I am analysing the following experiment: two cell lines representing the same disease subtype were treated with a drug or DMSO (control) and the samples were collected in 4 time points: 0.5, 1, 6, and 16 h. Two types of sequencing were performed for each sample: Ribo-Seq to measure translatome and RNA-Seq to measure transcriptome. If you prefer a table view, see below for the full experiment table.
Now, I would like to test for any differences between drug and DMSO over multiple time points, that is specific to Ribo-Seq (occurs mainly at the translation level).
According to the DESeq2 manual for RNA-Seq data analysis the simplest testing for any differences over multiple time points looks like:
dds <- DESeqDataSetFromMatrix(counts, ~ condition + time + condition:time) dds <- DESeq(dds, test = "LRT", reduced = ~ condition + time
After reading many posts at this forum regarding the DESeq2 experiment design I assume that the design matrix that takes into account two types of sequencing and pairwise comparisons within the same cell line should be:
dds <- DESeqDataSetFromMatrix(counts, ~ experiment + cell_line + condition + time + experiment:condition + condition:time) dds <- DESeq(dds, test = "LRT", reduced = ~ experiment + condition + time + experiment:condition
Is it a correct reasoning?
Tabular view of the experiment:
Experiment Cell_line Condition Time (h) _______________________________________ Ribo_seq A Drug 0.5 Ribo_seq A Drug 1 Ribo_seq A Drug 6 Ribo_seq A Drug 16 Ribo_seq B Drug 0.5 Ribo_seq B Drug 1 Ribo_seq B Drug 6 Ribo_seq B Drug 16 Ribo_seq A Control 0.5 Ribo_seq A Control 1 Ribo_seq A Control 6 Ribo_seq A Control 16 Ribo_seq B Control 0.5 Ribo_seq B Control 1 Ribo_seq B Control 6 Ribo_seq B Control 16 RNA_seq A Drug 0.5 RNA_seq A Drug 1 RNA_seq A Drug 6 RNA_seq A Drug 16 RNA_seq B Drug 0.5 RNA_seq B Drug 1 RNA_seq B Drug 6 RNA_seq B Drug 16 RNA_seq A Control 0.5 RNA_seq A Control 1 RNA_seq A Control 6 RNA_seq A Control 16 RNA_seq B Control 0.5 RNA_seq B Control 1 RNA_seq B Control 6 RNA_seq B Control 16