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

Journal Article

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 efficient design of follow-up experiments and enhance biological understanding. Our method outperforms univariate hit identification and identifies relevant genes that these approaches would have missed. We found that statistical cell-to-cell variation in phenotypic responses is an important predictor of hits in RNAi-directed image-based screens. Genes that we identified as modulators of DNA damage signaling in U2OS cells include B-Raf, a cancer driver gene in multiple tumor types, whose role in DNA damage signaling we confirm experimentally, and multiple subunits of protein kinase A.

Full Text

Duke Authors

Cited Authors

  • Rameseder, J; Krismer, K; Dayma, Y; Ehrenberger, T; Hwang, MK; Airoldi, EM; Floyd, SR; Yaffe, MB

Published Date

  • September 2015

Published In

Volume / Issue

  • 20 / 8

Start / End Page

  • 985 - 997

PubMed ID

  • 25918037

Electronic International Standard Serial Number (EISSN)

  • 1552-454X

International Standard Serial Number (ISSN)

  • 1087-0571

Digital Object Identifier (DOI)

  • 10.1177/1087057115583037

Language

  • eng