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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Vydané v:Cytometry. Part A Ročník 85; číslo 5; s. 422 - 433
Hlavní autori: Mosmann, Tim R., Naim, Iftekhar, Rebhahn, Jonathan, Datta, Suprakash, Cavenaugh, James S., Weaver, Jason M., Sharma, Gaurav
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States BlackWell Publishing Ltd 01.05.2014
Predmet:
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
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Shrnutí: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, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non‐Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen‐stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine‐producing influenza‐specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT. © 2014 The Authors. Published by Wiley Periodicals Inc.
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ISSN:1552-4922
1552-4930
1552-4930
DOI:10.1002/cyto.a.22445