Analysis of High-Throughput Flow Cytometry Data Using plateCore

Errol Strain,Ryan R Brinkman,Florian Hahne,Perry Haaland

doi:10.1155/2009/356141

Abstract

Flow cytometry (FCM) software packages from R/Bioconductor, such as flowCore and flowViz, serve as an open platform for development of new analysis tools and methods. We created plateCore, a new package that extends the functionality in these core packages to enable automated negative control-based gating and make the processing and analysis of plate-based data sets from high-throughput FCM screening experiments easier. plateCore was used to analyze data from a BD FACS CAP screening experiment where five Peripheral Blood Mononucleocyte Cell (PBMC) samples were assayed for 189 different human cell surface markers. This same data set was also manually analyzed by a cytometry expert using the FlowJo data analysis software package (TreeStar, USA). We show that the expression values for markers characterized using the automated approach in plateCore are in good agreement with those from FlowJo, and that using plateCore allows for more reproducible analyses of FCM screening data.

Highlights

While there are a number of different software packages available for analysis of Flow cytometry (FCM) data, these programs are often ill-suited to the development of new methods needed for analyzing high-throughput FCM studies
This study focuses on comparing two different Flow CytometryHigh-Content Screening (FC-HCS) analysis methods, it is important to consider the original goal of the experiment used to generate the data when interpreting the results
Since BD FACS CAP is a standard platform for screening a wide range of cell types, and antibody concentrations were not optimized for these particular PMBC samples, results are reported as the percentage of cells above the isotype gate rather than positive or negative

Summary

Introduction

While there are a number of different software packages available for analysis of FCM data, these programs are often ill-suited to the development of new methods needed for analyzing high-throughput FCM studies. Current FC-HCS data analysis methods often use a combination of software packages for different parts of the analysis. Statistical analysis is performed in packages like MATLAB (USA) and R (http://www.r-project.org/) [3]. This approach to FC-HCS analysis results in methods that are semiautomated at best, and they often require significant subjective and error-prone manual intervention to identify cells of interest [4].

Methods

Results

Conclusion