Phenotypic distance measures for image-based high-throughput screening

High-throughput image-based screening (also termed high-content screening) has become a popular method in systems biology, functional genomics and drug discovery. Data analysis of high-content screening can be divided into two steps: image quantification and phenotypic analysis. Previously we have developed two R packages, EBImage and imageHTS for image quantification. The current R package phenoDist is designed for measuring the phenotypic distance between treatments (e.g., RNAi, small molecular), in order to identify phenotypes and to group treatments into functional clusters. The package implements various methods to compute phenotypic distance including scaling, principle component analysis, factor analysis [6], Kolmogorov-Smirnov statistics [5], SVM (Support Vector Machine) supervised classification [2], SVM weight vector [3], and SVM classification accuracy [8]. The package also provides functions for phenotype identification, treatment clustering and gene enrichment analysis. In this vignette, we will demonstrate how phenoDist can be used for phenotypic distance calculation, phenotype identification and phenotypic clustering in high-content screening data analysis.