Hi,
I am trying to use glmnet to perform Cox analysis. I have 150 samples and for 30k genes I have gene counts.
These are the two questions I have.
How would I go about normalizing the gene counts to fit into the assumptions of
glmnet?How should I go about selecting a smaller subset of genes?
For point 1, I am thinking I can use vst (from DESeq2) or maybe voom (limma) with calcNormFactors (edgeR) but I am stuck wondering about the standardize = TRUE option from glmnet.
For point 2, I was using rowVars from DESeq2 after I had normalized my data with either vst or voom with calcNormFactors.
And so currently, I am stuck deciding what to do for points 1 and 2. Any help would be appreciated.
0 answers
No answers yet.
Log in to answer this question.
-
Heatmap with different comparisons
written by san96 •
-
DESeq2 Normalization and Pre-Filtering
written by turcoa1 •
-
Hugely different results between edgeR and DESeq2
written by Gama313
-
is normalize RNA seq raw counts used correctly?
written by kng •
-
heatmaps for limma-voom DE genes
written by AlexStar •
-
RNA Seq data with GSEA
written by pennakiza
-
glmnet and RNA-seq data
written by Anastaziel •
-
cutoff for expressed genes in DESeq2
written by olechnwin •
-
Problem about normalization of tcga rna-seq rsem raw counts for unbiased feature selection in regul…
written by panagiotis.mokos •
-
Variance versus coefficient of variation for selecting most variably expressed genes after variance…
written by bgrande •
Hi, can you please checkout these two bioconductor forum posts to see if they answer your question? Both answers are from experts in the field and both are authors of edgeR/DEseq2: https://support.bioconductor.org/p/93732/ https://support.bioconductor.org/p/93160/
I think combined they will help you figure out the right way forward.