RudRho / DECENT

Differential Expression with Capture Efficiency adjustmeNT

DECENT

Differential Expression with Capture Efficiency adjustmeNT for single-cell RNA-seq data

C. Ye, TP. Speed, A. Salim (2017) DECENT: Differential Expression with Capture Efficiency AdjustmeNT for Single-Cell RNA-seq Data. bioRxiv. https://doi.org/10.1101/225177

News

Aug 15, 2018

• Version 0.99.1 released
• Rho now depends on the mean by a logistic linear model.
• Gaussian quadrature approximation for optimization in LRT.
• Added single imputation function.
• GLM framework for complex design.
• Other corresponding changes.

Feb 6, 2018

• Version 0.2.0 released.
• Rewrite LRT in Rcpp.

Jan 30, 2018

• Version 0.1.2 released.
• Changed LRT starting values.
• Other minor changes.

Jan 15, 2018

• Version 0.1.1 released.
• Imputed data matrix can now be obtained by calling function getImputed or set imputed = TRUE when calling decent.

Installation

You can install DECENT from github with:

require(devtools)
devtools::install_github("cz-ye/DECENT")

Quick start

Here we use a simulated dataset for demonstration

data("sim")

# DECENT with spike-ins
de.table <- decent(data.obs = sim$data.obs, X = ~as.factor(sim$cell.type), use.spikes = T, 
                   spikes = sim$sp.obs, spike.conc = sim$sp.true)

# DECENT without spike-ins
de.table <- decent(data.obs = sim$data.obs, X = ~as.factor(sim$cell.type))

# Ground truth can be found in the DE.gene vector.

The output object of DE model, no-DE model and LRT will be saved in the working directory (dir argument) as decent.DE.rds, decent.noDE.rds and decent.lrt.rds. A data frame containing the DE results is returned by the function.

Note that the LRT step also involves optimization of parameters and is currently the bottleneck step.

The function is by default run in parallel using all cores. Specify the number of cores to use by changing the argument n.cores. Use option parallel = F to run on single core.

By default, cell size factors are estimated using MLE. In some cases, TMM (setting normalize = 'TMM') gives more accurate estimates.