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

Xian Zhang, Michael Boutros
2013 BMC Bioinformatics  
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). 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
more » ... tative 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. Results: Here 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. Conclusions: PhenoDissim 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.
doi:10.1186/1471-2105-14-336 pmid:24256072 pmcid:PMC4225524 fatcat:t4vfjswxzffklez235a4mque54