Accurate automated clustering of two-dimensional data for single-nucleotide polymorphism genotyping by a combination of clustering methods: Evaluation by large-scale real data

Shuichi Takitoh, Shogo Fujii, Yoichi Mase, Junichi Takasaki, Toshimasa Yamazaki, Yozo Ohnishi, Masao Yanagisawa, Yusuke Nakamura, Naoyuki Kamatani

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    Abstract

    Motivation: The Invader assay is a fluorescence-based high-throughput genotyping technology. If the output data from the Invader assay were classified automatically, then genotypes for individuals would be determined efficiently. However, existing classification methods do not necessarily yield results with the same accuracy as can be achieved by technicians. Our clustering algorithm, Genocluster, is intended to increase the proportion of data points that need not be manually corrected by technicians. Results: Genocluster worked well even when the number of clusters was unknown in advance and when there were only a few points in a cluster. The use of Genocluster enabled us to achieve an acceptance rate (proportion of assay results that did not need to be corrected by expert technicians) of 84.4% and a proportion of uncorrected points of 95.8%, as determined using the data from over 31 million points.

    Original languageEnglish
    Pages (from-to)408-413
    Number of pages6
    JournalBioinformatics
    Volume23
    Issue number4
    DOIs
    Publication statusPublished - 2007 Feb 15

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    ASJC Scopus subject areas

    • Clinical Biochemistry
    • Computer Science Applications
    • Computational Theory and Mathematics

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