I've been using DEseq2 to find the differentially expressed genes on a dataset of RNAseq samples. There about 700 cases and 300 control samples.

The design formula is "~ Condition + age + sex". DEseq2 takes a really long time to finish. It used about 16h with 8 cores and 32 GB memory.

Here is my running codes:

dds <- DESeqDataSetFromMatrix(countData = round(cts),
                                colData = covarianceTable,
                                design = ~ CONDITION + age + sex)

register(MulticoreParam(8))

## It takes a long time to run.................
dds_subset <- DESeq(dds,parallel=TRUE, BPPARAM=MulticoreParam(8))
resultsNames(dds_subset)

resLFC <- lfcShrink(dds_subset, coef="CONDITION_Case_vs_Control", type="apeglm",
                    parallel=TRUE, BPPARAM=MulticoreParam(8))

Also, the lfcShrink function also takes a long time.

So my questions are:

Is it the expected running time to run DESeq2 on a 58000 Genes * 10000 Samples expression matrix?

Is there any way that I can reduce the time needed for running DEseq2 on my dataset?

(I have tried to use only protein-coding genes(~20 000 genes), It still takes a long time, about 12h)

...I really appreciate any suggestion and many thanks in advance!

Best,

Ruifeng

You can use glmGamPoi here. See the vignette section on how to run it from DESeq().