limma - removing samples?
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RMP87 • 0
@rafael-24221
Last seen 17 days ago
Brazil

Hello!

I'm following the instructions shown in the limma user guide (17.4; page 113). The main objective of our project is to recover a list of DEG (from > 4 independents studies) and conduct a meta-analyzes (as a posterior step). However, some datasets have conditions that aren't the focus of the study (for example 300 samples = 100 A, 100 B, and 100 C; but our objective is to use only A and B conditions). In this case, we have to remove this kind of sample before the analyses (C), or can we load all the information (and conduct Background correction and normalize), but carrying the differential expression only on the samples related to the study?

Thank you very much!

ArrayExpress limma arrays • 129 views
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@gordon-smyth
Last seen 5 hours ago
WEHI, Melbourne, Australia

In general, it is best to analyse each independent dataset with all the samples as it was originally analysed. There is no need to restrict only to A and B samples nor is there any advantage in doing so. Including all the samples often has advantages in terms of normalization, batch correction or variance estimation.

Having said that, if the number of samples is large in each group (n=100) and there are no covariates or batches then analysing only the A and B conditions alone will also work well. So, in the end, it is up to you.

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Thank you very much for the explanation. Making it more precise. Could I analyze and normalize all samples from a dataset even though I had different tissues in the original study (e.g. skin - normal, skin - cancer, liver - normal, and liver - cancer) and work with only one of them (e.g. skin - normal, and skin - cancer)?

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