NDLI: An Independent Filter for Gene Set Testing Based on Spectral Enrichment

Content Provider	IEEE Xplore Digital Library
Author	Frost, H.R. Zhigang Li Asselbergs, F.W. Moore, J.H.
Copyright Year	2004
Abstract	Gene set testing has become an indispensable tool for the analysis of high-dimensional genomic data. An important motivation for testing gene sets, rather than individual genomic variables, is to improve statistical power by reducing the number of tested hypotheses. Given the dramatic growth in common gene set collections, however, testing is often performed with nearly as many gene sets as underlying genomic variables. To address the challenge to statistical power posed by large gene set collections, we have developed spectral gene set filtering (SGSF), a novel technique for independent filtering of gene set collections prior to gene set testing. The SGSF method uses as a filter statistic the p-value measuring the statistical significance of the association between each gene set and the sample principal components (PCs), taking into account the significance of the associated eigenvalues. Because this filter statistic is independent of standard gene set test statistics under the null hypothesis but dependent under the alternative, the proportion of enriched gene sets is increased without impacting the type I error rate. As shown using simulated and real gene expression data, the SGSF algorithm accurately filters gene sets unrelated to the experimental outcome resulting in significantly increased gene set testing power.
Sponsorship	IEEE Computer Society
Starting Page	1076
Ending Page	1086
Page Count	11
File Size	739454
File Format	PDF
ISSN	15455963
Volume Number	12
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bioinformatics Testing Genomics Gene expression Computational biology Standards Tracy-Widom Gene set testing gene set enrichment screening-testing principal component analysis random matrix theory
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Unsupervised gene set testing based on random matrix theory

Spectral gene set enrichment (SGSE)

Principal component gene set enrichment (PCGSE)

Nonnegative Principal Component Analysis for Cancer Molecular Pattern Discovery

Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis

RPCA-Based Tumor Classification Using Gene Expression Data

Fine-Scale Genetic Mapping Using Independent Component Analysis

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

PCA in High Dimensions: An Orientation

An Independent Filter for Gene Set Testing Based on Spectral Enrichment

Similar Documents

Unsupervised gene set testing based on random matrix theory

Spectral gene set enrichment (SGSE)

Principal component gene set enrichment (PCGSE)

Nonnegative Principal Component Analysis for Cancer Molecular Pattern Discovery

Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis

RPCA-Based Tumor Classification Using Gene Expression Data

Fine-Scale Genetic Mapping Using Independent Component Analysis

GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

PCA in High Dimensions: An Orientation

An Independent Filter for Gene Set Testing Based on Spectral Enrichment