Limma different sample size of groups and adjustment
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Entering edit mode
@gabin-pierlot-8307
Last seen 6.2 years ago
Switzerland

Dear BC list,

 

I am using limma the first time.

I have an unbalanced sample with 4 type of individuals, 

Healthy : 3 individuals

Treatment A : 10 individuals

Treatment B : 10 individuals

Treatment C : 10 individuals

I want to look at the differential gene expression for each type of individuals (using lmfit)

should I correct for the different sizes? How u handle this problem? And is there a special method to correct/adjust my sample for example smoking status or weight?

Kind regards

limma • 2.2k views
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3
Entering edit mode
Aaron Lun ★ 28k
@alun
Last seen 23 hours ago
The city by the bay

Different numbers of samples in each group do not pose a problem for limma. Just set up your design matrix like you normally would, e.g.:

> condition <- c(rep("Healthy", 3), rep(c("A", "B", "C"), each=10))
> condition <- factor(condition, levels=c("Healthy", "A", "B", "C"))
> design <- model.matrix(~condition)
> colnames(design)
[1] "(Intercept)" "conditionA"  "conditionB"  "conditionC"

The intercept here represents the average expression of the healthy samples, while each of the ensuing coefficients represents the log-fold change of expression for the corresponding treatment over that of the healthy samples. You can then use this design matrix in lmFit, as described in the user's guide. Contrasts between each treatment and the healthy samples can be performed by dropping the corresponding coefficient in topTable, while comparisons between treatments should use makeContrasts to compare the values of the relevant coefficients.

If you have additional predictor terms, you can put them in as factors or covariates in the model.matrix call. However, this may require some care, depending on what these terms are, whether they are confounded by the treatment conditions, etc. You'll have to provide some more details about your situation in order for us to give useful advice.

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