NDLI: A methodology for analyzing SAGE libraries for cancer profiling

Content Provider	ACM Digital Library
Author	Sander, Jörg Yuen, Man Saint Ng, Raymond T. Jones, Steven J. Sleumer, Monica C.
Copyright Year	2005
Abstract	Serial Analysis of Gene Expression (SAGE) has proven to be an important alternative to microarray techniques for global profiling of mRNA populations. We have developed preprocessing methodologies to address problems in analyzing SAGE data due to noise caused by sequencing error, normalization methodologies to account for libraries sampled at different depths, and missing tag imputation methodologies to aid in the analysis of poorly sampled SAGE libraries. We have also used subspace selection using the Wilcoxon rank sum test to exclude tags that have similar expression levels regardless of source. Using these methodologies we have clustered, using the OPTICS algorithm, 88 SAGE libraries derived from cancerous and normal tissues as well as cell line material. Our results produced eight dense clusters representing ovarian cancer cell line, brain cancer cell line, brain cancer bulk tissue, prostate tissue, pancreatic cancer, breast cancer cell line, normal brain, and normal breast bulk tissue. The ovarian cancer and brain cancer cell lines clustered closely together, leading to a further investigation on possible associations between these two cancer types. We also investigated the utility of gene expression data in the classification between normal and cancerous tissues. Our results indicate that brain and breast cancer libraries have strong identities allowing robust discrimination from their normal counterparts. However, the SAGE expression data provide poor predictive accuracy in discriminating between prostate and ovarian cancers and their respective normal tissues.
Starting Page	35
Ending Page	60
Page Count	26
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/1055709.1055712
Volume Number	23
Issue Number	1
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gene expression Cancer profiling Classification Clustering
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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