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hi, I am working on a GEO microarray dataset, 5 groups were defined and assigned to the Expressionset.

Beform vooma I used exprs(gset) <- normalizeBetweenArrays(exprs(gset)) after log2 transformation,

and used gset <- gset[complete.Cases(exprs(gset)), ] to remove missing value, as suggested in the manual.

so when I run vooma, I see this mean-variance trend,

I understand this graph does not reflect data quality, but I wonder if

a) is vooma appropriate for weighted distribution? or should I use voomaByGroup instead?

b) is there any more data filtering process I need to add beform vooma and lmFit in this case?

or in other words, how can I ensure that my data is properly processed?

Many thanks! Lily C

Microarray data processing and filtering is very much platform-specific and it is hard to give any advice without knowing the details of the dataset you are working on.

In my opinion, microarray data should never contain missing values when properly processed. So, if I were you, I would be revisiting the preprocessing of the GEO dataset.

In general, vooma has the much the same requirements as any limma microarray analysis and you would just do the same filtering and normalization as usual. I don't understand what you mean by a "weighted distribution" or why you would need voomaByGroup.

Added Later

I was a bit puzzled by your question because you say you're following "the manual" but actually the approach and code you show is not recommended anywhere in the limma documentation or User's Guide. After some Google searching, it seems to me that you are following code from this paper:

Islam, S., Kitagawa, T., Baron, B., Abiko, Y., Chiba, I., & Kuramitsu, Y. (2021). ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis. Scientific Reports, 11(1), 10563. https://www.nature.com/articles/s41598-021-90077-x

This paper isn't official limma documentation. Usually we recommend a limma-trend approach for microarray data rather than the vooma approach in this paper, not because vooma is wrong but just because limma-trend achieves the same end without requiring weights.