A novel phenotypic dissimilarity method for image-based high-throughput screens

Xian Zhang,Michael Boutros

doi:10.1186/1471-2105-14-336

Abstract

BackgroundDiscovering functional relationships of genes through cell-based phenotyping has become an important approach in functional genomics. High-throughput imaging offers the ability to quantitatively assess complex phenotypes after perturbation by RNA interference (RNAi). Such image-based high-throughput RNAi screening studies have facilitated the discovery of novel components of gene networks and their interactions. Images generated by automated microscopy are typically analyzed by extracting quantitative features of individual cells, resulting in large multidimensional data sets. Robust and sensitive methods to interpret these data sets and to derive biologically relevant information in a high-throughput and unbiased manner remain to be developed.ResultsHere we propose a new analysis method, PhenoDissim, which computes the phenotypic dissimilarity between cell populations via Support Vector Machine classification and cross validation. Applying this method to a kinome RNAi screening data set, we demonstrate that the proposed method shows a good replicate reproducibility, separation of controls and clustering quality, and we are able to identify siRNA phenotypes and discover potential functional links between genes.ConclusionsPhenoDissim is a novel analysis method for image-based high-throughput screen, relying on two parameters which can be automatically optimized without a priori knowledge. PhenoDissim is freely available as an R package.

Highlights

Discovering functional relationships of genes through cell-based phenotyping has become an important approach in functional genomics
We used a previously generated image-based RNA interference (RNAi) screening data set as a benchmark for phenotypic dissimilarity analysis [12]
Phenotype identification with PhenoDissim One major goal in image-based screens is to identify perturbations that show significantly different phenotypes when compared to negative controls

Summary

Introduction

Discovering functional relationships of genes through cell-based phenotyping has become an important approach in functional genomics. High-throughput imaging offers the ability to quantitatively assess complex phenotypes after perturbation by RNA interference (RNAi). Cell-based screening approaches have been successfully used to monitor the effect of individual gene knockdowns or small molecule treatments, identify key regulators contributing to the assessed phenotype and investigate their interactions [1,2]. Such highthroughput screening experiments can be divided into two categories: homogeneous intensity-based methods, such as reporter gene or cell viability assays, and imagebased phenotyping approaches.

Methods

Results

Discussion

Conclusion