1.1.17

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: dreamlet
 Type: Package
 Title: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
-Version: 1.0.1
+Version: 1.1.17
 Date: 2024-02-12
 Authors@R: person("Gabriel", "Hoffman", role=c("aut", "cre"), email="gabriel.hoffman@mssm.edu", comment=c(ORCID="0000-0002-0957-0224"))
 Description: Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.

NEWS.md

@@ -1,4 +1,7 @@
 
+# dreamlet 1.1.17
+  - bug fix in `plotPCA()`
+
 # dreamlet 1.1.16
   - Feb 8, 2024
   - fix bug in `pbWeights()`

R/plotPCA.R

@@ -16,6 +16,7 @@ setGeneric("plotPCA", BiocGenerics::plotPCA)
 #' @param maxOutlierZ cap outlier z-scores at this value for plotting to maintain consistent color scale
 #' @param nrow number of rows in plot
 #' @param size size passed to \code{geom_point()}
+#' @param fdr.cutoff FDR cutoff to determine outlier
 #' 
 #' @name plotPCA
 #' @rdname plotPCA
@@ -42,27 +43,24 @@ setGeneric("plotPCA", BiocGenerics::plotPCA)
 #
 #' @seealso \code{outlierByAssay()}
 #' @export
-setMethod("plotPCA", signature(object="dreamletProcessedData"), function(object, assays = assayNames(object), nPC=2, robust = FALSE, ..., maxOutlierZ=20, nrow=2, size=2){
+setMethod("plotPCA", signature(object="dreamletProcessedData"), function(object, assays = assayNames(object), nPC=2, robust = FALSE, ..., maxOutlierZ=20, nrow=2, size=2, fdr.cutoff=0.05){
 
   stopifnot(all(assays %in% assayNames(object)))
 
   PC1 <- PC2 <- z <- pValue <- FDR <- NULL
 
-  outlierByAssay( object, assays, robust = robust, ...) %>%
+  outlierByAssay( object, assays, robust = robust, nPC=nPC,...) %>%
     tibble(FDR = p.adjust(pValue, "fdr")) %>%
     arrange(FDR) %>%
     ggplot(aes(PC1, PC2, 
       color = pmin(z, maxOutlierZ),
-      shape = FDR < 0.05)) +
+      shape = FDR < fdr.cutoff)) +
       geom_point(size=size) +
       theme_classic() +
       theme(aspect.ratio=1, plot.title = element_text(hjust = 0.5)) +
       scale_color_gradient("Outlier z", limits=c(0, maxOutlierZ), low="grey60", high="red") +
+      scale_shape_discrete(bquote(FDR < .(fdr.cutoff))) +
       facet_wrap( ~ assay, nrow=nrow, scales="free")
 })
 
 
-
-
-
-