Wrapper for DESeq2 workflow — DESeq2

For generating statistics for 'seqData' objects

DESeq2_wrapper(
  omicsData,
  test = "Wald",
  p_adjust = "BH",
  comparisons = NULL,
  p_cutoff = 0.05,
  ...
)

Arguments

omicsData: an object of type 'seqData', created by as.seqData
test: either "Wald" or "LRT", which will then use either Wald significance tests, or the likelihood ratio test on the difference in deviance between a full and reduced model formula
p_adjust: Character string for p-value correction method, refer to ?p.adjust() for valid options. Defaults to "BH" (Benjamini & Hochberg)
comparisons: `data.frame` with columns for "Control" and "Test" containing the different comparisons of interest. Comparisons will be made between the Test and the corresponding Control If left NULL, then all pairwise comparisons are executed.
p_cutoff: Numeric value between 0 and 1 for setting p-value significance threshold
...: additional arguments passed to function

Value

statRes object

Details

Runs default DESeq workflow. Defaults to Wald test, no independent filtering, and running in parallel. Additional arguments can be passed for use in the function, refer to DESeq() and results() in DESeq2 package. Requires 'survival' package to run.

Flags (signatures) - Indicator of statistical significance for computed test. Zeros indicate no significance, while +/- 1 indicates direction of significance.

References

Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)