_with_nas\", \"df_logical_with_nas\", \"df_numeric_with_nas\", \"mat_numeric_matrix\", \"mat_numeric_dense\", \"mat_numeric_csparse\", \"mat_numeric_rsparse\", \"mat_numeric_matrix_with_nas\", \"mat_numeric_dense_with_nas\", \"mat_numeric_csparse_with_nas\", \"mat_numeric_rsparse_with_nas\", \"mat_integer_matrix\", \"mat_integer_dense\", \"mat_integer_csparse\", \"mat_integer_rsparse\", \"mat_integer_matrix_with_nas\", \"mat_integer_dense_with_nas\", \"mat_integer_csparse_with_nas\", \"mat_integer_rsparse_with_nas\", \"list\"), example = FALSE, format = c(\"list\", \"AnnData\", \"SingleCellExperiment\", \"Seurat\") )"},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a dataset — generate_dataset","text":"n_obs Number observations generate n_vars Number variables generate x_type Type matrix generate X layer_types Types matrices generate layers obs_types Types vectors generate obs var_types Types vectors generate var obsm_types Types matrices generate obsm varm_types Types matrices generate varm obsp_types Types matrices generate obsp varp_types Types matrices generate varp uns_types Types objects generate uns example TRUE, types overridden small set types. useful documentations. format Object type output, one \"list\", \"AnnData\", \"SingleCellExperiment\", \"Seurat\".","code":""},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a dataset — generate_dataset","text":"Object containing generated dataset defined output","code":""},{"path":"https://anndataR.data-intuitive.com/reference/generate_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a dataset — generate_dataset","text":"","code":"dummy <- generate_dataset() if (FALSE) { # \\dontrun{ dummy <- generate_dataset(format = \"AnnData\") dummy <- generate_dataset(format = \"SingleCellExperiment\") dummy <- generate_dataset(format = \"Seurat\") } # }"},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":null,"dir":"Reference","previous_headings":"","what":"Read H5AD — read_h5ad","title":"Read H5AD — read_h5ad","text":"Read data H5AD file","code":""},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read H5AD — read_h5ad","text":"","code":"read_h5ad( path, to = c(\"InMemoryAnnData\", \"HDF5AnnData\", \"SingleCellExperiment\", \"Seurat\"), mode = c(\"r\", \"r+\", \"a\", \"w\", \"w-\", \"x\"), ... )"},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read H5AD — read_h5ad","text":"path Path H5AD file read type object return. Must one : \"InMemoryAnnData\", \"HDF5AnnData\", \"SingleCellExperiment\", \"Seurat\" mode mode open HDF5 file. creates new file opens existing one read/write. r opens existing file reading. r+ opens existing file read/write. w creates file, truncating existing ones. w-/x synonyms, creating file failing already exists. ... Extra arguments provided adata$to_SingleCellExperiment() adata$to_Seurat(). See AnnData() information arguments functions. Note: update documentation r-lib/roxygen2#955 resolved.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read H5AD — read_h5ad","text":"object specified ","code":""},{"path":"https://anndataR.data-intuitive.com/reference/read_h5ad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read H5AD — read_h5ad","text":"","code":"h5ad_file <- system.file(\"extdata\", \"example.h5ad\", package = \"anndataR\") # Read the H5AD as a SingleCellExperiment object if (requireNamespace(\"SingleCellExperiment\", quietly = TRUE)) { sce <- read_h5ad(h5ad_file, to = \"SingleCellExperiment\") } # Read the H5AD as a Seurat object if (requireNamespace(\"SeuratObject\", quietly = TRUE)) { seurat <- read_h5ad(h5ad_file, to = \"Seurat\") } #> Warning: Data is of class dgRMatrix. Coercing to dgCMatrix. #> Warning: No columnames present in cell embeddings, setting to 'PC_1:38' #> Warning: No columnames present in cell embeddings, setting to 'umap_1:2'"},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert a Seurat object to an AnnData object — to_Seurat","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"to_Seurat() converts AnnData object Seurat object. one assay can converted time. Arguments used configure conversion. NULL, functions to_Seurat_guess_* used guess mapping.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"","code":"to_Seurat( adata, assay_name = \"RNA\", layers_mapping = NULL, reduction_mapping = NULL, graph_mapping = NULL, misc_mapping = NULL )"},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"adata AnnData object converted assay_name Name assay created (default: \"RNA\"). layers_mapping named list map AnnData layers Seurat layers. See section \"Layer mapping\" details. reduction_mapping named list map AnnData reductions Seurat reductions. item list must named list keys 'key', 'obsm', 'varm'. See section \"Reduction mapping\" details. graph_mapping named list map AnnData graphs Seurat graphs. item list must character vector length 1. See section \"Graph mapping\" details. misc_mapping named list map miscellaneous data names data Seurat object. See section \"Miscellaneous mapping\" details.