NDLI: Testing for Associations of Opposite Directionality in a Heterogeneous Population

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Statistics in Biosciences : Volume 9

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IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients

A Model-Based Approach for Species Abundance Quantification Based on Shotgun Metagenomic Data

Robust Bayesian FDR Control Using Bayes Factors, with Applications to Multi-tissue eQTL Discovery

Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

ROC Curve Analysis in the Presence of Imperfect Reference Standards

TROM: A Testing-Based Method for Finding Transcriptomic Similarity of Biological Samples

Testing for Associations of Opposite Directionality in a Heterogeneous Population

Establishing Informative Prior for Gene Expression Variance from Public Databases

Unit-Free and Robust Detection of Differential Expression from RNA-Seq Data

SIDEseq: A Cell Similarity Measure Defined by Shared Identified Differentially Expressed Genes for Single-Cell RNA sequencing Data

Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression

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Estimation of Stratified Mark-Specific Proportional Hazards Models Under Two-Phase Sampling with Application to HIV Vaccine Efficacy Trials

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Estimating a Treatment Effect in Residual Time Quantiles Under the Additive Hazards Model

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Testing for Associations of Opposite Directionality in a Heterogeneous Population

Content Provider	Springer Nature Link
Author	Zhang, Fangyuan Ding, Jie Lin, Shili
Copyright Year	2016
Abstract	In gene networks, it is possible that the patterns of gene co-expression may exist only in a subset of the sample. In studies of relationships between genotypes and expressions of genes over multiple tissues, there may be associations in some tissues but not in the others. Despite the importance of the problem in genomic applications, it is challenging to identify relationships between two variables when the correlation may only exist in a subset of the sample. The situation becomes even less tractable when there exist two subsets in which correlations are in opposite directions. By ranking subset relationships according to Kendall’s tau, a tau-path can be derived to facilitate the identification of correlated subsets, if such subsets exist. However, the current tau-path methodology only considers the situation in which there is association in a subsample; the more complex scenario depicting the existence of two subsets with opposite directionality of associations was not addressed. Further, existing algorithms for finding tau-paths may be suboptimal given their greedy nature. In this paper, we extend the tau-path methodology to accommodate the situation in which the sample may be drawn from a heterogeneous population composed of subpopulations portraying positive and negative associations. We also propose the use of a cross entropy Monte Carlo procedure to obtain an optimal tau-path, CEMC $$_{tp}$$ . The algorithm not only can provide simultaneous detection of positive and negative correlations in the same sample, but also can lead to the identification of subsamples that provide evidence for the detected associations. An extensive simulation study shows the aptness of CEMC $$_{tp}$$ for detecting associations under various scenarios. Compared with two standard tests for detecting associations, CEMC $$_{tp}$$ is seen to be more powerful when there are indeed complex subset associations with well-controlled type-I error rates. We applied CEMC $$_{tp}$$ to the NCI-60 gene expression data to illustrate its utility for uncovering network relationships that were missed with standard methods.
Starting Page	137
Ending Page	159
Page Count	23
File Format	PDF
ISSN	18671764
Journal	Statistics in Biosciences
Volume Number	9
Issue Number	1
e-ISSN	18671772
Language	English
Publisher	Springer US
Publisher Date	2016-09-22
Publisher Institution	International Chinese Statistical Association
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Cross entropy Monte Carlo (CEMC) Tau-path Heterogeneous sample Subset associations Gene networks
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Biochemistry, Genetics and Molecular Biology

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

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