8 days ago by
Republic of Ireland
Hey Marc, MetaboAnalyst is primarily for metabolomics data analyses, however, it does have companion tools, such as PLSDA, which is what these authors used. I looked at the methods in the paper and their input seems to have been Z-scores. They say:
Raw read counts were quantile-normalized, mean-centered, and divided
by the standard deviation of each variable.
Quantile normalisation you probably know. The second part, i.e. mean centering and dividing by the sdev, is a Z transformation.
Thus, with your RNA-seq data, I would normalise the raw counts with either of EdgeR, DESeq2, or limma/voom, transform the normalised counts to logged or variance-stabilised counts, and then Z-transform (in R, this can easily be done with the
Hopefully you are familiar with EdgeR, DESeq2, and / or limma/Voom.