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Potentialities of multivariate approaches in genome‐based cancer research: identification of candidate genes for new diagnostics by PLS discriminant analysis
Author(s) -
Musumarra G.,
Barresi V.,
Condorelli D. F.,
Fortuna C. G.,
Scirè S.
Publication year - 2004
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.846
Subject(s) - linear discriminant analysis , identification (biology) , computational biology , discriminant function analysis , selection (genetic algorithm) , partial least squares regression , gene , biology , genome , multivariate statistics , function (biology) , cancer , genetics , computer science , artificial intelligence , machine learning , botany
Partial least squares discriminant analysis (PLS‐DA) provides a sound statistical basis for the selection, from an original 9605‐data set, of a limited number of gene transcripts most effective in discriminating different tumour histotypes. The potentialities of the PLS‐DA approach are pointed out by its ability to identify genes which, according to current knowledge, are associated with cancer development. Moreover, PLS‐DA was able to identify MUC 13 and S100P proteins as candidates for the development of new colon cancer diagnostics. Various genes with unknown function and ESTs (expressed sequence tags), found to be important in discriminating genes for colon, leukaemia, renal and central nervous system tumour cells, are indicated as deserving high priority in future molecular studies. Copyright © 2004 John Wiley & Sons, Ltd.