Exploratory analysis of cell-based screening data for phenotype identification in drug-siRNA study
Abstract
Most phenotype-identification methods in cell-based screening assume prior knowledge about expected phenotypes or involve intricate parameter-setting. They are useful for analysis targeting known phenotype properties; but need exists to explore, with minimum presumptions, the potentially-interesting phenotypes derivable from data. We present a method for this exploration, using clustering to eliminate phenotype-labelling requirement and GUI visualisation to facilitate parameter-setting. The steps are: outlier-removal, cell clustering and interactive visualisation for phenotypes refinement. For drug-siRNA study, we introduce an auto-merging procedure to reduce phenotype redundancy. We validated the method on two Golgi apparatus screens and showcase its contribution for better understanding of screening-images.
Authors: William Chandra Tjhi, Kee Khoon Lee, Terence Hung, Ivor Wai-Hung Tsang, Yew-Soon Ong, Frédéric Bard, Victor Racine
Published in: International Journal of Computational Biology and Drug Design (2011)