Open AccessAdaTiSS: a novel data-Adaptive robust method for identifying Tissue Specificity Scores
Author(s) -
Meng Wang,
Lihua Jiang,
M Snyder
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/btab460
Subject(s) - outlier , population , computer science , data mining , expression (computer science) , computational biology , artificial intelligence , biology , demography , sociology , programming language
Accurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level. The Genotype-Tissue Expression project is one of the publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons and heterogeneous tissue expression make it challenging to accurately identify tissue specific gene expression. How to distinguish the inlier expression from the outlier expression becomes important to build the population level information and further quantify the TS. There still lacks a robust and data-adaptive TS method taking into account heterogeneities of the data.