This github repository provides
- The source code of function voomByGroup
- Help page about how to run DE analysis with voom and its derived methods (voomWithQualityWeights (with and without block), voomByGroup). Comparison between results obtained from them are included as well.
- Scripts for simulations and analysis of COVID-19 patients data from our preprint: Modeling group heteroscedasticity for pseudo-bulk samples in single-cell RNA-seq data.
#voom
voom(y, design=design, ...)
#voomQW with sample variability used
voomWithQualityWeights(y, design=design, ...)
#voomQW with block variability used
voomWithQualityWeights(y, design=design, var.group=group, ...)
#voomByGroup
voomByGroup(y, design=design, group=group, ...)
voomByGroup(counts, design, group, ...)
#arguments:
counts a numeric matrix containing raw counts, or an ExpressionSet containing raw counts, or a DGEList object.
group a vector or factor indicating groups to have different variation to model group-wise variance.
design design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that samples are treated as replicates.
dynamic a vector of logical values indicating whether to use overall mean-variance to estimate variation for observations in a specific group
plot denotes to how to plot the mean-variance trends. Available options are:
* "none": no plot
* "separate": individual mean-variance plots for each group
* "combine": all mean-variance curves in a single plot
* "all": individual mean-variance plots for each group, followed by a single plot with all mean-variance cuvres.
... other arguments are passed to voom