Document Detail

Noise reduction in genome-wide perturbation screens using linear mixed-effect models.
MedLine Citation:
PMID:  21685046     Owner:  NLM     Status:  MEDLINE    
MOTIVATION: High-throughput perturbation screens measure the phenotypes of thousands of biological samples under various conditions. The phenotypes measured in the screens are subject to substantial biological and technical variation. At the same time, in order to enable high throughput, it is often impossible to include a large number of replicates, and to randomize their order throughout the screens. Distinguishing true changes in the phenotype from stochastic variation in such experimental designs is extremely challenging, and requires adequate statistical methodology.
RESULTS: We propose a statistical modeling framework that is based on experimental designs with at least two controls profiled throughout the experiment, and a normalization and variance estimation procedure with linear mixed-effects models. We evaluate the framework using three comprehensive screens of Saccharomyces cerevisiae, which involve 4940 single-gene knock-out haploid mutants, 1127 single-gene knock-out diploid mutants and 5798 single-gene overexpression haploid strains. We show that the proposed approach (i) can be used in conjunction with practical experimental designs; (ii) allows extensions to alternative experimental workflows; (iii) enables a sensitive discovery of biologically meaningful changes; and (iv) strongly outperforms the existing noise reduction procedures.
AVAILABILITY: All experimental datasets are publicly available at The R package HTSmix is available at
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Danni Yu; John Danku; Ivan Baxter; Sungjin Kim; Olena K Vatamaniuk; David E Salt; Olga Vitek
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.     Date:  2011-06-17
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  27     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2011-08-05     Completed Date:  2012-02-09     Revised Date:  2013-06-28    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  2173-80     Citation Subset:  IM    
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
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MeSH Terms
Gene Knockout Techniques
High-Throughput Screening Assays*
Linear Models
Saccharomyces cerevisiae / genetics
Grant Support
4R33DK070290-02/DK/NIDDK NIH HHS

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