An overview of flow and image cytometry data file standards developed by our group.
Data File Standard for Flow Cytometry, Version FCS 3.1
Gating-ML: XML-based Gating Descriptions in Flow Cytometry
ISAC’s Gating-ML 2.0 Data Exchange Standard for Gating Description
ISAC's Classification Results File Format
The Image Cytometry Experiment Format
ACS (in progress)
The Archival Cytometry Standard
GenePattern Flow Cytometry Suite
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. In order to bring advanced flow cytometry data analysis tools to experimentalists without programmatic skills, we developed the GenePattern Flow Cytometry Suite. It contains over 30 open source modules covering methods from basic processing of FCS files to advanced algorithms for automated identification of cell populations, normalization and quality assessment.