Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts.
Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processing, visualization, quality assessment, and statistical inference. The software is freely available in the Bioconductor package prada. The method permits analysis of large screens to detect the effects of molecular interventions in cellular systems.
Hahne
Florian
F
Division of Molecular Genome Analysis, German Cancer Research Center, INF 580, 69120 Heidelberg, Germany. f.hahne@dkfz.de
Arlt
Dorit
D
Sauermann
Mamatha
M
Majety
Meher
M
Poustka
Annemarie
A
Wiemann
Stefan
S
Huber
Wolfgang
W
D016208:Databases, Factual; D001330:Electronic Data Processing / Q000379:methods / Q000592:standards; D020869:Gene Expression Profiling; D030342:Genetic Diseases, Inborn / Q000453:epidemiology*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D016017:Odds Ratio; D012984:Software