We are pleased to announce the call for flow cytometry data sets to support the FlowCAP2 challenges. The goal of FlowCAP2 is to assess computational approaches for flow cytometry data analysis, including automated gating, data normalization and classification. We are asking the community to support this project by providing high dimensional, complex and feature-rich datasets on which the automated methods will be evaluated. Not only will you be contributing to the development of automated analysis methodologies, but your data will be analyzed by leading research groups and their results (e.g., possible novel biomarkers) will be provided back to you. If you are interested in participating as either a data provider or later as a participant, please email the organizers through flowcap@googlegroups.com<mailto:flowcap@googlegroups.com>.

The rapid expansion of flow cytometry applications has outpaced the functionality of traditional analysis tools used to interpret flow cytometry data such that scientists are faced with the daunting prospect of manually identifying interesting cell populations in 20 dimensional data from a collection of millions of cells. For this reason reliable automated approaches to flow cytometric analysis are desirable. While there has been a growing interest among the scientific community in developing such methods, guidance for end users about appropriate use and application of these methods is scarce.

In response to this need, we are conducting the second Flow Cytometry: Critical Assessment of Population Identification Methods challenge (FlowCAP2). The first set of FlowCAP challenges (See the project website at 
flowcap.flowsite.org<http://flowcap.flowsite.org/> for more information) provided the means to objectively test approaches for the identification of cell populations of interest in flow cytometry data by comparison to manual analysis by experts using common datasets. We are extending this evaluation to include evaluation of datasets where some secondary outcome information (e.g., survival time, disease diagnosis) is available on the datasets in question. We hope this will facilitate an objective evaluation of analysis methods. However, complex, manually analyzed datasets (e.g., those from vaccination, immune response or rare populations studies) are still of interest for some of the planed challenges and we ask those groups generating such datasets contact the organizing committee. Anonymized datasets, including de-identified markers are welcome, and a further description of biological and technical dataset examples of interest is included at the end of this email.

An NIH/NIAID-sponsored summit will be held at the NIH campus in September 2011. FlowCAP participants will be invited to present their work and participate in group discussions.

Cheers,
Ryan Brinkman on behalf of the FlowCAP Organizing Committee:

Ryan Brinkman, British Columbia Cancer Agency
Raphael Gottardo, Fred Hutchinson Cancer Research Center
Tim Mosmann, University of Rochester
Richard H. Scheuermann, University of Texas Southwestern Medical Center
Jill Schoenfeld, TreeStar Inc.




Example FlowCAP biological investigation datasets:

1. “The Knockout Mouse Use Case” - analysis of primary and secondary lymphoid organs for evidence of deviations from normal lymphoid development – bone marrow, thymus, lymph node, spleen, peripheral blood, Peyer’s patch, skin. Marker panels: thymocyte panel (CD3, CD4, CD8, CD25, CD44), preB cell panel, T cell panel, B cell panel, Dendritic cell panel

2. In vivo immune response to vaccination/infection in human subjects – usually PBMC samples taken before and at various time points after vaccination. May be used to test the efficacy of different vaccine formulations (relatively small numbers of subjects) or to look for differences in immune responses due to the effects of genetic polymorphisms (large numbers, thousands, of subjects). Responding cells would represent a small proportion of the total PBMC, often <1%.

3. In vitro immune responses – in vitro stimulations with more granular timepoints. In vitro culturing will change the distribution of cell types in comparison with the primary specimen, an often include an enrichment for the cell type of interest. The responding relevant cell type if often absent from the unstimulated sample.

4. Disease diagnosis – usually used for diagnosis of leukemia, lymphoma or some kind of myeloproliferative disorder. The diagnostic specimen (PBMC, bone marrow aspirate or lymph node) is often quite distinct from a normal specimen in terms of the presence of an abnormal cell type or a significant skewing in the proportion of a cell with relatively normal expression patterns.

5. Disease progression/minimal residual disease detection – this use case involve the detection and monitoring of leukemia or lymphoma in peripheral blood. The goal is to push the limits of detection (<.01%). The phenotypic characteristics of the abnormal cell are often known from the diagnostic specimen in which the abnormal cells are present in abundance.

6. Therapeutic efficacy – this use case could be an example of a cellular biomarker discovery project or a supervised type of analysis. As a cellular biomarker project, the idea would be to identify all unique cell types from the dataset and to determine if any of them correlate with some other measure of therapeutic response (outcome measure) that would validate them as a biomarker of therapeutic response.

7. Rare population detection – (e.g., stem cell detection).


Example FlowCAP technical investigation datasets:

1. Technical variability (e.g., laser drift, reagent lot effects, site differences)

2. Large number of events, >1,000,000 (more is better)

3. Large number of markers, >12 (more is better)

4. Integration of data with from independent samples with partially overlapping staining/marker panels