Differentially expressed genes analysis Limma microarray

Hi.

I have a question. I am working with a microarray with the next samples:

control_rep_1
control_rep_2
virusA_rep_1
virusA_rep_2
virusA_rep_3
virusB_rep_1
virusB_rep_2
virusB_rep_3
virusA_virusB_rep_1
virusA_virusB_rep_2
virusA_virusB_rep_3

I have to extract the differentially expressed genes:

control vs virusA
control vs virusB
control vs virusA_virus_B
virusA vs virusB

I have to do so using Limma. In the case of the virusA, I am using the next design before to run the lmFit function:

virusA.design <- data.frame(REF = 1 , ctrl_vs_virusA = c(0,0,1,1,1,-1,-1,-1,-1,-1,-1))

virusA <- lmFit(normalised_data, virusA.design)

virusA <- eBayes(virusA)

But the adjusted p-values make no sense. I guess I am doing something wrong.

Do you have an idea about how should I design the different comparisons?

Thanks in advance,

Juan

limma r microarrays

• 1,739 views • link

updated 6.7 years ago by h.mon

• written 6.7 years ago by judhenaosa •

1 answer

That's a very confusing way of going about things and I expect some of the resulting coefficients will be twice what they should. Please make a design as follows:

metadata = data.frame(groups=c("control", "control", rep("virusA", 3), rep("virusB", 3), rep("virusAB", 3)))
design = model.matrix(~groups, metadata)

That will be much simpler.

• 0 views • link

written 6.7 years ago by Devon Ryan

Thanks for your help.

My next question is about the lmFit function. I mean, I want to see the specific effect of the control samples over the treatment samples and the effect of the virusA vs virusB, and extract the differentially expressed genes in each individual case. However, I do not know how to do specify this in the script.

Thanks in advance,

Juan

• 0 views • link

written 6.7 years ago by judhenaosa •

Please have a look at the limma vignette, there are examples of using both contrasts and extracting coefficients.

• 0 views • link

written 6.7 years ago by Devon Ryan