Inferring biological tasks using Pareto analysis of high-dimensional data

被引：0

作者：

Hart, Yuval ^{[1
]}

Sheftel, Hila ^{[1
]}

Hausser, Jean ^{[1
]}

Szekely, Pablo ^{[1
]}

Ben-Moshe, Noa Bossel ^{[2
]}

Korem, Yael ^{[1
]}

Tendler, Avichai ^{[1
]}

Mayo, Avraham E. ^{[1
]}

Alon, Uri ^{[1
]}

机构：

[1] Weizmann Inst Sci, Dept Mol Cell Biol, IL-76100 Rehovot, Israel

[2] Weizmann Inst Sci, Dept Phys Complex Syst, IL-76100 Rehovot, Israel

来源：

NATURE METHODS | 2015年 / 12卷 / 03期

基金：

欧洲研究理事会; 瑞士国家科学基金会;

关键词：

ARCHETYPAL ANALYSIS; GEOMETRY; REVEALS;

D O I：

10.1038/NMETH.3254

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We present the Pareto task inference method (ParTI; http://www.weizmann.ac.il/mcb/UriAlon/download/ParTI) for inferring biological tasks from high-dimensional biological data. Data are described as a polytope, and features maximally enriched closest to the vertices (or archetypes) allow identification of the tasks the vertices represent. We demonstrate that human breast tumors and mouse tissues are well described by tetrahedrons in gene expression space, with specific tumor types and biological functions enriched at each of the vertices, suggesting four key tasks.

引用

页码：233 / +

页数：6

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