Open AccessGene-set integrative analysis of multi-omics data using tensor-based association test
Author(s) -
Sheng-Mao Chang,
Meng Yang,
Wenbin Lu,
YuJyun Huang,
Yueyang Huang,
Hung Hung,
Jeffrey C. Miecznikowski,
TzuPin Lu,
Jung-Ying Tzeng
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab125
Subject(s) - omics , inference , computer science , data mining , set (abstract data type) , software , data set , computational biology , bioinformatics , artificial intelligence , biology , programming language
Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference.