I am running time-series analysis and was able to create design and analysis successfully. However, I have few doubts in using transformed matrix
ddsObj = DESeqDataSetFromTximport(txi = count_data, colData = sample_df, design = ~TIME) # remove too low counts <1 keep <- rowSums(counts(ddsObj)) > 1 ddsObj <- ddsObj[keep,] # run DESeq analysis with reduced model with time point 1 as reference ddsTC <- DESeqDataSet(ddsObj, ~TIME) ddsTC <- DESeq(ddsTC, test = "LRT", reduced = ~1) resTC <- results(ddsTC) resTC$symbol <- mcols(ddsTC)$symbol head(resTC[order(resTC$padj),], 4)
I wish to know, how to use a transformed matrix? For instance I performed VST and unable to use it as deseq object.
vsd = varianceStabilizingTransformation(ddsObj) boxplot(assay(vsd)) plotPCA(vst, intgroup=c('Temperature','TIME')) ddsTC <- DESeqDataSet(vsd, ~TIME) #renaming the first element in assays to 'counts' #Error in DESeqDataSet(vst, ~TIME) : some values in assay are not integers
R version 4.0.1 (2020-06-06) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Catalina 10.15.5