Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Erkka Valo
Finland
Research Article
Flow And: Comprehensive Computational Framework for Flow Cytometry Data Analysis
Author(s): 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 HautaniemiAnna-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.. View More»
DOI:
10.4172/jpb.1000197