Tensorial blind source separation for improved analysis of multi-omic data

HIGHLIGHTS

• who: Andrew Teschendorff from the (UNIVERSITY) have published the article: Tensorial blind source separation for improved analysis of multi-omic data, in the Journal: (JOURNAL)
• what: The authors did not consider the corresponding performance measures for the individual variation (i.e. the variation specific to one data type), because not all algorithms infer sources of individual variation (e_g CCA), thus precluding direct comparison between them, and because identifying sources of joint variation is always the main purpose of multi-way algorithms. To illustrate how the output produced by tICA can be used for valuable inference . . .
  

   

  

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