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Linear Separability of Gene Expression Data Sets

Authors:

Benny ChorAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 7, Issue 2

Pages 375 - 381

https://doi.org/10.1109/TCBB.2008.90

Published: 01 April 2010 Publication History

Abstract

We study simple geometric properties of gene expression data sets, where samples are taken from two distinct classes (e.g., two types of cancer). Specifically, the problem of linear separability for pairs of genes is investigated. If a pair of genes exhibits linear separation with respect to the two classes, then the joint expression level of the two genes is strongly correlated to the phenomena of the sample being taken from one class or the other. This may indicate an underlying molecular mechanism relating the two genes and the phenomena(e.g., a specific cancer). We developed and implemented novel efficient algorithmic tools for finding all pairs of genes that induce a linear separation of the two sample classes. These tools are based on computational geometric properties and were applied to 10 publicly available cancer data sets. For each data set, we computed the number of actual separating pairs and compared it to an upper bound on the number expected by chance and to the numbers resulting from shuffling the labels of the data at random empirically. Seven out of these 10 data sets are highly separable. Statistically, this phenomenon is highly significant, very unlikely to occur at random. It is therefore reasonable to expect that it manifests a functional association between separating genes and the underlying phenotypic classes.

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Cited By

Borges JRamos HLoureiro A(2022)A Classification Strategy for Internet of Things Data Based on the Class Separability Analysis of Time Series DynamicsACM Transactions on Internet of Things10.1145/35330493:3(1-30)Online publication date: 13-Jul-2022
Hamid ATran Q(2015)Emerging Trends in Computational Biology, Bioinformatics, and Systems BiologyundefinedOnline publication date: 21-Aug-2015
Jafarian ANgom ARueda L(2011)New gene subset selection approaches based on linear separating genes and gene-pairsProceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics10.5555/2075619.2075626(50-62)Online publication date: 2-Nov-2011

Index Terms

Linear Separability of Gene Expression Data Sets
1. Applied computing
  1. Life and medical sciences

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 7, Issue 2

April 2010

189 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 April 2010

Published in TCBB Volume 7, Issue 2

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Cited By

Borges JRamos HLoureiro A(2022)A Classification Strategy for Internet of Things Data Based on the Class Separability Analysis of Time Series DynamicsACM Transactions on Internet of Things10.1145/35330493:3(1-30)Online publication date: 13-Jul-2022
Hamid ATran Q(2015)Emerging Trends in Computational Biology, Bioinformatics, and Systems BiologyundefinedOnline publication date: 21-Aug-2015
Jafarian ANgom ARueda L(2011)New gene subset selection approaches based on linear separating genes and gene-pairsProceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics10.5555/2075619.2075626(50-62)Online publication date: 2-Nov-2011

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