Merge pull request compbiomed#46 from dfjenkins3/master

Added example data, bugfix for pData, and fix for travis integration

.travis.yml

@@ -5,3 +5,6 @@ sudo: required
 warnings_are_errors: true
 
 bioc_required: true
+
+before_install:
+  - Rscript -e 'update.packages(ask = FALSE)'

DESCRIPTION

@@ -4,6 +4,8 @@ Title: Interactive Analysis of Single Cell RNA-Seq Data
 Version: 0.1.2
 Author: David Jenkins
 Maintainer: David Jenkins <dfj@bu.edu>
+Depends:
+    R (>= 3.2)
 Description: Run common single cell analysis directly through your browser
     including differential expression, downsampling analysis, and clustering.
 License: MIT + file LICENSE

R/data.R

@@ -0,0 +1,21 @@
+#' Example Single Cell RNA-Seq data in SCESet Object
+#' 
+#' A subset of 30 samples from a single cell RNA-Seq experiment from Zeisel, et
+#' al. Science 2015. The data was produced from cells from the mouse
+#' somatosensory cortex (S1) and hippocampus (CA1). 15 of the cells were
+#' identified as oligodendrocytes and 15 of the cell were identified as
+#' microglia.
+#' 
+#' @name GSE60361_subset
+#' @docType data
+#' @format List of two data frames, with counts and annotations. Use them as
+#' input to createSCESet()
+#' @source DOI: 10.1126/science.aaa1934
+#' @keywords datasets
+#' @examples 
+#' library(scater)
+#' data("GSE60361_subset")
+#' GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,
+#'                                 annotfile = GSE60361_subset$annot,
+#'                                 inputdataframes = TRUE)
+"GSE60361_subset"

R/misc_functions.R

@@ -36,6 +36,12 @@ summarizeTable <- function(indata){
 #'
 #' @return a SCESet object
 #' @export createSCESet
+#' @examples 
+#' library(scater)
+#' data("GSE60361_subset")
+#' GSE60361_SCESet <- createSCESet(countfile = GSE60361_subset$counts,
+#'                                 annotfile = GSE60361_subset$annot,
+#'                                 inputdataframes = TRUE)
 createSCESet <- function(countfile=NULL, annotfile=NULL, featurefile=NULL,
                          inputdataframes=FALSE){
   if(is.null(countfile)){

R/scDiffEx.R

@@ -26,7 +26,7 @@
 scDiffEx <- function(inSCESet, condition, significance=0.05, ntop=500,
                      usesig=TRUE, diffexmethod, clusterRow=TRUE,
                      clusterCol=TRUE){
-  in.condition <- droplevels(as.factor(scater::pData(inSCESet)[,condition]))
+  in.condition <- droplevels(as.factor(Biobase::pData(inSCESet)[,condition]))
   if (length(levels(in.condition)) != 2)
     stop("only two labels supported, ", condition, " has ",
          length(levels(in.condition)), " labels")
@@ -73,7 +73,7 @@ scDiffEx <- function(inSCESet, condition, significance=0.05, ntop=500,
 #'
 plot_DiffEx <- function(inSCESet, condition, geneList, clusterRow=TRUE,
                      clusterCol=TRUE){
-  diffex.annotation <- data.frame(scater::pData(inSCESet)[,condition])
+  diffex.annotation <- data.frame(Biobase::pData(inSCESet)[,condition])
   colnames(diffex.annotation) <- condition
   topha <- ComplexHeatmap::HeatmapAnnotation(df = diffex.annotation,
                                              height = unit(0.333, "cm"))
@@ -101,7 +101,7 @@ plot_DiffEx <- function(inSCESet, condition, geneList, clusterRow=TRUE,
 #'
 plot_d3DiffEx <- function(inSCESet, condition, geneList, clusterRow=TRUE,
                           clusterCol=TRUE){
-  diffex.annotation <- data.frame(scater::pData(inSCESet)[,condition])
+  diffex.annotation <- data.frame(Biobase::pData(inSCESet)[,condition])
   colnames(diffex.annotation) <- condition
   topha <- ComplexHeatmap::HeatmapAnnotation(df = diffex.annotation,
                                              height = unit(0.333, "cm"))
@@ -126,7 +126,7 @@ plot_d3DiffEx <- function(inSCESet, condition, geneList, clusterRow=TRUE,
 #'
 scDiffEx_deseq2 <- function(inSCESet, condition){
   cnts <- scater::counts(inSCESet)
-  annot_data <- scater::pData(inSCESet)[,condition,drop=F]
+  annot_data <- Biobase::pData(inSCESet)[,condition,drop=F]
   colnames(annot_data) <- "condition"
   dds <- DESeq2::DESeqDataSetFromMatrix(countData = cnts,
                                         colData = annot_data,
@@ -174,7 +174,7 @@ scDiffEx_deseq <- function(inSCESet, condition){
 #' @export scDiffEx_limma
 #'
 scDiffEx_limma <- function(inSCESet, condition){
-  design <- stats::model.matrix(~factor(scater::pData(inSCESet)[,condition]))
+  design <- stats::model.matrix(~factor(Biobase::pData(inSCESet)[,condition]))
   fit <- limma::lmFit(Biobase::exprs(inSCESet), design)
   ebayes <- limma::eBayes(fit)
   topGenes <- limma::topTable(ebayes, coef=2, adjust="fdr", number=nrow(inSCESet))

inst/shiny/ui.R

@@ -1,3 +1,4 @@
+library(singleCellTK)
 library(shiny)
 library(shinyjs)
 library(plotly)