GenePattern flow cytometry suite
Name
1751-0473-8-14.pdf
Size
703.53 KB
Format
Adobe PDF
Checksum (MD5)
6bee8475f1083efbef2366305a75f193
Author(s)
Spidlen, Josef
Barsky, Aaron
Breuer, Karin
Carr, Peter
Nazaire, Marc-Danie
Hill, Barbara
Qian, Yu
Liefeld, Ted
Reich, Michael
Wilkinson, Peter
Date Issued
July 2013
Journal
Source Code for Biology and Medicine
Publisher
BioMed Central Ltd
Citation
Spidlen, Josef et al. “GenePattern Flow Cytometry Suite.” Source Code for Biology and Medicine 8.1 (2013): 14.
Version
Final published version
Abstract
Background:
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. 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.
Results:
In 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.
Conclusions:
GenePattern 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.
MIT Department
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/1751-0473-8-14
