This package allows for the differential expression analysis of two groups of scRNA-seq count data. It employs a novel family of discrete distributions for describing the difference of two NB distributions (named DOTNB). DEGage take the raw counts of scRNA-seq as inputs, and thus avoid introducing artificially bias in normalization steps in current methods. A workflow is shown as follows.
To install DEGage, run the following:
library(devtools)
install_github("chenyongrowan/DEGage")
For more detailed documentation and tutorials, please find them on RPubs by RStudio 
:
https://rpubs.com/aliciaprowan/1043456
DEGage contains 5 main functions: DEGage(), DEGage_multitest(), DEGage_preprocess(), DEGage_complete(), and DEGage_Simulation().
DEGage performs pairwise differential analysis on NGS count data. The input is typically a dataframe where columns contain samples and rows contain genes. DEGage equips with two sampling strategies, random assignment and subsampling, for handling imbalanced scRNA-seq datasets.
DEGage(counts, group, perm.preprocess = TRUE,
gene.filter.threshold = 1, nperms = 2000,
nsubsample = NA, perm.pval = 0.1, ncores = 4,
maxiter = 1000, mean.ratio = 1.4, subsampled.k = T)
Similar to DEGage, except it can perform an indefinite number of pairwise comparisons.
DEGage_multitest(counts, group, perm.preprocess = FALSE,
gene.filter.threshold = 1, nperms = 2000,
nsubsample = NA, perm.pval = 0.1, ncores = 4,
writing.dir = NULL)
Performs a simple scRNA-seq count pre-processing workflow with Seurat and generates automatic cell type annotations with SingleR. It is primarily used in DEGage_complete; however, it is available for use individually.
DEGage_preprocess(input, dir.type = 'mtx', min.nFeatureRNA = 200,
max.nFeatureRNA = 8000, mt.percent = .2,
cell.annotations = TRUE,
celltype.min = 20, output = "Seurat")
Takes unprocessed scRNA-seq counts as an input, automatically processes and generates cell type annotations for them, then performs differential expression analysis on the groups identified through cell-type clustering.
DEGage_complete(input, dir.type = 'mtx',
min.nFeatureRNA = 200, max.nFeatureRNA = 8000,
mt.percent = .2, cell.annotations = TRUE,
celltype.min = 20, perm.preprocess = FALSE,
gene.filter.threshold = 1, nperms = 2000,
nsubsample = NA, perm.pval = 0.1,
ncores = 4, writing.dir = NULL)
Generates simplified simulated scRNA-Seq counts following an NB distribution with pre-defined proportions of dropouts.
DEGage_Simulation(ngenes, ndegs, cellgroups, lfc = 1,
prop.zeros = .3, seed = NULL, ncores = 4)
In this section, we will detail how to use DEGage functions
First, we will simulate a small data frame of counts to pass through DEGage() using DEGage simulate. It has 100 cells total with 50 in each condition, as well as 1000 genes with 100 degs:
library(DEGage)
cellgroups <- factor(c(rep(1,50), rep(2,50)))
df <- DEGage_Simulation(ngenes = 1000, ndegs = 100, cellgroups = cellgroups)
Next, we will pass these counts through DEGage:
results <- DEGage(counts = df, group = cellgroups)
To test DEGage_multitest, we will simulate a second dataframe of counts, merge them together, and pass them through DEGage_multitest():
cellgroups2 <- factor( c( rep(3,50), rep(4,50) ) )
df2 <- DEGage_Simulation(ngenes = 1000, ndegs = 100, cellgroups = cellgroups2)
df <- cbind(df, df2)
cellgroups <- factor(c(cellgroups, cellgroups2))
multitest.results <- DEGage_multitest(df, cellgroups)
Please cite the following article if you use DEGage in your research:
Petrany A., Chen R., Zhang S. and Chen, Y. "Theoretical Framework and Application for the Difference of Two Negative Binomial Distributions in Comparative Analysis of Sequencing Data". Genome Research, Under Review.