Gene selection via a spectral approach

L. Wolf, A. Shashua, S. Mukherjee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Array technologies have made it possible to record simultaneously the expression pattern of thousands of genes. A fundamental problem in the analysis of gene expression data is the identification of highly relevant genes that either discriminate between phenotypic labels or are important with respect to the cellular process studied in the experiment. Examples include: cell cycle or heat shock in yeast experiments, chemical or genetic perturbations of mammalian cell lines, and genes involved in class discovery for human tumors. We focus on the task of unsupervised gene selection. Selecting a small subset of genes is particularly challenging as the data sets involved are typically characterized by a small sample size and a very large feature space. We propose a model independent approach which scores candidate gene selections using spectral properties of the candidate affinity matrix. The algorithm is simple to implement, yet contains a number of remarkable properties which guarantee consistent sparse selections. We applied our algorithm on five different data sets. The first consists of time course data from four well studied Hematopoietic cell lines (HL-60, Jurkat, NB4, and U937). The other four data sets include three well studied treatment outcomes (large cell lymphoma, childhood medulloblastomas, breast tumors) and one unpublished data set (lymph status). We compared our approach both with other unsupervised methods (SOM,PCA,GS) and with supervised methods (SNR,RMB,RFE). The results show that our approach considerably outperforms all the other unsupervised approaches in our study, is competitive with supervised methods and in some cases even outperforms supervised approaches.

Original languageEnglish
Title of host publication2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
PublisherIEEE Computer Society
ISBN (Electronic)0769526608
DOIs
StatePublished - 2005
Externally publishedYes
Event2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops - San Diego, United States
Duration: 21 Sep 200523 Sep 2005

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2005-September
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
Country/TerritoryUnited States
CitySan Diego
Period21/09/0523/09/05

Keywords

  • Gene selection
  • Microarray analysis
  • Spectral methods

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