Inferring biological tasks using Pareto analysis of high-dimensional data

被引：1

作者：

Hart Y. ^{[1
]}

Sheftel H. ^{[1
]}

Hausser J. ^{[1
]}

Szekely P. ^{[1
]}

Ben-Moshe N.B. ^{[2
]}

Korem Y. ^{[1
]}

Tendler A. ^{[1
]}

Mayo A.E. ^{[1
]}

Alon U. ^{[1
]}

机构：

[1] Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot

[2] Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot

来源：

Nature Methods | 2015年 / 12卷 / 3期

基金：

欧洲研究理事会;

关键词：

D O I：

10.1038/nmeth.3254

中图分类号：

学科分类号：

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. © 2015 Nature America, Inc.

引用

页码：233 / 235

页数：2

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