A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.

ABSTRACT: Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sa

SUBMITTER: Lai EY 

PROVIDER: S-EPMC5493430 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature