Performs statistical analysis for differential expression of seqData objects, using methods from one of: edgeR, DESeq2, or limma-voom
diffexp_seq(
omicsData,
method = "edgeR",
p_adjust = "BH",
comparisons = NULL,
p_cutoff = 0.05,
...
)object of type 'seqData' created by
as.seqData
character string of length one specifying which wrapper to use. Can be 'edgeR', 'DESeq2', or 'voom'
character string for p-value correction method, refer to ?p.adjust() for valid options. Defaults to "BH" (Benjamini & Hochberg).
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.
numeric value between 0 and 1 for setting p-value significance threshold
additional arguments passed to methods functions. Note, formatting option changes will interfere with wrapping functionality.
object of class statRes
Runs default differential expression workflows.
Flags (signatures) - Indicator of statistical significance. Zeroes indicate no significance, while +/- 1 indicates direction of significance.
Method "edgeR" - Runs default edgeR workflow with empirical Bayes quasi-likelihood F-tests. Additional arguments can be passed for use in the function. Refer to calcNormFactors() and glmQLFit() in edgeR package. Requires the 'edgeR' and 'limma' packages to run.
Method "DESeq2" - 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.
Method "voom" - Runs default limma-voom workflow using empirical Bayes moderated t-statistics. Additional arguments can be passed for use in the function. Refer to calcNormFactors() in edgeR package. Requires the 'edgeR' and 'limma' packages to run.
Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140. doi: 10.1093/bioinformatics/btp616.
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)
Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.
# \donttest{
library(pmartRdata)
myseqData <- group_designation(omicsData = rnaseq_object, main_effects = "Virus")
edger_results <- diffexp_seq(omicsData = myseqData, method = "edgeR")
deseq_results <- diffexp_seq(omicsData = myseqData, method = "DESeq2")
voom_results <- diffexp_seq(omicsData = myseqData, method = "voom")
# }