Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts

Florian Hahne¹ , Dorit Arlt¹ , Mamatha Sauermann¹ , Meher Majety¹ , Annemarie Poustka¹ , Stefan Wiemann¹ and Wolfgang Huber²

¹Division of Molecular Genome Analysis, German Cancer Research Center, INF 580, 69120 Heidelberg, Germany

²EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK

author email corresponding author email

Genome Biology 2006, 7:R77doi:10.1186/gb-2006-7-8-r77


Published:	17 August 2006

Subject areas: Bioinformatics, Cell biology

Abstract

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.