The DESeq2-apeglm With Inferential Samples implementation supposes
a hierarchical distribution of log2 fold changes.
The final posterior standard deviation is calculated by
adding the posterior variance from modeling biological replicates
apeglm, and the observed variance on the posterior mode
over inferential replicates. This function requires the DESeq2 and
apeglm packages to be installed and will print an error if they are
a SummarizedExperiment containing the inferential
replicate matrices, as output by
the design matrix
the coefficient to test (see
a SummarizedExperiment with metadata columns added: the log2 fold change and posterior SD using inferential replicates, and the original log2 fold change (apeglm) and its posterior SD
lfcShrink function in the
Zhu, Ibrahim, Love "Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences" Bioinformatics (2018).
Love, Huber, Anders "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" Genome Biology (2014).
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