A Multivariate Computational Method to Analyze High-Content RNAi Screening Data.

ABSTRACT: High-content screening (HCS) using RNA interference (RNAi) in combination with automated microscopy is a powerful investigative tool to explore complex biological processes. However, despite the plethora of data generated from these screens, little progress has been made in analyzing HC data using multivariate methods that exploit the full richness of multidimensional data. We developed a novel multivariate method for HCS, multivariate robust analysis method (M-RAM), integrating image feature selection with ranking of perturbations for hit identification, and applied this method to an HC RNAi screen to discover novel components of the DNA damage response in an osteosarcoma cell line. M-RAM automatically selects the most informative phenotypic readouts and time points to facilitate the more

SUBMITTER: Rameseder J 

PROVIDER: S-EPMC5377593 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature