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Old 06-14-2011, 06:11 PM   #1
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Default time series analysis of count data?

It hasn't been that long since Simon's post below in this thread: but is there anything new out there in terms of applying microarray time series analysis methods to count data? I'm very interested in collecting this type of time series discrete count data but am new to the statistical methods involved. Could count data be normalized somehow- e.g. by library size- and used directly in EDGE-type models for microarray expression data or is the error model just fundamentally different? I've used DESeq for pairwise comparisons but would like to find something that uses the power of time series data to detect significant differences between digital expression trajectories.

Originally Posted by Simon Anders View Post
DESeq allows you to perform pairwise comparisons, and, to my knowledge, the same is true for all other tools out there. So, you can pick pairs of time points and compare these. Using GLMs, you can also compare differences between pairs of time points for one drug with differences for another drug or for the untreated controls.

But which comparisons (contrasts) are useful to analyze data? Figuring this out is, in my opinion, the main challenge of time course data.

Of course, all these pairwise comparisons are a bit pedestrian, if you have more than a three or four time points. You might be more interested in curve fits, and this is a very different statistical task, with which I have little experience. I haven't seen yet any such analysis published for RNA-Seq data, but there is lots of paper on microarray time courses. Maybe the article by Hafemeister et al in the current issue of Bioinformatics is a good starting point. Translating such methods to the RNA-Seq settings is certainly something that needs to be done now.

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