Automatic Clustering of Flow Cytometry Data with Density-Based Merging
نویسندگان
چکیده
منابع مشابه
Automatic Clustering of Flow Cytometry Data with Density-Based Merging
The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow c...
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Flow cytometry technique produces large, multidimensional datasets of individual cells that are helpful for biomedical science and clinical research. Given the size of the data, efficient computational analysis techniques are necessary to assist researchers in understanding and interpreting the data. Automatic cluster analysis of samples based on flow cytometry data is a powerful and promising ...
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Clustering is an important data exploration task. Its use in data mining is growing very fast. Traditional clustering algorithms which no longer cater to the data mining requirements are modified increasingly. Clustering algorithms are numerous which can be divided in several categories. Two prominent categories are distance-based and density-based (e.g. K-means and DBSCAN, respectively). While...
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ژورنال
عنوان ژورنال: Advances in Bioinformatics
سال: 2009
ISSN: 1687-8027,1687-8035
DOI: 10.1155/2009/686759