A Multivariate Computational Method to Analyze High-Content RNAi Screening Data.
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.
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA Computational Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, MA, USA.
Jonathan
J
Rameseder
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Konstantin
K
Krismer
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Yogesh
Y
Dayma
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Tobias
T
Ehrenberger
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Mun Kyung
MK
Hwang
Department of Statistics and FAS Center for Systems Biology, Harvard University, Cambridge, MA, USA Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Edoardo M
EM
Airoldi
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Scott R
SR
Floyd
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA Broad Institute of MIT and Harvard, Cambridge, MA, USA Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA myaffe@mit.edu.
Michael B
MB
Yaffe
D000465:Algorithms; D000818:Animals; D002460:Cell Line; D003198:Computer Simulation; D004249:DNA Damage; D055785:Gene Knockdown Techniques; D057166:High-Throughput Screening Assays; D006801:Humans; D008954:Models, Biological; D010641:Phenotype; D048493:Proto-Oncogene Proteins B-raf / Q000235:genetics; D034622:RNA Interference; D012333:RNA, Messenger / Q000235:genetics*; D034741:RNA, Small Interfering / Q000235:genetics*
D012333:RNA, Messenger; D034741:RNA, Small Interfering; D048493:Proto-Oncogene Proteins B-raf
RNAi screening; feature selection; high-content screening; hit identification; multivariate data analysis