Inferring biological tasks using Pareto analysis of high-dimensional data

Yuval Hart, Hila Sheftel, Jean Hausser, Pablo Szekely, Noa Bossel Ben-Moshe, Yael Korem, Avichai Tendler, Avraham E. Mayo, Uri Alon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

99 Scopus citations

Abstract

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.

Original languageAmerican English
Pages (from-to)233-235
Number of pages3
JournalNature Methods
Volume12
Issue number3
DOIs
StatePublished - 26 Feb 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015 Nature America, Inc.

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