Combined with mRNA-Seq data, Ribo-Seq or polyRibo-Seq experiments can be used to study changes in translational efficiencies of genes and/or transcripts for different treatments. For test purposes the following generates a small test data set from the sample data used in this vignette, where two types of RNA samples (assays) are considered: polyribosomal mRNA (Ribo) and total mRNA (mRNA). In addition, there are two treatments (conditions): M1 and A1.

library(DESeq2)
countDFeBygpath <- system.file("extdata", "countDFeByg.xls", 
    package = "systemPipeR")
countDFeByg <- read.delim(countDFeBygpath, row.names = 1)
coldata <- DataFrame(assay = factor(rep(c("Ribo", "mRNA"), each = 4)), 
    condition = factor(rep(as.character(targets.as.df(targets(args))$Factor[1:4]), 
        2)), row.names = as.character(targets.as.df(targets(args))$SampleName)[1:8])
coldata

Differences in translational efficiencies can be calculated by ratios of ratios for the two conditions:

The latter can be modeled with the DESeq2 package using the design $\sim assay + condition + assay:condition$, where the interaction term $assay:condition$ represents the ratio of ratios. Using the likelihood ratio test of DESeq2, which removes the interaction term in the reduced model, one can test whether the translational efficiency (ribosome loading) is different in condition A1 than in M1.

dds <- DESeq2::DESeqDataSetFromMatrix(countData = as.matrix(countDFeByg[, 
    rownames(coldata)]), colData = coldata, design = ~assay + 
    condition + assay:condition)
# model.matrix(~ assay + condition + assay:condition,
# coldata) # Corresponding design matrix
dds <- DESeq2::DESeq(dds, test = "LRT", reduced = ~assay + condition)
res <- DESeq2::results(dds)
head(res[order(res$padj), ], 4)
# write.table(res, file='transleff.xls', quote=FALSE,
# col.names = NA, sep='\t')



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