Distribution-Free Single-Cell Differential Expression Analysis

Complex hypothesis testing through conditional cumulative distribution function estimation. Method is detailed in: Gauthier M, Agniel D, Thiebaut R & Hejblum BP (2020). "Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis", BioRxiv .

ccdf

Overview

ccdf is a package for performing single-cell RNA-seq differential expression analysis and more generally complex hypothesis testing.

The main function of the package is ccdf_testing(). It allows to use either an asymptotic test for large sample size or a permutation test for small sample size with the argument method.

The methods implemented in this package are detailed in the following article:

Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020). Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis, BioRxiv DOI:10.1101/2021.05.21.445165

Installation

To install ccdf, you can download the development version on GitHub.

#install.packages("devtools")
devtools::install_github("Mgauth/ccdf")

Example

Here is a basic example which shows how to use ccdf with simple generated data.

## Data Generation
X <- rbinom(n=100, size = 1, prob = 0.5)
Y <- t(replicate(10, ((X==1)*rnorm(n = 50,0,1)) + ((X==0)*rnorm(n = 50,0.5,1))))

# Hypothesis testing
res_asymp <- ccdf_testing(exprmat=data.frame(Y=Y), variable2test=data.frame(X=as.factor(X)), test="asymptotic")$pvals # asymptotic test
res_perm <- ccdf_testing(exprmat=Y, variable2test=X, test="permutations",
                         adaptive=TRUE)$pvals # adaptive permutation test

– Marine Gauthier, Denis Agniel, Rodolphe Thiébaut & Boris Hejblum