Cluster Analysis of Flow Cytometric List Mode Data on a Personal Computer

Tom C. Bakker schut, Bart G. de Grooth, Jan Greve

    Research output: Contribution to journalArticleAcademicpeer-review

    22 Citations (Scopus)
    76 Downloads (Pure)

    Abstract

    A cluster analysis algorithm, dedicated to analysis of flow cytometric data is described. The algorithm is written in Pascal and implemented on an MS-DOS personal computer. It uses k-means, initialized with a large number of seed points, followed by a modified nearest neighbor technique to reduce the large number of subclusters. Thus we combine the advantage of the k-means (speed) with that of the nearest neighbor technique (accuracy). In order to achieve a rapid analysis, no complex data transformations such as principal components analysis were used. Results of the cluster analysis on both real and artificial flow cytometric data are presented and discussed. The results show that it is possible to get very good cluster analysis partitions, which compare favorably with manually gated analysis in both time and in reliability, using a personal computer.
    Original languageEnglish
    Pages (from-to)649-659
    Number of pages11
    JournalCytometry
    Volume14
    Issue number1
    DOIs
    Publication statusPublished - 1993

    Keywords

    • Data Analysis
    • k-Means
    • Software

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