[BioC] MLSeq Mathematical Concepts

Dario Strbenac dstr7320 at uni.sydney.edu.au
Wed Apr 23 07:00:14 CEST 2014


>From reading the vignette, MLSeq seems to be a set of wrapper functions that allows the user easy access to normalisation strategies in edgeR or DEseq and passes the data onto algorithms such as Support Vector Machine or Random Forest. Are there any results that demonstrate that normalisation improves classification performance ? I am also not convinced about the description of using voom weights to transform the data. The author of voom stated that specialised clustering and classification algorithms are needed to handle the CPM and weights separately. Why does MLSeq use standard classification algorithms and how were the weights and expression values combined ?

Dario Strbenac
PhD Student
University of Sydney
Camperdown NSW 2050

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