Noise reduction in genome-wide perturbation screens using linear mixed-effect models

Danni Yu, John Danku, Ivan Baxter, Sungjin Kim, Olena K. Vatamaniuk, David E. Salt, Olga Vitek

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2173-2180
Number of pages8
JournalBioinformatics
Volume27
Issue number16
Early online date17 Jun 2011
DOIs
Publication statusPublished - 15 Aug 2011

Keywords

  • false discovery rate
  • scale RNAI screens
  • saccharomyces-cerevisiae
  • normalization methods
  • genetic interactions
  • yeast
  • identification
  • interference
  • networks
  • ionomics

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