The importance of flow cytometry as an analytical tool in varied research/clinical areas has widely increased over the past decade. However, flow cytometry data standards do not capture the full scope of flow cytometry experiments, which contributes to irreproducibility and unverifiability by independent researchers. The lack of standardization also prevents collaborative opportunities to recreate experimental methods and results.
To address these shortcomings, we have brought together a unique cross-disciplinary international collaborative group of bioinformaticists, computational statisticians, software developers and clinician scientists, from both academia and industry (including both software and hardware suppliers) to collaborate on development of data standards in flow cytometry. In conjunction with the ISAC Data Standards Task Force and ISAC Standards Committee, our goal is to provide consistency in the electronic recording of flow cytometry data analysis. We aim to create universal solutions for representing, collecting, annotating, archiving, analyzing and disseminating flow cytometry data, including the development of open source platform independent software tools verifying our standardization approach as well as serving as reference implementations. Results of those efforts can be obtained from the sourceforge download page of this project or accessed as described further.
This project is open to community participation. Please contact Dr. Ryan Brinkman if you would like to contribute or follow our efforts.
A standard outlining the Minimum Information about a Flow Cytometry Experiment required to report so that experimental findings can be interpreted unambiguously. MIFlowCyt is required by several journals including Cytometry A, Nature, PLOS and other. Please see our Cytometry A paper for details.
An overview of flow and image cytometry data file standards developed by our group.
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.
There are 4 major steps in flow cytometry data analysis: 1) data pre-processing, typically including cellular debris removal, compensation and data transformations; 2) cell population identification (clustering homogeneous events); 3) cell population matching (for cross sample comparison); and 4) relating cell populations to external variables (for biomarker discovery, or to target values, e.g., for clinical diagnosis). We have developed various automated tools addressing all these aspects using the R/BioConductor platform.
A Resource of Annotated Flow Cytometry Datasets Associated with Peer-Reviewed Publications
Data associated with peer-reviewed publications should be easily available and accessible; however, the rapid expansion of flow cytometry applications has outpaced the development of tools for storage, analysis, and data representation. In order to address this issue, we developed FlowRepository by extending and adapting Cytobank, an online tool for storage and collaborative analysis of cytometric data.
