From ef5cbd1a03ebe572ebdecd8d990e35c5a9912b6e Mon Sep 17 00:00:00 2001
From: rcannood <rcannood@gmail.com>
Date: Fri, 15 Nov 2024 10:10:08 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20scverse/?=
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---
 pkgdown.yml                     |  2 +-
 reference/anndataR-package.html |  3 ++-
 reference/from_Seurat.html      | 22 +++++++++++-----------
 search.json                     |  2 +-
 4 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/pkgdown.yml b/pkgdown.yml
index 64ca1a0..a7ec8d8 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   anndataR: anndataR.html
   design: design.html
-last_built: 2024-11-14T07:55Z
+last_built: 2024-11-15T10:09Z
 urls:
   reference: https://anndataR.data-intuitive.com/reference
   article: https://anndataR.data-intuitive.com/articles
diff --git a/reference/anndataR-package.html b/reference/anndataR-package.html
index e9083e3..356b061 100644
--- a/reference/anndataR-package.html
+++ b/reference/anndataR-package.html
@@ -126,11 +126,12 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 <p>Authors:</p><ul><li><p>Luke Zappia <a href="mailto:luke@lazappi.id.au">luke@lazappi.id.au</a> (<a href="https://orcid.org/0000-0001-7744-8565" class="external-link">ORCID</a>) (lazappi)</p></li>
 <li><p>Martin Morgan <a href="mailto:mtmorgan.bioc@gmail.com">mtmorgan.bioc@gmail.com</a> (<a href="https://orcid.org/0000-0002-5874-8148" class="external-link">ORCID</a>) (mtmorgan)</p></li>
 <li><p>Louise Deconinck <a href="mailto:louise.deconinck@gmail.com">louise.deconinck@gmail.com</a> (<a href="https://orcid.org/0000-0001-8100-6823" class="external-link">ORCID</a>) (LouiseDck)</p></li>
-<li><p>Data Intuitive <a href="mailto:info@data-intuitive.com">info@data-intuitive.com</a></p></li>
 </ul><p>Other contributors:</p><ul><li><p>Danila Bredikhin <a href="mailto:danila.bredikhin@embl.de">danila.bredikhin@embl.de</a> (<a href="https://orcid.org/0000-0001-8089-6983" class="external-link">ORCID</a>) (gtca) [contributor]</p></li>
 <li><p>Isaac Virshup (<a href="https://orcid.org/0000-0002-1710-8945" class="external-link">ORCID</a>) (ivirshup) [contributor]</p></li>
 <li><p>Brian Schilder <a href="mailto:brian_schilder@alumni.brown.edu">brian_schilder@alumni.brown.edu</a> (<a href="https://orcid.org/0000-0001-5949-2191" class="external-link">ORCID</a>) (bschilder) [contributor]</p></li>
 <li><p>Chananchida Sang-aram (<a href="https://orcid.org/0000-0002-0922-0822" class="external-link">ORCID</a>) (csangara) [contributor]</p></li>
+<li><p>Data Intuitive <a href="mailto:info@data-intuitive.com">info@data-intuitive.com</a> [funder, copyright holder]</p></li>
+<li><p>Chan Zuckerberg Initiative [funder]</p></li>
 </ul></div>
 
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
diff --git a/reference/from_Seurat.html b/reference/from_Seurat.html
index d7b25a2..673b539 100644
--- a/reference/from_Seurat.html
+++ b/reference/from_Seurat.html
@@ -269,10 +269,10 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'</span>
 <span class="r-wrn co"><span class="r-pr">#&gt;</span> This message will be shown once per session</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 UMAP embedding parameters a = 0.9922 b = 1.112</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 Read 200 rows and found 10 numeric columns</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 Using Annoy for neighbor search, n_neighbors = 30</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 Building Annoy index with metric = cosine, n_trees = 50</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 UMAP embedding parameters a = 0.9922 b = 1.112</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 Read 200 rows and found 10 numeric columns</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 Using Annoy for neighbor search, n_neighbors = 30</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 Building Annoy index with metric = cosine, n_trees = 50</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> 0%   10   20   30   40   50   60   70   80   90   100%</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> [----|----|----|----|----|----|----|----|----|----|</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> *</span>
@@ -326,14 +326,14 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span> *</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> *</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> |</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 Writing NN index file to temp file /tmp/RtmpHHMOLQ/file17d121ba02a3</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 Searching Annoy index using 1 thread, search_k = 3000</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 Annoy recall = 100%</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:55:59 Commencing smooth kNN distance calibration using 1 thread</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 Writing NN index file to temp file /tmp/RtmpePOFm4/file17c22df9ca6b</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 Searching Annoy index using 1 thread, search_k = 3000</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 Annoy recall = 100%</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:28 Commencing smooth kNN distance calibration using 1 thread</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  with target n_neighbors = 30</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:56:00 Initializing from normalized Laplacian + noise (using RSpectra)</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:56:00 Commencing optimization for 500 epochs, with 6160 positive edges</span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> 07:56:00 Optimization finished</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:29 Initializing from normalized Laplacian + noise (using RSpectra)</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:29 Commencing optimization for 500 epochs, with 6160 positive edges</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> 10:09:29 Optimization finished</span>
 <span class="r-in"><span><span class="fu">from_Seurat</span><span class="op">(</span><span class="va">obj</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> AnnData object with n_obs × n_vars = 200 × 100</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA'</span>
diff --git a/search.json b/search.json
index ecfb89e..b45a5f6 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) Data Intuitive info@data-intuitive.com 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]","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 #> 07:55:59 UMAP embedding parameters a = 0.9922 b = 1.112 #> 07:55:59 Read 200 rows and found 10 numeric columns #> 07:55:59 Using Annoy for neighbor search, n_neighbors = 30 #> 07:55:59 Building Annoy index with metric = cosine, n_trees = 50 #> 0%   10   20   30   40   50   60   70   80   90   100% #> [----|----|----|----|----|----|----|----|----|----| #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> * #> | #> 07:55:59 Writing NN index file to temp file /tmp/RtmpHHMOLQ/file17d121ba02a3 #> 07:55:59 Searching Annoy index using 1 thread, search_k = 3000 #> 07:55:59 Annoy recall = 100% #> 07:55:59 Commencing smooth kNN distance calibration using 1 thread #>  with target n_neighbors = 30 #> 07:56:00 Initializing from normalized Laplacian + noise (using RSpectra) #> 07:56:00 Commencing optimization for 500 epochs, with 6160 positive edges #> 07:56:00 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_1.42.0         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":""}]