Selecting Relevant Genes with a Spectral Approach
Array technologies have made it possible to record simultaneouslythe expression pattern of thousands of genes. A fundamental problemin the analysis of gene expression data is the identification ofhighly relevant genes that either discriminate between phenotypiclabels or are important with respect to...
| Main Authors: | , , |
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| Language: | en_US |
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2005
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| Online Access: | http://hdl.handle.net/1721.1/30444 |
| _version_ | 1811095908812587008 |
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| author | Wolf, Lior Shashua, Amnon Mukherjee, Sayan |
| author_facet | Wolf, Lior Shashua, Amnon Mukherjee, Sayan |
| author_sort | Wolf, Lior |
| collection | MIT |
| description | Array technologies have made it possible to record simultaneouslythe expression pattern of thousands of genes. A fundamental problemin the analysis of gene expression data is the identification ofhighly relevant genes that either discriminate between phenotypiclabels or are important with respect to the cellular process studied inthe experiment: for example 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. In this paperwe focus on the task of unsupervised gene selection. The problemof selecting a small subset of genes is particularly challengingas the datasets involved are typically characterized by a very smallsample size  in the order of few tens of tissue samples  andby a very large feature space as the number of genes tend to bein the high thousands. We propose a model independent approachwhich scores candidate gene selections using spectral properties ofthe candidate affinity matrix. The algorithm is very straightforwardto implement yet contains a number of remarkable properties whichguarantee consistent sparse selections. To illustrate the value of ourapproach we applied our algorithm on five different datasets. Thefirst consists of time course data from four well studied Hematopoieticcell lines (HL-60, Jurkat, NB4, and U937). The other fourdatasets include three well studied treatment outcomes (large celllymphoma, childhood medulloblastomas, breast tumors) and oneunpublished dataset (lymph status). We compared our approachboth with other unsupervised methods (SOM,PCA,GS) and withsupervised methods (SNR,RMB,RFE). The results clearly showthat our approach considerably outperforms all the other unsupervisedapproaches in our study, is competitive with supervised methodsand in some case even outperforms supervised approaches. |
| first_indexed | 2024-09-23T16:33:32Z |
| id | mit-1721.1/30444 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:33:32Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | mit-1721.1/304442019-04-10T16:37:20Z Selecting Relevant Genes with a Spectral Approach Wolf, Lior Shashua, Amnon Mukherjee, Sayan AI Array technologies have made it possible to record simultaneouslythe expression pattern of thousands of genes. A fundamental problemin the analysis of gene expression data is the identification ofhighly relevant genes that either discriminate between phenotypiclabels or are important with respect to the cellular process studied inthe experiment: for example 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. In this paperwe focus on the task of unsupervised gene selection. The problemof selecting a small subset of genes is particularly challengingas the datasets involved are typically characterized by a very smallsample size  in the order of few tens of tissue samples  andby a very large feature space as the number of genes tend to bein the high thousands. We propose a model independent approachwhich scores candidate gene selections using spectral properties ofthe candidate affinity matrix. The algorithm is very straightforwardto implement yet contains a number of remarkable properties whichguarantee consistent sparse selections. To illustrate the value of ourapproach we applied our algorithm on five different datasets. Thefirst consists of time course data from four well studied Hematopoieticcell lines (HL-60, Jurkat, NB4, and U937). The other fourdatasets include three well studied treatment outcomes (large celllymphoma, childhood medulloblastomas, breast tumors) and oneunpublished dataset (lymph status). We compared our approachboth with other unsupervised methods (SOM,PCA,GS) and withsupervised methods (SNR,RMB,RFE). The results clearly showthat our approach considerably outperforms all the other unsupervisedapproaches in our study, is competitive with supervised methodsand in some case even outperforms supervised approaches. 2005-12-22T01:19:04Z 2005-12-22T01:19:04Z 2004-01-27 MIT-CSAIL-TR-2004-003 AIM-2004-002 CBCL-234 http://hdl.handle.net/1721.1/30444 en_US Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 0 p. 12089662 bytes 629163 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI Wolf, Lior Shashua, Amnon Mukherjee, Sayan Selecting Relevant Genes with a Spectral Approach |
| title | Selecting Relevant Genes with a Spectral Approach |
| title_full | Selecting Relevant Genes with a Spectral Approach |
| title_fullStr | Selecting Relevant Genes with a Spectral Approach |
| title_full_unstemmed | Selecting Relevant Genes with a Spectral Approach |
| title_short | Selecting Relevant Genes with a Spectral Approach |
| title_sort | selecting relevant genes with a spectral approach |
| topic | AI |
| url | http://hdl.handle.net/1721.1/30444 |
| work_keys_str_mv | AT wolflior selectingrelevantgeneswithaspectralapproach AT shashuaamnon selectingrelevantgeneswithaspectralapproach AT mukherjeesayan selectingrelevantgeneswithaspectralapproach |