Rapid Cell Population Identification in Flow Cytometry Data

被引：153

作者：

Aghaeepour, Nima ^{[2
,3
]}

Nikolic, Radina ^{[2
,4
]}

Hoos, Holger H. ^{[5
]}

Brinkman, Ryan R. ^{[1
,2
]}

机构：

[1] Univ British Columbia, Dept Med Genet, Vancouver, BC V5Z 1M9, Canada

[2] BC Canc Agcy, Terry Fox Lab, Vancouver, BC, Canada

[3] Univ British Columbia, Dept Bioinformat, Vancouver, BC V5Z 1M9, Canada

[4] Univ Oxford, Dept Stat, Oxford OX1 3TG, England

[5] Univ British Columbia, Dept Comp Sci, Vancouver, BC V5Z 1M9, Canada

来源：

CYTOMETRY PART A | 2011年 / 79A卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

flow cytometry; data analysis; cluster analysis; model selection; bioinformatics; statistics; AUTOMATION; REDUCTION;

D O I：

10.1002/cyto.a.21007

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor. (C) 2010 International Society for Advancement of Cytometry

引用

页码：6 / 13

页数：8

共 26 条

[1]

Aghaeepour N, 2009, CLUST THEOR WORKSH N

[2]

Aghaeepour N, 2010, FLOWMEANS PACKAGE BI

[3]

[Anonymous], 2000, ICML

[4]

Bashashati Ali, 2009, Advances in Bioinformatics, V2009, P584603, DOI 10.1155/2009/584603

[5] Combining Mixture Components for Clustering [J].

Baudry, Jean-Patrick ;

Raftery, Adrian E. ;

Celeux, Gilles ;

Lo, Kenneth ;

Gottardo, Raphael .

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2010, 19 (02) :332-353

[6] Toward automation of flow data analysis [J].

Bocsi, Jozef ;

Tarnok, Attila .

CYTOMETRY PART A, 2008, 73A (08) :679-680

[7] High-content flow cytometry and temporal data analysis for defining a cellular signature graft-versus-host disease [J].

Brinkman, Ryan Remy ;

Gasparetto, Maura ;

Lee, Shang-Jung Jessica ;

Ribickas, Albert J. ;

Perkins, Janelle ;

Janssen, William ;

Smiley, Renee ;

Smith, Clay .

BIOLOGY OF BLOOD AND MARROW TRANSPLANTATION, 2007, 13 (06) :691-700

[8] Good Cell, Bad Cell: Flow Cytometry Reveals T-cell Subsets Important in HIV Disease [J].

Chattopadhyay, Pratip K. ;

Roederer, Mario .

CYTOMETRY PART A, 2010, 77A (07) :614-622

[9] Feature significance for multivariate kernel density estimation [J].

Duong, Tam ;

Cowling, Arianna ;

Koch, Inge ;

Wand, M. P. .

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2008, 52 (09) :4225-4242

[10]

Finak Greg, 2009, Advances in Bioinformatics, V2009, P247646, DOI 10.1155/2009/247646

← 1 2 3 →