title: Noise reduction in genome-wide perturbation screens using linear mixed-effect models.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  ovitek@stat.purdue.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
authors.forename: Danni
authors.initials: D
authors.lastname: Yu
authors.forename: John
authors.initials: J
authors.lastname: Danku
authors.forename: Ivan
authors.initials: I
authors.lastname: Baxter
authors.forename: Sungjin
authors.initials: S
authors.lastname: Kim
authors.forename: Olena K
authors.initials: OK
authors.lastname: Vatamaniuk
authors.forename: David E
authors.initials: DE
authors.lastname: Salt
authors.forename: Olga
authors.initials: O
authors.lastname: Vitek
mesh_terms: D055786:Gene Knockout Techniques; D023281:Genomics; D057166:High-Throughput Screening Assays; D016014:Linear Models; D010641:Phenotype; D012441:Saccharomyces cerevisiae / Q000235:genetics