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

doi:10.1038/nmeth.3254

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 journal › Article › peer-review

105 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 language	English
Pages (from-to)	233-235
Number of pages	3
Journal	Nature Methods
Volume	12
Issue number	3
DOIs	https://doi.org/10.1038/nmeth.3254
State	Published - 26 Feb 2015
Externally published	Yes

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© 2015 Nature America, Inc.

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AU - Tendler, Avichai

AU - Mayo, Avraham E.

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Inferring biological tasks using Pareto analysis of high-dimensional data

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