integrateWithSingleCell: Integrate bulk DE results with Bioconductor single-cell...

In DESeq2: Differential gene expression analysis based on the negative binomial distribution

Description

A function that assists with integration of bulk DE results tables with pre-processed scRNA-seq datasets available on Bioconductor, for downstream visualization tasks. The user is prompted to pick a scRNA-seq dataset from a menu. The output of the function is a list with the original results table, bulk gene counts, and the SingleCellExperiment object selected by the user.

Usage

integrateWithSingleCell(res, dds)

Arguments

res	a results table, as produced via results
dds	a DESeqDataSet with the bulk gene expression data (should contain gene-level counts)

Details

This function assists the user in choosing a datset from a menu of options that are selected based on the organism of the current dataset. Currently only human and mouse bulk and single-cell RNA-seq datasets are supported, and it is assumed that the bulk DE dataset has GENCODE or Ensembl gene identifiers. Following the selection of the scRNA-seq dataset, visualization can be performed with a package vizWithSCE, which can be installed with install_github("KwameForbes/vizWithSCE").

Value

list containing: res, dds, and a SingleCellExperiment as selected by the user

Author(s)

Kwame Forbes

Examples

## Not run: 
  # involves interactive menu selection...
  dds <- makeExampleDESeqDataSet()
  rownames(dds) <- paste0("ENSG",1:nrow(dds))
  dds <- DESeq(dds)
  res <- results(dds)
  dat <- integrateWithSingleCell(res, dds)

## End(Not run)

DESeq2 documentation built on Feb. 22, 2021, 10 a.m.