Benchmarking DESeq / EdgeR / LIMMA vs Lasso vs RF
Hi!
I'm wondering if anyone is aware of any benchmarking between approaches traditionally used in RNAseq or microarray analysis (e.g. DESeq2 / EdgeR / LIMMA) against others such as Random Forest or Lasso regression to identify genes / transcripts that are differentially expressed between conditions?
I'm aware that there may be different assumptions about data distributions, but still I'm curious about this. I tried searching for this but cannot find anything...
Thanks in advance
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Is a direct comparison of the number of up- and down-regulated genes meaningful?
written by biock •
-
RNAseq meta-analysis to identify “consistently expressed” genes
written by cgibbsm •
-
LASSO regression for gene selection and clinical variables
written by fabian •
-
Using Pseudobulk Approach for Identifying Marker Genes Within a Single Condition
written by nhaus •
-
Alternatives to TMM normalization
written by Jautis
-
Analysis of the gene and protein expression datasets
written by Vinesh •
-
RNA-SEQ count data as input for Machine Learning
written by Alberto •
-
Functional analysis of RNASeq differentially expressed genes
written by Colari19
-
RNA seq differential expression clustering tools
written by guillaume.rbt
-
Meta-analysis to identify potential receptors that could facilitate virus invasion.
written by siddharth.avadhanam •
To my knowledge, this has never been tested.
Dedicated tools to analyze expression data are designed to accommodate specific difficulties, such as the high number of dimension and the usually (very) low number of replicates. I expect regression of any kind to perform poorly in comparison, because of those difficulties (try to fit a line when you have only 2 or 3 replicates). Not sure how a random forest would be useful in that context.