Preprocessing for RNASeq data for CMSclassifier
I am interested in comparing the Consensus Molecular Subtypes (CMS) labels from the random forest and single sample predictor methods from the package CMSclassifier. I have RNASeq data as raw counts as outputed by HTSeq. The CMSclassfier::classifyCMS.RF requires as input "log2_scaled Gene Expression Profiles (GEP) data values". Is it sufficient to log2 the raw counts from HTSeq or would it be more appropriate to also quantile-normalize the log2 values as in the CMScaller package? Or is there a more suitable normalization that you would recommend in these settings?
Thanks!
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Does gene raw read count mean and dispersion preserve batch effects if I used raw counts from diffe…
written by aldas02027 •
-
How to prepare the data prior to Quantile Normalization for comparisons across studies/platforms ?
written by BioNovice247 •
-
Differential expression analysis (DEA) of lncRNA
written by bart •
-
Filtering lowly expressed genes
written by Barista •
-
How to get transcript level estimates in TPM from TCGA BRCA
written by StartR •
-
Question regarding TCGA and TCGAbiolinks --> RNAseq data as HTseq-Counts, FPKM, FPKM-UQ
written by mario.red8976
-
Generating read counts for my microRNA reads for a non model invertebrate
written by karthic
-
TCGA expression data (TPM)
written by Folder40g
-
precursor miRNAs from TCGA
written by Kostas •
-
Discrepancy in DESeq2 fold change
written by sophie.ehresmann •
I would go for either
rlogorvsttransformation, as recommended for downstream analysis in the DESeq2 manual for classification/clustering and machine learning applications.