NDLI: GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes

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GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes

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Call for Papers: Special issue on sofware and databases in TCBB

Issue 1, January 2013

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 9

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 8

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 7

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 6

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 5

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 4

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 3

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 2

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 1

GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes

Content Provider	ACM Digital Library
Author	Molter, Colin Bersini, Hugues Meganck, Stijn Duque, Robin Lazar, Cosmin Steenhoff, David Nowe, Ann Taminau, Jonatan Weiss solis, David Y. Coletta, Alain
Abstract	The potential of microarray gene expression (MAGE) data is only partially explored due to the limited number of samples in individual studies. This limitation can be surmounted by merging or integrating data sets originating from independent MAGE experiments, which are designed to study the same biological problem. However, this process is hindered by batch effects that are study-dependent and result in random data distortion; therefore numerical transformations are needed to render the integration of different data sets accurate and meaningful. Our contribution in this paper is two-fold. First we propose GENESHIFT, a new nonparametric batch effect removal method based on two key elements from statistics: empirical density estimation and the inner product as a distance measure between two probability density functions; second we introduce a new validation index of batch effect removal methods based on the observation that samples from two independent studies drawn from a same population should exhibit similar probability density functions. We evaluated and compared the GENESHIFT method with four other state-of-the-art methods for batch effect removal: Batch-mean centering, empirical Bayes or COMBAT, distance-weighted discrimination, and cross-platform normalization. Several validation indices providing complementary information about the efficiency of batch effect removal methods have been employed in our validation framework. The results show that none of the methods clearly outperforms the others. More than that, most of the methods used for comparison perform very well with respect to some validation indices while performing very poor with respect to others. GENESHIFT exhibits robust performances and its average rank is the highest among the average ranks of all methods used for comparison.
Starting Page	383
Ending Page	392
Page Count	10
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2013.12
Volume Number	10
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gene expression,Estimation,Sociology,Statistics,Data integration,Lungs,integrative analysis of gene expression microarrays,Gene expression,Estimation,Sociology,Statistics,Data integration,Lungs,nonparametric methods,Batch effects,microarray data integration,distance measures between probability density functions,inner product,density estimation
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes

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GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes