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Partial least squares-discriminant analysis

SG Stephen A Goutman
JB Jonathan Boss
KG Kai Guo
FA Fadhl M Alakwaa
AP Adam Patterson
SK Sehee Kim
MS Masha Georges Savelieff
JH Junguk Hur
EF Eva L Feldman
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Partial least squares-discriminant analysis (PLS-DA) identifies metabolites that carry the greatest group-separating information, as represented by the first latent variable, which we performed using the R package mixOmics.18 Score plots illustrate differences between case versus control groups. The variable importance in projection (VIP) score of each metabolite, a weighted sum of the squared correlations between PLS-DA components and metabolites,19 contributed significantly to the separation of case versus controls for VIP >1.20

Copyright and License information:  ©2020-09-14 Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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