Distribution Analyzer, a methodology for identifying and clustering outlier conditions from single-cell distributions, and its application to a Nanog reporter RNAi screen

BMC Bioinformatics

Julian A. Gingold⁵  Ed S. Coakley⁴  Jie Su³  Dung-Fang Lee⁵  Zerlina Lau²  Hongwei Zhou⁵  Dan P. Felsenfeld²  Christoph Schaniel¹  Ihor R. Lemischka¹ 

[1] Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA

[2] Integrated Screening Core, Experimental Therapeutics Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA

[3] Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York 10065, NY, USA

[4] Program in Applied Mathematics, Yale University, New Haven 06511, CT, USA

[5] Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA

关键词: Kolmogorov-Smirnov distance;    Hellinger distance;    Nanog RNAi screen;    High-content screening methodology;    Fluorescence distribution;    Genome-scale screen analysis;   

Others  :  1230724
DOI  :  10.1186/s12859-015-0636-7

 received in 2015-01-16, accepted in 2015-06-05,  发布年份 2015

【 摘 要 】

Background

Chemical or small interfering (si) RNA screens measure the effects of many independent experimental conditions, each applied to a population of cells (e.g., all of the cells in a well). High-content screens permit a readout (e.g., fluorescence, luminescence, cell morphology) from each cell in the population. Most analysis approaches compare the average effect on each population, precluding identification of outliers that affect the distribution of the reporter in the population but not its average. Other approaches only measure changes to the distribution with a single parameter, precluding accurate distinction and clustering of interesting outlier distributions.

Results

We describe a methodology to identify outlier conditions by considering the cell-level measurements from each condition as a sample of an underlying distribution. With appropriate selection of a distance metric, all effects can be embedded in a fixed-dimensionality Euclidean basis, facilitating identification and clustering of biologically interesting outliers. We demonstrate that measurement of distances with the Hellinger distance metric offers substantial computational efficiencies over alternative metrics. We validate this methodology using an RNA interference (RNAi) screen in mouse embryonic stem cells (ESC) with a Nanog reporter. The methodology clusters effects of multiple control siRNAs into their true identities better than conventional approaches describing the median cell fluorescence or the commonly used Kolmogorov-Smirnov distance between the observed fluorescence distribution and the null distribution. It identifies outlier genes with effects on the reporter distribution that would have been missed by other methods. Among them, siRNA targeting Chek1 leads to a wider Nanog reporter fluorescence distribution. Similarly, siRNA targeting Med14 or Med27 leads to a narrower Nanog reporter fluorescence distribution. We confirm the roles of these three genes in regulating pluripotency by mRNA expression and alkaline phosphatase staining using independent short hairpin (sh) RNAs.

Conclusions

Using our methodology, we describe each experimental condition by a probability distribution. Measuring distances between probability distributions permits a multivariate rather than univariate readout. Clustering points derived from these distances allows us to obtain greater biological insight than methods based solely on single parameters. We find several outliers from a mouse ESC RNAi screen that we confirm to be pluripotency regulators. Many of these outliers would have been missed by other analysis methods.

【 授权许可】

   
2015 Gingold et al.

【 图 表 】

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