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"Seurat object","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"layer-mapping","dir":"Reference","previous_headings":"","what":"Layer mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list map AnnData layers Seurat layers. item list must character vector length 1, values correspond names layers AnnData object, names correspond names layers resulting Seurat object. value NULL corresponds AnnData X slot. Example: layers_mapping = list(counts = \"counts\", data = NULL, foo = \"bar\"). NULL, internal function to_Seurat_guess_layers used guess layer mapping follows: AnnData layers copied Seurat layers name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"reduction-mapping","dir":"Reference","previous_headings":"","what":"Reduction mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list map AnnData $obsm $varm Seurat reductions. item list must named list keys 'key', 'obsm', 'varm'. Example: reduction_mapping = list(pca = list(key = \"PC_\", obsm = \"X_pca\", varm = \"PCs\")). NULL, internal function to_Seurat_guess_reductions used guess reduction mapping follows: $obsm items starting X_ copied name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"graph-mapping","dir":"Reference","previous_headings":"","what":"Graph mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list mapping graph names names graphs AnnData object. item list must character vector length 1. values correspond names graphs resulting Seurat object, names correspond names graphs AnnData object. Example: graph_mapping = list(nn = \"connectivities\"). NULL, internal function to_Seurat_guess_graphs used guess graph mapping follows: obsp named connectivities mapped nn. graphs starting connectivities_ stripped prefix copied name.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"miscellaneous-mapping","dir":"Reference","previous_headings":"","what":"Miscellaneous mapping","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"named list mapping miscellaneous data names data AnnData object. item list must vector one two elements. first element must one : 'X', 'layers', 'obs', 'obsm', 'obsp', 'var', 'varm', 'varp', 'uns'. second element name data corresponding slot. second element present, whole slot specified first element used. Example: misc_mapping = list(uns = \"uns\", varp_neighbors = c(\"varp\", \"neighbors\")). NULL, internal function to_Seurat_guess_misc used guess miscellaneous mapping follows: $uns defined, values $uns copied Seurat misc.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/to_Seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert a Seurat object to an AnnData object — to_Seurat","text":"","code":"ad <- AnnData( X = matrix(1:5, 3L, 5L), obs = data.frame(row.names = LETTERS[1:3], cell = 1:3), var = data.frame(row.names = letters[1:5], gene = 1:5) ) to_Seurat(ad) #> Warning: Data is of class matrix. Coercing to dgCMatrix. #> An object of class Seurat #> 5 features across 3 samples within 1 assay #> Active assay: RNA (5 features, 0 variable features) #> 1 layer present: counts"},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":null,"dir":"Reference","previous_headings":"","what":"Write H5AD — write_h5ad","title":"Write H5AD — write_h5ad","text":"Write H5AD file","code":""},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write H5AD — write_h5ad","text":"","code":"write_h5ad( object, path, compression = c(\"none\", \"gzip\", \"lzf\"), mode = c(\"w-\", \"r\", \"r+\", \"a\", \"w\", \"x\") )"},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write H5AD — write_h5ad","text":"object object write, either \"SingleCellExperiment\" \"Seurat\" object path Path file write compression compression algorithm use writing HDF5 file. Can one \"none\", \"gzip\" \"lzf\". Defaults \"none\". mode mode open HDF5 file. creates new file opens existing one read/write. r+ opens existing file read/write. w creates file, truncating existing ones w-/x synonyms creating file failing already exists.","code":""},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write H5AD — write_h5ad","text":"path invisibly","code":""},{"path":"https://anndataR.data-intuitive.com/reference/write_h5ad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write H5AD — write_h5ad","text":"","code":"adata <- AnnData( X = matrix(1:5, 3L, 5L), layers = list( A = matrix(5:1, 3L, 5L), B = matrix(letters[1:5], 3L, 5L) ), obs = data.frame(row.names = LETTERS[1:3], cell = 1:3), var = data.frame(row.names = letters[1:5], gene = 1:5) ) h5ad_file <- tempfile(fileext = \".h5ad\") write_h5ad(adata, h5ad_file) # Write a SingleCellExperiment as an H5AD if (requireNamespace(\"SingleCellExperiment\", quietly = TRUE)) { ncells <- 100 counts <- matrix(rpois(20000, 5), ncol = ncells) logcounts <- log2(counts + 1) pca <- matrix(runif(ncells * 5), ncells) tsne <- matrix(rnorm(ncells * 2), ncells) sce <- SingleCellExperiment::SingleCellExperiment( assays = list(counts = counts, logcounts = logcounts), reducedDims = list(PCA = pca, tSNE = tsne) ) h5ad_file <- tempfile(fileext = \".h5ad\") write_h5ad(sce, h5ad_file) } # Write a Seurat as a H5AD if (requireNamespace(\"SeuratObject\", quietly = TRUE)) { # TODO: uncomment this code when the seurat converter is fixed # counts <- matrix(1:15, 3L, 5L) # dimnames(counts) <- list( # letters[1:3], # LETTERS[1:5] # ) # gene.metadata <- data.frame( # row.names = LETTERS[1:5], # gene = 1:5 # ) # obj <- SeuratObject::CreateSeuratObject(counts, meta.data = gene.metadata) # cell.metadata <- data.frame( # row.names = letters[1:3], # cell = 1:3 # ) # obj <- SeuratObject::AddMetaData(obj, cell.metadata) # # h5ad_file <- tempfile(fileext = \".h5ad\") # write_h5ad(obj, h5ad_file) } #> NULL"},{"path":[]},{"path":"https://anndataR.data-intuitive.com/news/index.html","id":"new-features-0-99-0","dir":"Changelog","previous_headings":"","what":"New features","title":"anndataR 0.99.0","text":"Update DESCRIPTION file reflect current release R version used Bio: R (>= 3.2.0) –> R (>= 4.3.0). Reformat NEWS file follow conventions. X layers obs obs_names var var_names HDF5AnnData InMemoryAnnData SingleCellExperiment Seurat","code":""}]