Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad varia...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Nature protocols Ročník 7; číslo 3; s. 500 - 507
Hlavní autori: Stegle, Oliver, Parts, Leopold, Piipari, Matias, Winn, John, Durbin, Richard
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 01.03.2012
Nature Publishing Group
Predmet:
631/114/794
631/1647/2017
Algorithms
Analysis
Analytical Chemistry
Association analysis
Biological Techniques
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer programs
Computers
Data processing
Factor analysis
Factor Analysis, Statistical
Gene expression
Gene Expression Profiling - methods
Gene Expression Profiling - statistics & numerical data
Gene mapping
Genetic aspects
Genetic Association Studies - methods
Life Sciences
Mathematical models
Microarrays
Models, Statistical
Organic Chemistry
Physiological aspects
Protocol
Sensitivity and Specificity
Software
Statistical analysis
Statistical models
Transcription
Transcription factors
Variables
Yeast fungi
United Kingdom > UK
Germany
ISSN:1754-2189, 1750-2799, 1750-2799
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad variance components in the measurements. PEER takes as input transcript profiles and covariates from a set of individuals, and then outputs hidden factors that explain much of the expression variability. Optionally, these factors can be interpreted as pathway or transcription factor activations by providing prior information about which genes are involved in the pathway or targeted by the factor. The inferred factors are used in genetic association analyses. First, they are treated as additional covariates, and are included in the model to increase detection power for mapping expression traits. Second, they are analyzed as phenotypes themselves to understand the causes of global expression variability. PEER extends previous related surrogate variable models and can be implemented within hours on a desktop computer.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
These authors contributed equally to this work.
ISSN:1754-2189
1750-2799
1750-2799
DOI:10.1038/nprot.2011.457