Question: How to use limma for a categorial outcome variable? Where outcome is the cohort status and predictor is gene expression.
gravatar for halo22
14 months ago by
Indianapolis, IN
halo22140 wrote:

Hello All,

I am very new to limma and trying to build a logistic regression like model using limma where my goal is to understand associations between the cohort status and gene expression while accounting for variables like age and sex. My gene expression is quantified using RSEM with TPM counts. I have normalized my data using TMM normalization. The following is my code snippet for limma.

design <- model.matrix(~0+Cohort+sex+age, data=PhenoType)   #Cohort:1/0
v <- voomWithQualityWeights(myNormalized_data, design=design, normalization="none", plot=TRUE)
fit1 <- lmFit(v,design)
fit2 <- eBayes(fit1)

Is this design correct? Again, I am not interested in group comparison but predicting cohort status using expression values.

rna-seq limma rna next-gen • 539 views
ADD COMMENTlink modified 8 months ago by Biostar ♦♦ 20 • written 14 months ago by halo22140
gravatar for h.mon
14 months ago by
h.mon29k wrote:

Do not use TPM values, use raw counts.

Differential expression analysis starting from TPM data

ADD COMMENTlink written 14 months ago by h.mon29k

Following from h.mon's point, can you please be very clear about your data processing steps?

You can use the RSEM estimated counts with limma, edgeR, or DESeq2. Take a look at these answers on Bioconductor:

ADD REPLYlink written 14 months ago by Kevin Blighe60k
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