How to get differential gene expression within paired samples using EdgeR or DESeq2?

Patient <- factor(c(8,8,33,33,51,51)) Tissue <- factor(c("N","T","N","T","N","T")) data.frame(Sample=colnames(y),Patient,Tissue) Sample Patient Tissue 8N 8 N 8T 8 T 33N 33 N 33T 33 T 51N 51 N 51T 51 T design <- model.matrix(~Patient+Tissue) rownames(design) <- colnames(y) design (Intercept) Patient33 Patient51 TissueT 8N 1 0 0 0 8T 1 0 0 1 33N 1 1 0 0 33T 1 1 0 1 51N 1 0 1 0 51T 1 0 1 1 fit <- glmFit(y, design) lrt <- glmLRT(fit) topTags(lrt) Coefficient: TissueT RefSeqID Symbol NbrOfExons logFC logCPM LR PValue FDR 5737 NM_001039585 PTGFR 4 -5.18 4.74 98.7 2.97e-23 3.12e-19 5744 NM_002820 PTHLH 4 3.97 6.21 92.2 8.00e-22 4.21e-18 3479 NM_001111283 IGF1 5 -3.99 5.71 86.5 1.38e-20 4.85e-17 colnames(design)[1] "(Intercept)" "Patient33" "Patient51" "TissueT"

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)

6 months ago by

Kevin Blighe ♦ 67k

Republic of Ireland

Kevin Blighe ♦ 67k wrote:

I found it somewhat difficult to follow your question due to the blurred screenshots, and the fact that all of the parameters that you list in your design formula are not in the metadata that you show, e.g., Treatment. In fact, the different parts of your question seem somewhat disparate to each other... (?)

For all intents and purposes, if you want to do comparisons like P1_AT vs P1_BT, then simply include Sample in your design formula. However, in this case, it looks like you only have 1 replicate per group?

ADD COMMENT • link modified 6 months ago • written 6 months ago by Kevin Blighe ♦ 67k

Thanks, Kevin for helping me out. I have made some changes to the original post by replacing the screenshots and changing in the design matrix from "Time" to "Treatment".

I want to compare and find DE genes within paired samples like "P1_AT vs P1_BT", "P2_AT vs P2_BT" and so on?

In the above post, two sections from edgeR (section 3.3.1-Multifactor analysis and section 4.1-Paired).

From section4.1, I need some help in understanding the output. f

it <- glmFit(y, design)
lrt <- glmLRT(fit)
topTags(lrt)
Coefficient:  TissueT
RefSeqID   Symbol NbrOfExons logFC logCPM   LR   PValue      FDR
5737   NM_001039585    PTGFR          4 -5.18   4.74 98.7 2.97e-23 3.12e-19
5744      NM_002820    PTHLH          4  3.97   6.21 92.2 8.00e-22 4.21e-1

colnames(design)[1] "(Intercept)" "Patient33" "Patient51" "TissueT"

This is my understanding, TissueT refers to comparison of all tumor samples (8T, 33T, 51T) vs all normal samples (8N, 33N, 51N). Am I correct?

What about Patient33, does it mean Patient33 (N and T) vs Patient8 (N and T)?

What about Patient51, does it mean Patient51 (N and T) vs Patient8 (N and T)?

In order to compare, Patient51 N vs Patient51 T, what design and contrast should be selected?

ADD REPLY • link modified 6 months ago • written 6 months ago by learner.bioinformatics • 0

This is my understanding, TissueT refers to comparison of all tumor samples (8T, 33T, 51T) vs all normal samples (8N, 33N, 51N). Am I correct?

Yes, this is correct. However, to add, due to the fact that Patient is also included in the design formula (~ Patient + Tissue), the results that you derive for TissueT are controlled for the Tumor-Normal pairing, i.e., it is already a paired analysis.

What about Patient33, does it mean Patient33 (N and T) vs Patient8 (N and T)? What about Patient51, does it mean Patient51 (N and T) vs Patient8 (N and T)?

No, these do not have much useful meaning, in this situation. 'Patient33' would be: Patient33 (T+N) vs [Patient51 (T+N) & Patient8 (T+N)]

In order to compare, Patient51 N vs Patient51 T, what design and contrast should be selected?

You should not derive p-values from a 1 versus 1 comparison. It would be better to use TissueT, which will give you stats for Tumour versus Normal, while controlling for patient-specific effects.

ADD REPLY • link modified 6 months ago • written 6 months ago by Kevin Blighe ♦ 67k

Hi Kevin

I have a very similar situation and also interested in getting the below kind of comparison for my dataset.

In order to compare, Patient51 N vs Patient51 T, what design and contrast should be selected? You are correct that the p-value comes from a 1 versus 1 comparison should not be considered.

Took the content from edgeR manual,

I got the following colnames for this design_2 = model.matrix(~Subject+Treat)
- (Intercept)
- Subject2
- Subject3
- TreatT

    1) qlf <- glmQLFTest(fit), 
Coefficient : TreatT. Compare all 3 controls vs all 3 treatments
    2) qlf <- glmQLFTest(fit, coef=2), 
Coefficient:  Subject2, compare Subject 2(control + treatment) vs Subject 1(control + treatment)
    3) qlf <- glmQLFTest(fit, coef=3), 
Coefficient:  Subject3, compare Subject 3(control + treatment) vs Subject 1(control + treatment)

I want to compare Subject 1_C vs Subject 1_T, Subject 2_C vs Subject 2_T and Subject 3_C vs Subject 3_T. Therefore I created the new design.

setting dispersion to NA (bcos there are no replicates) but in the attached screenshot from section 3.4.1 (Paired Samples).

Both the control and the treatment are administered to each of three patients. They are comparing the treatment to the control for each patient separately.

How to compare them separately?

Subject 1_C vs Subject 1_T

Subject 2_C vs Subject 2_T

Subject 3_C vs Subject 3_T

What mistake I have done?

ADD REPLY • link modified 5 months ago • written 5 months ago by bioinforesearchquestions • 280

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