GenePattern flow cytometry suite.

Peter Carr,Karin Breuer,Yu Qian,Ryan R Brinkman,Rafick-Pierre Sekaly,Aaron Barsky,Peter Wilkinson,Ted Liefeld,Josef Spidlen,Richard H Scheuermann,Michael Reich,Marc-Danie Nazaire,Barbara Allen Hill,Jill P Mesirov

doi:10.1186/1751-0473-8-14

Abstract

BackgroundTraditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. Recent technological advances have increased the amount of data generated by the technology and outpaced the development of data analysis approaches. While there are advanced tools available, including many R/BioConductor packages, these are only accessible programmatically and therefore out of reach for most experimentalists. GenePattern is a powerful genomic analysis platform with over 200 tools for analysis of gene expression, proteomics, and other data. A web-based interface provides easy access to these tools and allows the creation of automated analysis pipelines enabling reproducible research.ResultsIn order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains 34 open source GenePattern flow cytometry modules covering methods from basic processing of flow cytometry standard (i.e., FCS) files to advanced algorithms for automated identification of cell populations, normalization and quality assessment. Internally, these modules leverage from functionality developed in R/BioConductor. Using the GenePattern web-based interface, they can be connected to build analytical pipelines.ConclusionsGenePattern Flow Cytometry Suite brings advanced flow cytometry data analysis capabilities to users with minimal computer skills. Functionality previously available only to skilled bioinformaticians is now easily accessible from a web browser.

Highlights

Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data
In addition to the proposed framework, the GP Flow cytometry (FCM) GenePattern flow cytometry suite (Suite) contains several Data preprocessing modules often required before the start of data analysis
While it is becoming clear that analysis methods based on manual gating are unsuitable for the increased amount of data and simultaneously measured fluorescence parameters, they still represent the main functionality in commercial FCM data analysis software

Summary

Introduction

Traditional flow cytometry data analysis is largely based on interactive and time consuming analysis of series two dimensional representations of up to 20 dimensional data. This number increased with the recent introduction of CyTOF instruments allowing potentially up to 100 stable isotope labels in a single sample and creating up to 100 dimensional FCS data files [5] Analyzing such complex data is time consuming and human experts can miss important cell populations if these are only visible in high dimensional space not clearly distinguishable in any of the pairwise plots. For these reasons, there has been recent interest in developing new data analysis techniques that will exploit the full potential of modern flow cytometers and provide standardized, reproducible and objective analyses [6,7]. These are often created in the form of programming libraries, such as R/BioConductor [8] packages, and only accessible to sophisticated users rather than experimentalists who lack advanced programming skills

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