Журнал протеомики и биоинформатики
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ISSN: 0974-276X
ISSN: 0974-276X
Anna-Maria Lahesmaa-Korpinen, Sari E. Jalkanen, Ping Chen, Erkka Valo, Javier Núñez-Fontarnau, Ville Rantanen, Ali Oghabian, Jukka Vakkila, Kimmo Porkka, Satu Mustjoki and Sampsa Hautaniemi
Flow cytometry is a widely used high-throughput measurement technology in basic research and diagnostics. Recently the amount of data generated from flow cytometry experiments has been increasing, both in sample numbers and the number of parameters measured per cell. These highly multivariate datasets have become too large for use with tools depending mainly on manual analysis. We have implemented a computational framework (FlowAnd) that is designed to analyze and integrate largescale, multi-color flow cytometry data. The tool implements methods for data importing, various transformations, several clustering algorithms for automatic clustering, visualization tools as well as straightforward statistical testing. We applied FlowAnd to a phosphoproteomics data set from 37 chronic myeloid leukemia patients treated with two kinase inhibitors. Our results indicate high concordance between automated gating using three clustering algorithms and manual gating. Analysis of more than 70 flow cytometry experiments demonstrate the utility of features in FlowAnd, such as a graphical tool for rapid validation of clustering results, in large-scale flow cytometry data analysis. The FlowAnd framework allows accurate, fast and well documented analysis of multidimensional flow cytometry experiments. It provides several clustering algorithms for automatic gating, the possibility to add novel tools in various programming languages, such as Java, R, Python or MATLAB in an environment amenable to high-performance computing. FlowAnd can also be easily modified to comply with various marker panels and parameter settings. FlowAnd, all data and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/flowand.