Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

No significant event #419

Open
Elle-hu opened this issue Jan 25, 2021 · 1 comment
Open

No significant event #419

Elle-hu opened this issue Jan 25, 2021 · 1 comment

Comments

@Elle-hu
Copy link

Elle-hu commented Jan 25, 2021

Dear Nuno,

I run psichomics on data that I have previously analyzed with VAST-TOOLS and SUPPA. Both these methods found significant splicing events in my data, several of which I validated by RT-PCR. To merge the results obtained with these tools I considered to use psichomics for its ability to merge annotation from difference sources. However the diffAnalyses command give me a list of events that, although show different inclusion, do not result significant according to the Wilcoxon p-value (BH adjusted) < 0.01 parameter. Also the other statistics included don´t seem to report any event as significant. How is that possible? Do you have any suggestion on how to proceed? I compared 2 conditions with 3 biological replicate each.

Thanks a lot!

@nuno-agostinho
Copy link
Owner

nuno-agostinho commented Feb 2, 2021

Hello, @Lilly88!

As PSI values do not follow a normal distribution, we recommend using non-parametric tests (i.e. statistical tests that don't assume any data distribution), such as a Wilcoxon test. Although non-parametric tests allow to analyse any kind of data regardless of their distribution, comparing a really small number of samples (such as 3 against 3) may not return statistically significant differences compared to tests that assume normally-distributed data.

For psichomics, we are working on a method to identify differently spliced events assuming PSI values follow a beta distribution and we have been applying it for small sample sizes. This will be available in a future update.

In the meantime, you can try to:

  1. Relax the threshold for differently spliced events depending on your statistical results (e.g. consider an adjusted p-value < 0.05)
  2. Manually transform your PSI values so they get closer to a normal distribution (e.g. via logit-transformation, but look out for PSI values of 0 and 1!) and then apply a t-test. I think this might be as easy as doing either of the following methods:
# Method 1: scale values between 0 and 1 to 0.0001 and 0.9999, logit-transform and apply t-test
psiLogit <- car::logit(psi, adjust=1E-4)
diffAnalyses(psiLogit, groups, method="ttest")
# Method 2: discard PSI values of 0 or 1, logit-transform and apply t-test
psiWithout0or1 <- psi
psiWithout0or1[psi == 1 | psi == 0] <- NA # Discard PSI values of 0 and 1
psiLogit <- car::logit(psiWithout0or1)
diffAnalyses(psiLogit, groups, method="ttest")

If you have any doubts, feel free to contact me. Cheers! :)

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants