Calculating p-value and adjusted p-value for pre-normalized microarray data with fold change precalculated

Question

[Shero]

I have a microarray dataset with two mutants dataset that has already been normalised, and the fold change values for each gene in each mutant versus the wild type have been calculated. I'm interested in determining the statistical significance of these fold changes.

What I've tried: I initially considered using limma, but I wasn't sure if it was appropriate for pre-calculated fold change values.

What I was expecting: I was hoping to obtain a list of genes with their corresponding p-values and adjusted p-values, indicating their significance. Ideally, this would help me identify genes that are significantly upregulated or downregulated in the mutant compared to the wild type.

Given that I only have the fold change values for two biological replicates (and no raw intensity or count data), what would be the best approach to achieve this? Any guidance, recommended tools, or R packages would be greatly appreciated!

Thank you in advance!

Answer 1

[LChart]

You shouldn't really separate fold change and p-value calculations. Use limma, and add the "pre-calculated" fold-change as an extra column to the result table. It had better be that the signs are consistent between the limma [log] fold-change and the pre-calculated fold change. In such cases you can use the limma p-value and report both FC calculations.

[Shero]

I dont have the raw data. the only thing i have is the normalised ratio compared to the wild type

[LChart]

Then there is just no way you can get a p-value.

[Shero]

but i want to say that i have the value of two independant samples (relative to wild type) even this way i cant? ?