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2 hours ago by

Hi Folks,

I am currently working on 16 bulk RNAseq samples. I did some basic research on statistical testing of differential expression and figured out two R packages to edgeR and DESeq2.

The 16 samples are actually paired samples data from before treatment and after treatment from 8 patients. I am interested in finding DE genes within paired samples for instance (P1_AT vs P1_BT). This is the design matrix

Sample   Patient    Time
P1_BT       P1       BT
P1_AT       P1       AT
P2_BT       P2       BT
P2_AT       P2       AT
P8_BT       P8       BT
P8_AT       P8       AT

So I have created my design matrix like section 4.1
- design <- model.matrix(~ Patient+Treatment)

EdgeR manual, my experiment design is exactly similar to section 4.1 of edgeR manual.

4.1 RNA-Seq of oral carcinomas vs matched normaltissueScreen-Shot-2020-05-10-at-12-50-58-AM

But I am interested in getting 1) P1_AT vs P1_BT, 2) P2_AT vs P2_BT ... 8) P8_AT vs P8_BT

To find genes with baseline differences between the drug and the placebo at 0 hours:
qlf <- glmQLFTest(fit, contrast=my.contrasts[,"DrugvsPlacebo.0h"])

 my.contrasts <- makeContrasts(
+         Drug.1vs0 = Drug.1h-Drug.0h,
+         Drug.2vs0 = Drug.2h-Drug.0h,
+         Placebo.1vs0 = Placebo.1h-Placebo.0h,
+         Placebo.2vs0 = Placebo.2h-Placebo.0h,
+         DrugvsPlacebo.0h = Drug.0h-Placebo.0h,
+         DrugvsPlacebo.1h = (Drug.1h-Drug.0h)-(Placebo.1h-Placebo.0h),
+         DrugvsPlacebo.2h = (Drug.2h-Drug.0h)-(Placebo.2h-Placebo.0h),
+ levels=design)

3.3.1 Defining each treatment combination as a group

The design matrix is different for section 4.1 and section 3.3.1. My experiment similar to section 4.1, so I have used that design matrix. But I want the results of section 3.3.1 within samples comparison.

Any comments are appreciated.

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