Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts. | Open Access Method Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts Florian Hahne Dorit Arlt Mamatha Sauermann Meher Majety Annemarie Poustka Stefan Wiemann and Wolfgang Huber Addresses Division of Molecular Genome Analysis German Cancer Research Center INF 580 69120 Heidelberg Germany. tEMBL -European Bioinformatics Institute Wellcome Trust Genome Campus Cambridge CB10 1SD UK. Correspondence Florian Hahne. Email Published 17 August 2006 Genome Biology 2006 7 R77 doi l86 gb-2006-7-8-r77 The electronic version of this article is the complete one and can be found online at http 2006 7 8 R77 Received 18 May 2006 Revised 7 July 2006 Accepted 17 August 2006 2006 Hahne et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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. Background Cell-based assays permit functional profiling by probing the roles of molecular actors in biologic processes or phenotypes. They perturb the activity or abundance of gene products of interest and measure the resulting effect in a population of cells 1 2 . This can be done in principle for any gene or combination of genes and any biologic process.
