MIFlowCyt

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.

Minimum Information about a Flow Cytometry Experiment

Data File Formats

An overview of flow and image cytometry data file standards developed by our group.

Data File Standard for Flow Cytometry, Version FCS 3.1

FCS 3.1

Data File Standard for Flow Cytometry, Version FCS 3.1

Gating-ML: XML-based Gating Descriptions in Flow Cytometry

Gating-ML 1.5

Gating-ML: XML-based Gating Descriptions in Flow Cytometry

ISAC’s Gating-ML 2.0 Data Exchange Standard for Gating Description

Gating-ML 2.0

ISAC’s Gating-ML 2.0 Data Exchange Standard for Gating Description

ISAC's Classification Results File Format

CLR

ISAC's Classification Results File Format

ICEFormat - The Image Cytometry Experiment Format

ICEFormat

The Image Cytometry Experiment Format

ACS - The Archival Cytometry Standard

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.

GenePattern Flow Cytometry Suite

R/BioConductor Tools

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.

R/BioConductor Tools for Flow Cytometry Data Analysis

FlowRepository

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.

FlowRepository