I am looking for details on the principle and recommended workflow using the
catchKallisto functions for differential tx-level analysis with
edgeR. The manual mentions but does not offer any details on the functions.
help() mentions that a per-transcript overdispersion value is estimated which is then used to divide the transcript counts by.
Is that all one has to do, followed by the standard (e.g. QLF) workflow?
Is there any benchmarking or performance data available on how the method compares to other approaches such as