SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 2: Biological evaluation

A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high‐dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, the...

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Bibliographic Details
Published in:	Cytometry. Part A Vol. 85; no. 5; pp. 422 - 433
Main Authors:	Mosmann, Tim R., Naim, Iftekhar, Rebhahn, Jonathan, Datta, Suprakash, Cavenaugh, James S., Weaver, Jason M., Sharma, Gaurav
Format:	Journal Article
Language:	English
Published:	United States BlackWell Publishing Ltd 01.05.2014
Subjects:	Algorithms Antigens - blood Antigens - immunology automated analysis Blood Cells - cytology Blood Cells - immunology Cell Lineage Cluster Analysis Computational Biology EM algorithm Flow Cytometry - methods flow cytometry clustering ground truth data Humans Normal Distribution Original SWIFT T-Lymphocytes - cytology T-Lymphocytes - immunology SWIFT ground truth data automated analysis EM algorithm flow cytometry clustering
ISSN:	1552-4922, 1552-4930, 1552-4930
Online Access:	Get full text
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