Deleted:DEseq2 time-series design.
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Shilpi • 0
Last seen 19 days ago

Hi Michael,

I have some queries regarding differential expression using deseq. I have time-point data with 3 biological replicates and control with biological replicates.

I have generated the counts using htseq-count. I want to be able to compare my time points with control. My data is like following

sampleName fileName condition

Control1.count  Control1.count  Control

Control2.count  Control2.count  Control

Control3.count  Control3.count  Control

3hr_rep1.count   3hr_rep1.count     3 hr

3hr_rep2.count  3hr_rep2.count   3 hr

3hr_rep3.count   3hr_rep3.count     3 hr

6hr_rep1.count  6hr_rep1.count   6 hr

6hr_rep2.count   6hr_rep2.count  6 hr

6hr_rep3.count  6hr_rep3.count   6 hr

12hr_rep1.count  12hr_rep1.count     12 hr

12hr_rep2.count     12hr_rep2.count  12 hr

12hr_rep3.count  12hr_rep3.count     12 hr

samples <- read.table(file.path(directory, "control_sample"), header = TRUE)
dds <- DESeqDataSetFromHTSeqCount(sampleTable = samples, directory= directory, design= ~condition)
dds$condition<- relevel(dds$condition, "Control")
dds <- DESeq(dds)
res <- results(dds)

I have a few questions.

  • How to take care of biological replicates to identify differentially expressed genes between conditions.
  • How can I design dds so as to compare all my time points with respect to control. -Also, is it possible to a comparison between all sample at once.

I want to be able to produce a heatmap with each column representing data from three biological replicates if that makes sense. I am presently able to plot normalised read counts where each replicates is represented as distinct sample and not considering the replicates aspect.

I want help in designing the dds. Any suggestion is appreciated.

Many thanks in advance.


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