I am using DESeq2 to perform LRT analysis of a multi-treatment experiment, like an ANOVA. I have 5 different conditions, each with 4 replicates, and would like to generate a single p-value that indicates difference among the conditions, but not specific to any two conditions (as in a t-test).
I have run it as I best understand from the other threads I have read and the manual, and I want to be sure I understand the output. The commands I entered and the outputs generated in R are pasted below.
I can't tell from the output if the p-value is for comparing all 5 conditions, or just 12dpd 0 vs 12dpd 4. When I run mcols(res)$description (see below) it says "LRT p-value: '~ group' vs '~ 1'" and "log2 fold change (MLE): group 12dpd 4 vs 12dpd 0". Does this mean I am only generating the p-value based on differences between the two groups 12dpd 4 and 12dpd 0? or all of the groups? (12dpd 0 is the first and 12dpd 4 is the last in the colData set-up).
R version 3.2.0 (2015-04-16) -- "Full of Ingredients"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin13.4.0 (64-bit)
> countData<- read.table("/Users/claireriggs/R/12dpd_annot_shared_exp_051415.txt", header=TRUE, row.names=1)
> colData<- read.table ("/Users/claireriggs/Desktop/mRNA/DESeq2/12dpd_sampleinfo.txt", header=TRUE, row.names=1)
note: colData looks like this:
> dds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design= ~trmt)
> design(dds) = ~ group
> dds = DESeq(dds, test = "LRT", reduced = ~ 1)
estimating size factors
gene-wise dispersion estimates
-- note: fitType='parametric', but the dispersion trend was not well captured by the
function: y = a/x + b, and a local regression fit was automatically substituted.
specify fitType='local' or 'mean' to avoid this message next time.
final dispersion estimates
fitting model and testing
> res <- results(dds)
out of 52619 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 24794, 47%
LFC < 0 (down) : 22871, 43%
outliers  : 149, 0.28%
low counts  : 0, 0%
(mean count < 0.2)
 see 'cooksCutoff' argument of ?results
 see 'independentFiltering' argument of ?results
 "mean of normalized counts for all samples" "log2 fold change (MLE): group 12dpd 4 vs 12dpd 0"
 "standard error: group 12dpd 4 vs 12dpd 0" "LRT statistic: '~ group' vs '~ 1'"
 "LRT p-value: '~ group' vs '~ 1'" "BH adjusted p-values"