Title: "Noise reduction in genome-wide perturbation screens using linear mixed-effect models"
Speaker: Danni Yu, Department of Statistics, Purdue University
Place: HORT 117; February 8, 2011; Tuesday, 4:30pm


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 the order of the replicates 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 S. cerevisiae, which involve 4940 single-gene knock-out haploid mutants, 1127 single-gene knock-out diploid mutants, and 5798 single-gene over-expression haploid strains. We show that the proposed approach (a) can be used in conjunction with practical experimental designs, (b) allows extensions to alternative experimental workflows, (c) enables a sensitive discovery of biologically meaningful changes, and (d) strongly outperforms the existing noise reduction procedures.

Associated Reading:

Malo, N. et al. 2006. Statistical practice in high-throughput screening data analysis. Nature Biotechnology 24:167-175.

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