DEseq2 to test half-life for time-series data
Entering edit mode
Last seen 6 days ago
United States


I have time-series data (in order to measure the half-life of RNA degradation) under different conditions. To test any genes react in a condition-specific manner over time, I could test the interaction term with a LRT test, e.g. ~ condition + time + condition:time as the full model and ~ condition + time as the reduced model), similar to the example stated here:

Alternatively, I could also calculate the half-life of each gene based on their time-series normalized expression values (like the HalfLivesBlock() in DRUID code) and then model the half-life (instead of gene expression) simply as half_life ~ condition.

My question is, if I would take the second method, I would need to use the normalized expression values (e.g. TPM), not raw reads, to calculate half-life. After that, can I still be able to use DEseq2 to model the differential half-life? I doubt it, since half-life may not fit the negative binomial distribution like raw reads count. DEseq2 takes raw count because it will internally model its dispersion (mean ~ deviation relationship) and model it in NB distribution. If I convert the time-series expression to a new variable (e.g. half-life, progression rate etc.), is my new data still eligible for DEseq analysis?

Please advise. Thanks

half-life DESeq2 time-series • 87 views
Entering edit mode
Last seen 13 hours ago
United States

From the DESeq2 side, our outcome is gene expression, so non-negative count values.

I would say, if you want to put a different value in the LHS of the equation, like half-life, use a GLM instead.


Login before adding your answer.

Traffic: 550 users visited in the last hour
Help About
Access RSS

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6