How to control age and gender effect in expression data using EdgeR
Hi, I have multiple rna-seq samples with patients who underwent shock vs controls (Variable of interest = VAR). A sample of how the data looks like is this:
- Patient Gender Age VAR
- 1 F 4 Shock
- 2 M 2 Control
- 3 M 1 Shock
- 4 F 6 Shock
- 5 F 3 Shock
- 6 F 2 Control
My current edgeR design is just:
design <- model.matrix(~VAR)
I wanted to remove/control for the effect of Gender and Age on my results. How would I write the design variable to do this? I am a beginner at this, and I have read the EdgeR manual but am still slightly confused!
I would be so grateful for any help!
-Jamie
• 2,931 views
•
link
updated
by
ATpoint
•
written
by
jamieoyang •
•
written
by
jamieoyang •
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Account for Clinical Variables in RNA-Seq Analysis
written by fabian •
-
How to regress out age and sex using limma removeBatchEffect
written by mropri
-
Additive Models in edgeR
written by bioinformatics2020
-
EdgeR and contrasts: How to compare with a group not in the model
written by 13530213 •
-
How to interpret DESeq2 result?
written by helen •
-
DESeq2 design formula with paired samples
written by helen •
-
Looking for a Viable Design Matrix for Differential Gene Expression Using Paired Sample
written by bdighera •
-
edgeR Factorial DE Design
written by hamindigoblue •
-
DESeq2 time series data and age and gender correction
written by Lalit •
-
how to control age and gender effect in expression data using DESeq2
written by Lalit •
Do you expect interactions between sex or age and VAR? Meaning do you want to subtract the effect of age or do you think age would change the response to shock?
The question is if also if it is worth bothering as n=2 for male and n=4 for female in human patients is a very small sample size given all the potential confounding effects in humans.
Hello, thank you for your reply! The n is much larger than the 6 people I have up there, that was just provided as a sample for the variables I have for my patients.
I would like to subtract the effect of age!
Before correcting for age and gender, a good idea would be to do a PCA plot and color your sample by age, gender and VAR if you see some variable that may have an impact on the sample grouping.
That is a good idea, I will definitely do that, thank you!
After that though, do you have any advice on how to write the design model?