Computational methods for evaluation of cell-based data assessment--Bioconductor.
Recent advances in miniaturization and automation of technologies have enabled cell-based assay high-throughput screening, bringing along new challenges in data analysis. Automation, standardization, reproducibility have become requirements for qualitative research. The Bioconductor community has worked in that direction proposing several R packages to handle high-throughput data including flow cytometry (FCM) experiment. Altogether, these packages cover the main steps of a FCM analysis workflow, that is, data management, quality assessment, normalization, outlier detection, automated gating, cluster labeling, and feature extraction. Additionally, the open-source philosophy of R and Bioconductor, which offers room for new development, continuously drives research and improvement of theses analysis methods, especially in the field of clustering and data mining. This review presents the principal FCM packages currently available in R and Bioconductor, their advantages and their limits.
Department of Epidemiology and Biostatistics, EHESP, F-35043 Rennes, France. nlemeur@gmail.com
Nolwenn
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Le Meur
D001185:Artificial Intelligence; D001331:Automation / Q000379:methods; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005434:Flow Cytometry / Q000379:methods; D057166:High-Throughput Screening Assays / Q000379:methods* / Q000592:standards; D011786:Quality Control; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000592:standards*