Introduction

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.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.All experimental datasets are publicly available at www.ionomicshub.org. The R package HTSmix is available at http://www.stat.purdue.edu/~ovitek/HTSmix.html.ovitek@stat.purdue.eduSupplementary data are available at Bioinformatics online.

Publications

  1. Noise reduction in genome-wide perturbation screens using linear mixed-effect models.
    Cite this
    Yu D, Danku J, Baxter I, Kim S, Vatamaniuk OK, Salt DE, Vitek O, 2011-08-01 - Bioinformatics (Oxford, England)

Credits

  1. Danni Yu
    Developer

    Department of Statistics, Purdue University, United States of America

  2. John Danku
    Developer

  3. Ivan Baxter
    Developer

  4. Sungjin Kim
    Developer

  5. Olena K Vatamaniuk
    Developer

  6. David E Salt
    Developer

  7. Olga Vitek
    Investigator

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Summary
AccessionBT000089
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByOlga Vitek