I am looking to do a time-course analysis of my RNA-seq human, whole blood datasets. I have 30 samples, across 4 time-points. I have no replicates.
T0 - Week 0, initial treatment T1 - Week 2, two weeks into treatment T2 - Week 12, twelve weeks into treatment T3 - Week 26, end of treatment; “Cure”
I have done the pairwise comparisons, comparing each time-point to T3, our cure state, using Deseq2.
Now I want to track the changes of gene expression over the full treatment time.
I have read the time-course Vinaigrette on Deseq2 but I am not sure I am understanding it right.
I have assigned two variables, timepoint and SampleID to account for the different times and samples.
(timepoint <- factor(c(rep("0",30),rep("2",30)rep("12",30)rep("26",30)))) (coldata <- data.frame(row.names=colnames(countdata), timepoint, SampleID)) ddsMat <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~SampleID + timepoint) dds <- DESeq(ddsMat) res <- results(dds)
However I don’t know if this is enough. I can’t see where the time element comes into play. Any advice on doing a Time- course analysis?