Direct statistical testing of PSI (Percent Spliced In)?
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4.0 years ago

Hello everyone. I was wondering if there is a direct way of statistical testing PSI (Percent Spliced In) values without the need of external software. Let's say I have the following PSI values obtained by De Novo alternative events prediction. For instance, I would like to compare if PSI values of events 1 [1,] and 2 [2,] of the four samples (2 controls [,1][,2] and two perturbated samples [,3][,4]) are statistically different.

    [,1]    [,2]    [,3]    [,4]
[1,]    0.12658228  0.06343907  0.1038961   0.11666667
[2,]    0.87341772  0.93656093  0.8961039   0.88333333
[3,]    0.9117984   0.92075472  0.94163424  0.91400491
[4,]    0.0882016   0.07924528  0.05836576  0.08599509
[5,]    0.01875378  0.02342787  0.02713178  0.02777778
[6,]    0.98124622  0.97657213  0.97286822  0.97222222

R packages like DEXSeq allow for differential splicing testing, but I am currently limited to only using the information I have (PSI or counts for each event) and not able to use any third programs.

Is there a way in which PSI can be statistically tested directly? Which statistical test would you recommend for the case? Given the ammount of events I believe multiple testing could be the best choice. But I would probably only test for one or two events in each experiment, which makes me believe a two group testing could be more than enough.

psi statistical-test test splicing AS • 1.1k views
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You're missing all the depth and exon length information to replicate what one of those callers would need, so you are pretty much going to have to do a crude series of t-tests or a negative binomial.

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That is very true, Jeremy, thank you for answering. In fact, I also have the event start and end coordinates and the read counts. And I had them already normalized to FPKMs.

So taking into account PSI to filter interesting events, technically... using their related FPKMs would suffice in a two group t-test?


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