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

Abstract

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Software for high-throughput cytometry assays <p>A software tool for the analysis of high-throughput cell-based assays is presented.</p> AbstractHighthroughput 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. BackgroundCell-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. There is a variety of technologies that rely on the availability of genomic resources such as full-length cDNA libraries [3][4][5][6][7], small interfering RNA libraries [8][9][10][11][12], or collections of protein-specific interfering ligands (small chemical compounds) [13]. Loss-of-function assays that investigate the effect of silencing or (partial) removal of a gene product or its activity [10] are distinguished from gain-of-function assays, in which the function of a gene product is analyzed after its abundance or activity is increased [14].Depending on the process of interest, phenotypes can be assessed at various levels of complexity. In the simplest case a phenotype is a yes/no alternative, such as survival versus nonsurvival. More detail can be seen from a quantitative variable such as the activity of a reporter gene measured on a fluorescent plate reader, and even more complex features can involve time series or microscopic images. Although flow cytometry is among the standard methods in immunology, it has not been widely used in high-throughput screening, probably because of the lack of automation in data acquisition as well as in data analysis. However, the technology has evolved significantly in the recent past, and the latest generation of instruments can be equipped with high-throughput screening loaders that permit the measurement of large numbers of samples in reasonable periods of time [15]. One major advantage of flow cytometry is its ability to measure multiple parameters for each individual cell of a cell population. Whereas conventional cell-based assays are limited to recording population averages, this approach allows the investigation of biologic variation at the single cell level.A br...

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