SCDETest: Differential expression using scde

In Coolgenome/iTALK: Characterize and Illustrate Intercellular Communication

Description

Identifies differentially expressed genes between two groups of cells using scde

Usage

SCDETest(sub_data, min_gene_expressed, min_valid_cells,
  contrast = unique(sub_data$compare_group), batch = NULL,
  n.randomizations = 150, n.cores = 10, batch.models = models,
  return.posteriors = FALSE, verbose = 1)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
batch	Different batch identifier
n.cores	number of cores to utilize
batch.models	(optional) separate models for the batch data (if generated using batch-specific group argument). Normally the same models are used.
return.posteriors	whether joint posterior matrices should be returned
verbose	integer verbose level (1 for verbose)
@param	n.randomizations number of bootstrap randomizations to be performed

Details

This test does not support pre-processed genes. To use this method, please install scde, using the instructions at http://hms-dbmi.github.io/scde/tutorials.html

Value

A matrix of differentially expressed genes and related statistics.

References

"Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi:10.1038/nmeth.2967) https://github.com/hms-dbmi/scde

Coolgenome/iTALK documentation built on Aug. 3, 2019, 3:12 p.m.