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Statistics and Informatics in Molecular Cancer Research$
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Carsten Wiuf and Claus L. Andersen

Print publication date: 2009

Print ISBN-13: 9780199532872

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780199532872.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 24 September 2021

Two methods for comparing genomic data across independent studies in cancer research: Meta-analysis and oncomine concepts map

Two methods for comparing genomic data across independent studies in cancer research: Meta-analysis and oncomine concepts map

Chapter:
(p.160) 7 Two methods for comparing genomic data across independent studies in cancer research: Meta-analysis and oncomine concepts map
Source:
Statistics and Informatics in Molecular Cancer Research
Author(s):

Wendy Lockwood Banka

Matthew J. Anstett

Daniel R. Rhodes

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199532872.003.0007

This chapter presents an overview of two methods used to compare microarray data across datasets in Oncomine: Meta-analysis and Oncomine Concepts Map. Both approaches begin with differential expression analyses to capture the differences in gene expression between two populations of interest, such as lung adenocarcinoma and normal lung. When more than one such analysis is available, results can be validated across studies by comparing the p-values of individual genes in meta-analysis, or by comparing the full list of over- or under-expressed genes in Oncomine Concepts Map. Because Oncomine Concepts Map includes a library of tens of thousands of these gene signatures, and uses Association Analysis to systematically compare each gene list to all others, this query can reveal unexpected similarities between different biological phenomena.

Keywords:   Oncomine, Oncomine Concepts Map, Metamap, differential expression analysis, meta-analysis, gene signature, association analysis

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