FIND: A new software tool and development platform for enhanced multicolor flow analysis

BMC Bioinformatics

Software

William C Ray¹  Shareef M Dabdoub²  Sheryl S Justice³ 

[1]Biophysics Program, The Ohio State University, 2031 Physics Research Building, 191 W. Woodruff Ave., Columbus, Ohio, USA

[2]The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, 700 Childrens Dr., Columbus, Ohio, USA

[3]Biophysics Program, The Ohio State University, 2031 Physics Research Building, 191 W. Woodruff Ave., Columbus, Ohio, USA

[4]The Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, 700 Childrens Dr., Columbus, Ohio, USA

[5]Center for Microbial Pathogenesis, The Research Institute at Nationwide Children's Hospital, 700 Childrens Dr., Columbus, Ohio, USA

[6]Center for Microbial Pathogenesis, The Research Institute at Nationwide Children's Hospital, 700 Childrens Dr., Columbus, Ohio, USA

关键词: Pane;    Protein Structure Prediction;    Flow Cytometry Data;    Scalable Vector Graphic;    Context Menu;   

DOI  :  10.1186/1471-2105-12-145

 received in 2010-11-03, accepted in 2011-05-10,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundFlow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use.ResultsWe have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.ConclusionsThe FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.

【 授权许可】

CC BY   
© Dabdoub et al; licensee BioMed Central Ltd. 2011