This message has been cross posted to the following eGroups: Biopharmaceutical Section and Statistical Consulting Section .
-------------------------------------------
Hello All,
Summary: What is the current state of the art for analysis of RNA-seq data accounting for continuous covariates?
It has been some years since I last worked on the analysis experiments utilizing mRNA abundance data.
I'm currently working on the analysis of RNA-seq data from an experiment where the key question is the difference in the effect of two different drug treatments on abundance of RNA across the genome. IE, which genes have show a different effect of drug A (standard of care) and drug B (new treatment)?
There are six treatment groups
- untreated,
- dose A1 of drug A,
- dose A1 of drug A + dose B1 of drug B,
- dose A1 of drug A + dose B2 of drug B,
- dose A1 of drug A + dose B3 of drug B,
- dose A1 of drug A + dose B4 of drug B,
with doses B1 < B2 < B3 < B4.
Animals were assigned to treatment groups by an algorithm that attempts to balance across several covariates, so these covariates need to be included in the analysis.
I would like to model the effect based on exposure (via AUC) and to account for covariates, including both covariates used for treatment group assignment, and covariates that may be explain mRNA abundance.
The RNA-seq literature seems to be full of methods & tools for analyzing RNA-seq data (DEseq, SAMseq, etc.) but these appear to be limited to statistical models with very simple structure.
What is the current state of the art for analysis of RNA-seq data accounting for multiple covariates?
-Greg
-------------------------------------------
Gregory Warnes
Owner and Chief Scientist
Gregory R. Warnes Consulting
-------------------------------------------