added dataset -- closes #148

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: scLANE
 Type: Package
 Title: Model gene expression dynamics with spline-based NB GLMs, GEEs, & GLMMs
-Version: 0.7.5
+Version: 0.7.6
 Authors@R: c(person(given = "Jack", family = "Leary", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0009-0004-8821-3269")), 
              person(given = "Rhonda", family = "Bacher", email = "[email protected]", role = c("ctb", "fnd"), comment = c(ORCID = "0000-0001-5787-476X")))
 Description: This package uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. 
              The modeling architectures currently supported are negative binomial GLMs, GEEs, & GLMMs. 
              Downstream analysis functionalities include model comparison, dynamic gene clustering, smoothed counts generation, gene set enrichment testing, & visualization. 
 License: MIT + file LICENSE
 Encoding: UTF-8
-LazyData: false
+LazyData: true
 RoxygenNote: 7.2.1
 Depends: 
   glm2, 

NAMESPACE

@@ -30,6 +30,7 @@ importFrom(MASS,glm.nb)
 importFrom(MASS,negative.binomial)
 importFrom(MASS,theta.mm)
 importFrom(Matrix,colSums)
+importFrom(Matrix,t)
 importFrom(Rcpp,sourceCpp)
 importFrom(bigstatsr,as_FBM)
 importFrom(broom.mixed,tidy)

NEWS.md

@@ -1,3 +1,8 @@
+# `scLANE` v0.7.6
+
+* Added Zenodo tracking.
+* Added simulated dataset to `data/`
+
 # `scLANE` v0.7.5
 
 * Preparing for BioConductor submission i.e., reformatting code, adding documentation, etc.

R/data.R

@@ -0,0 +1,19 @@
+#' A \code{\link[SingleCellExperiment]{SingleCellExperiment}} object containing simulated counts.
+#'
+#' Data simulated using the \code{scaffold} R package for 50 dynamic and 50 static genes across 1200 cells from 3 subjects.
+#'
+#' @format An object of class \code{\link[SingleCellExperiment]{SingleCellExperiment}}.
+#' @source https://www.rhondabacher.com/scaffold-vignette.pdf
+#' @usage data(sim_counts)
+"sim_counts"
+
+#' A data.frame containing ground-truth pseudotime.
+#'
+#' The true ordering of the 1200 cells contained in \code{sim_counts}.
+#'
+#' @format An object of class \code{data.frame} with 1200 rows and one variable:
+#' \itemize{
+#'  \item PT: the true pseudotime (0.0025--1)
+#' }
+#' @usage data(sim_pseudotime)
+"sim_pseudotime"

R/testDynamic.R

@@ -5,6 +5,7 @@
 #' @description This function tests whether a NB \code{marge} model is better than a null (intercept-only) NB GLM using the Likelihood Ratio Test. In effect, the test tells us whether a gene's expression changes (in any way) over pseudotime.
 #' @import glm2
 #' @import magrittr
+#' @importFrom Matrix t
 #' @importFrom bigstatsr as_FBM
 #' @importFrom foreach foreach %dopar% registerDoSEQ
 #' @importFrom doParallel registerDoParallel
@@ -45,34 +46,29 @@
 #' @seealso \code{\link[glmmTMB]{glmmTMB}}
 #' @export
 #' @examples
-#' \dontrun{
-#' testDynamic(expr.mat = raw_counts,
-#'             pt = pseudotime_df,
-#'             parallel.exec = TRUE)
-#' testDynamic(expr.mat = sce_obj,
-#'             pt = slingshot_obj,
-#'             size.factor.offset = sizeFactors(sce_obj),
-#'             genes = rownames(sce_obj)[1:100])
-#' testDynamic(expr.mat = raw_counts,
-#'             pt = pseudotime_df,
-#'             parallel.exec = TRUE,
-#'             n.cores = 8,
-#'             n.potential.basis.fns = 7)
-#' testDynamic(expr.mat = counts(sce_obj),
-#'             pt = pseudotime_df,
+#' \donttest{
+#' data(sim_counts)
+#' data(sim_pseudotime)
+#' cell_offset <- createCellOffset(sim_counts)
+#' testDynamic(sim_counts,
+#'             pt = sim_pseudotime,
+#'             size.factor.offset = cell_offset,
+#'             genes = sample(rownames(sim_counts), 20))
+#' testDynamic(sim_counts,
+#'             pt = sim_pseudotime,
+#'             size.factor.offset = cell_offset,
 #'             is.gee = TRUE,
-#'             id.vec = colData(sce_obj)$subject_id,
+#'             id.vec = sim_counts$subject,
 #'             cor.structure = "ar1",
-#'             parallel.exec = TRUE,
-#'             n.cores = 8,
-#'             n.potential.basis.fns = 7)
-#' testDynamic(expr.mat = seu_obj,
-#'             pt = pseudotime_df,
-#'             parallel.exec = TRUE,
-#'             n.cores = 8,
+#'             genes = sample(rownames(sim_counts), 20))
+#' testDynamic(sim_counts,
+#'             pt = sim_pseudotime,
+#'             size.factor.offset = cell_offset,
 #'             is.glmm = TRUE,
-#'             id.vec = seu_obj$subject_id)
-#' }
+#'             glmm.adaptive = TRUE,
+#'             id.vec = sim_counts$subject,
+#'             genes = sample(rownames(sim_counts), 20))
+#'}
 
 testDynamic <- function(expr.mat = NULL,
                         pt = NULL,
@@ -98,19 +94,18 @@ testDynamic <- function(expr.mat = NULL,
   }
   if (inherits(expr.mat, "SingleCellExperiment")) {
     expr.mat <- BiocGenerics::counts(expr.mat)[genes, ]
-    expr.mat <- as.matrix(expr.mat)
   } else if (inherits(expr.mat, "Seurat")) {
     expr.mat <- Seurat::GetAssayData(expr.mat,
                                      slot = "counts",
                                      assay = Seurat::DefaultAssay(expr.mat))
-    expr.mat <- as.matrix(expr.mat[genes, ])
+    expr.mat <- expr.mat[genes, ]
   } else if (inherits(expr.mat, "dgCMatrix")) {
-    expr.mat <- as.matrix(expr.mat[genes, ])
+    expr.mat <- expr.mat[genes, ]
   } else {
     expr.mat <- expr.mat[genes, ]
   }
+  expr.mat <- as.matrix(Matrix::t(expr.mat))  # transpose to dense cell x gene matrix
   if (!(inherits(expr.mat, "matrix") || inherits(expr.mat, "array"))) { stop("Input expr.mat must be coerceable to a matrix of integer counts.") }
-  expr.mat <- t(expr.mat)  # transpose to cell x gene matrix
 
   # extract pseudotime dataframe if input is results from Slingshot
   if (inherits(pt, "SlingshotDataSet")) {