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I hope this message finds you well. My team and I are avid users of SUPPA2 for splicing analysis, and we are currently working with larger sets of samples. Our goal is to compare PSI values produced by the psiPerEvent function across a substantial number of samples to calculate scores that reflect the variation likelihood for splicing events among these samples, ideally resulting in a comprehensive numerical matrix.
Is there any functionality within SUPPA2 that can assist in comparing the sample PSI values from psiPerEvent across multiple samples to derive a score, hence facilitating the creation of a large-sample numerical matrix?
If such an option is not currently available, could you advise on a potential approach or suggest any complementary tools that could accommodate our needs for inter-sample comparison of PSI values?
Your guidance in this matter would be invaluable to our research and any further understanding of variable splicing probabilities across different loci among numerous samples.
Thank you very much for your time and support.
Best regards,
Xi Xu
The text was updated successfully, but these errors were encountered:
Dear Xi Xu,
Thanks a lot for your email.
If you have two groups, SUPPA provides a function to calculate the
differential splicing between the two groups.
It looks at each event PSI values across the two groups of samples and
perform a statistical test.
If you have multiple groups, you can combine all PSI values into one matrix
and perform clustering with the cluster functions in SUPPA.
If the groups are not known, you could select the events that have the
largest variation across samples and use those to perform some unsupervised
clustering.
In the wiki pages there are some practical examples that might help:
https://github.com/comprna/SUPPA/wiki/SUPPA2-tutorial
I hope this helps
Thanks for using SUPPA
Eduardo
On Thu, 8 Feb 2024 at 03:18, XXuxi ***@***.***> wrote:
Dear SUPPA2 Development Team,
I hope this message finds you well. My team and I are avid users of SUPPA2
for splicing analysis, and we are currently working with larger sets of
samples. Our goal is to compare PSI values produced by the psiPerEvent
function across a substantial number of samples to calculate scores that
reflect the variation likelihood for splicing events among these samples,
ideally resulting in a comprehensive numerical matrix.
Is there any functionality within SUPPA2 that can assist in comparing the
sample PSI values from psiPerEvent across multiple samples to derive a
score, hence facilitating the creation of a large-sample numerical matrix?
If such an option is not currently available, could you advise on a
potential approach or suggest any complementary tools that could
accommodate our needs for inter-sample comparison of PSI values?
Your guidance in this matter would be invaluable to our research and any
further understanding of variable splicing probabilities across different
loci among numerous samples.
Thank you very much for your time and support.
Best regards,
Xi Xu
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Dear SUPPA2 Development Team,
I hope this message finds you well. My team and I are avid users of SUPPA2 for splicing analysis, and we are currently working with larger sets of samples. Our goal is to compare PSI values produced by the psiPerEvent function across a substantial number of samples to calculate scores that reflect the variation likelihood for splicing events among these samples, ideally resulting in a comprehensive numerical matrix.
Is there any functionality within SUPPA2 that can assist in comparing the sample PSI values from psiPerEvent across multiple samples to derive a score, hence facilitating the creation of a large-sample numerical matrix?
If such an option is not currently available, could you advise on a potential approach or suggest any complementary tools that could accommodate our needs for inter-sample comparison of PSI values?
Your guidance in this matter would be invaluable to our research and any further understanding of variable splicing probabilities across different loci among numerous samples.
Thank you very much for your time and support.
Best regards,
Xi Xu
The text was updated successfully, but these errors were encountered: