NDLI: Establishing Informative Prior for Gene Expression Variance from Public Databases

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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

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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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Establishing Informative Prior for Gene Expression Variance from Public Databases

Content Provider	Springer Nature Link
Author	Li, Nan McCall, Matthew N. Wu, Zhijin
Copyright Year	2016
Abstract	Identifying differential expressed genes across various conditions or genotypes is the most typical approach to studying the regulation of gene expression. An estimate of gene-specific variance is often needed for the assessment of statistical significance in most differential expression (DE) detection methods, including linear models (e.g., for transformed and normalized microarray data) and generalized linear models (e.g., for count data in RNAseq). Due to a common limit in sample size, the variance estimate is often unstable in small experiments. Shrinkage estimates using empirical Bayes methods have proven useful in improving the variance estimate, hence improving the detection of DE. The most widely used empirical Bayes methods borrow information across genes within the same experiments. In these methods, genes are considered exchangeable or exchangeable conditioning on expression level. We propose, with the increasing accumulation of expression data, borrowing information from historical data on the same gene can provide better estimate of gene-specific variance, thus further improve DE detection. Specifically, we show that the variation of gene expression is truly gene-specific and reproducible between different experiments. We present a new method to establish informative gene-specific prior on the variance of expression using existing public data, and illustrate how to shrink the variance estimate and detect DE. We demonstrate improvement in DE detection under our strategy compared to leading DE detection methods.
Starting Page	160
Ending Page	177
Page Count	18
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	2016-10-05
Publisher Institution	International Chinese Statistical Association
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Differential expression Empirical Bayes Public database Shrinkage variance estimate Hierarchical model
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

Pathway crosstalk effects: shrinkage and disentanglement using a Bayesian hierarchical model

Bayesian Hierarchical Model for Differential Gene Expression Using RNA-Seq Data

Empirical Bayes inference for the Weibull model

Hierarchical Bayes models for cDNA microarray gene expression

Shrinkage Estimators for Covariance Matrices

Flexible empirical bayes models for differential gene expression (2007).

Empirical Bayes analysis of variance component models for microarray data

Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions

Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

Establishing Informative Prior for Gene Expression Variance from Public Databases

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