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Normalization for RNAseq data

The JMP Genomics has a few normalization methods for RNAseq data, including KDMM, RPM scaling, TMM, TPM and upper quartile scaling. The JMP Pro 17 is missing such important tools.

The purpose of normalization methods for RNAseq or other large scale data, such as metabolomics, is to reduce systematic experimental bias and technical variation. Manuscript can be rejected by journal because the data was not normalized. The most commonly used normalization methods for RNAseq are “Reads Per Kilobase of transcripts per Million mapped reads” (RPKM) (Mortazavi et al., 2008; doi: 10.1038/nmeth.1226) and Trimmed Mean of M-values (TMM) (Robinson and Oshlack; doi: 10.1186/gb-2010-11-3-r25).

My wish is to include normalization methods into JMP Pro

 

 

1 Comment
SamGardner
Level VII
Status changed to: Investigating

@Marina_N thank you for the input.  We are looking into adding some additional normalization tools for genomic data in the future.