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Old 02-16-2019, 03:52 AM   #1
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Location: europe

Join Date: Feb 2019
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Default Pre-process. data reduction : statistical refinement with glm

Dear community,

My question is about statistics.

I have a complex experimental dataset (hundreds of samples) in which I want to test several factors and crossed factors. I know that before running any statistical test, it is very common to reduce the library sizes (which are about 2500 transcripts per sample) by applying a filtering step on the fold-change. However, fold-change is tricky in my context since it should be done between two conditions of a factor, but I want to test for multiple factors and their interaction (eg: y ~ factorA + factorB + factorA:factorB + factorC).

So, to cope with this, I was considering to filter the library sizes by performing what I call a "statistical refinement", i.e., performing a first statistical test with the complete GLM model and from this I would subset all transcripts having p<0.05 for at least one of the factors (not padj).

It has the effect of reducing my library size to approximately 800 transcripts, which is, I think reasonable to perform my second statistical trial. From this second statistical trial, I will then consider deferentially expressed genes to be statistically significant when padj<0.05 for the corresponding factors.

I have not found studies having processed the data in a similar manner so I would like to know what is your opinion about such method. In my opinion, filtering steps are essential but (i) fold-changes filters are not always relevant (a small fold change of a transcript with very small variability across conditions could have dramatic biological effects and so should not be disregarded), (ii) fold change filters are appropriate only within a simple design (ie, 1 factor with 2 levels, eg: exposed vs non exposed), (iii) otherwise statistical refinement may be a good option to consider.

Many thanks in advance for your constructive arguments and if you have the references of studies showing similar methods (I have not found yet).

Kind regards
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filter, refinement, statistics

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