Flow cytometry (FCM) is an important technique used in basic and clinical research for studying the immunological status of patients treated with vaccines or other immunotherapies, as well as for characterizing cancer, HIV infection and other diseases. Current data standards lack sufficient depth to represent the full scope of FCM experiments and there is considerable demand for tools that can organize FCM analyses into databases and assist in the exploration and analysis of large datasets. We therefore propose to implement a standardized approach to capturing, analyzing, and disseminating FCM data by building and extending novel and existing open source software tools. To facilitate data management, initial work will focus on developing a community-based guideline for recording and reporting the details of FCM experiments. Platform-independent software implementations of this standard will be created to facilitate data exchange between both software components and collaborative groups, enabling reproducible and extensible data analyses. Statistical tools with reference implementations will also be developed to support more complex visualization and analyses of large data sets in a high throughput fashion. As well, novel tools for electronic collaboration will be developed to facilitate the integrated access and comprehension of analysis results, data and supporting software to empower users to be able to evaluate, reproduce, and extend original analyses. The overriding hypothesis is that joint development of bioinformatics standards and software tools for FCM will greatly facilitate both basic and clinical research of multiparametric cell analyses in human disease areas that depend upon this technique. We anticipate that this work will also stimulate the development of further commercial and academic implementations.
| von Rossum, Anna; Enns, Winnie; Shi, Yu P et al. (2014) Bim regulates alloimmune-mediated vascular injury through effects on T-cell activation and death. Arterioscler Thromb Vasc Biol 34:1290-7 |
| Zare, Habil; Haffari, Gholamreza; Gupta, Arvind et al. (2013) Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis. BMC Genomics 14 Suppl 1:S14 |
| Zare, Habil; Bashashati, Ali; Kridel, Robert et al. (2012) Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma. Am J Clin Pathol 137:75-85 |
| Aghaeepour, Nima; Nikolic, Radina; Hoos, Holger H et al. (2011) Rapid cell population identification in flow cytometry data. Cytometry A 79:6-13 |
| Zare, Habil; Shooshtari, Parisa; Gupta, Arvind et al. (2010) Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics 11:403 |
| Roussel, Mikael; Benard, Cyrille; Ly-Sunnaram, Beatrice et al. (2010) Refining the white blood cell differential: the first flow cytometry routine application. Cytometry A 77:552-63 |
| Spidlen, Josef; Moore, Wayne; Parks, David et al. (2010) Data File Standard for Flow Cytometry, version FCS 3.1. Cytometry A 77:97-100 |
| Hahne, Florian; Khodabakhshi, Alireza Hadj; Bashashati, Ali et al. (2010) Per-channel basis normalization methods for flow cytometry data. Cytometry A 77:121-31 |
| Blimkie, Darren; Fortuno 3rd, Edgardo S; Thommai, Francis et al. (2010) Identification of B cells through negative gating-An example of the MIFlowCyt standard applied. Cytometry A 77:546-51 |
| Lo, Kenneth; Hahne, Florian; Brinkman, Ryan R et al. (2009) flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 10:145 |
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