Comparison of multidimensional flow cytometric data by a novel data mining technique

James F. Leary, Jacob Smith, Peter Szaniszlo, Lisa M. Reece

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Most flow/image cytometric data analysis methods look for clusters in the data corresponding to specific cell subpopulations. Comparisons between different cytometry datafiles often use human pattern recognition visualization of all the different combinations of variables ("parameters") two at a time in so-called bivariate scattergrams. Not only is this tedious, but it can miss potential clusters due to projection of higher dimensional dataspaces down onto two dimensional planes making them indiscernible as separate clusters. Novel data mining algorithms, implemented in software allow for the comparison of two or more higher dimensional datafiles without the requirement for reduction of dimensionality for human visualization. Of equal importance is the comparison of higher dimensional clusters which may move around slightly in space, yet still be "similar" according to algorithms which provide measures of similarity. This software, written in C/C++ and currently implemented in software with a Windows graphical user interface, allows for direct reading of FCS2.0 format flow cytometry datafiles of any number of parameters. In a few minutes or less, complex multiparameter data of two or more files can be compared on a personal computer or workstation. The software operates in either supervised or unsupervised mode, depending on whether the user wishes to include prior user knowledge or in a data mining discovery mode. Differences between these files can be exported as sub-datafiles which can be further analyzed using any other software that can read FCS2.0 data format.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume6441
DOIs
StatePublished - 2007
EventImaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues V - San Jose, CA, United States
Duration: Jan 22 2007Jan 24 2007

Other

OtherImaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues V
CountryUnited States
CitySan Jose, CA
Period1/22/071/24/07

Fingerprint

Data mining
Visualization
Computer workstations
Flow cytometry
Graphical user interfaces
Personal computers
Pattern recognition

Keywords

  • Cluster analysis
  • Data mining
  • Exploratory data analysis
  • Flow cytometry
  • Subtractive clustering

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Leary, J. F., Smith, J., Szaniszlo, P., & Reece, L. M. (2007). Comparison of multidimensional flow cytometric data by a novel data mining technique. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 6441). [64410N] https://doi.org/10.1117/12.700940

Comparison of multidimensional flow cytometric data by a novel data mining technique. / Leary, James F.; Smith, Jacob; Szaniszlo, Peter; Reece, Lisa M.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 6441 2007. 64410N.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Leary, JF, Smith, J, Szaniszlo, P & Reece, LM 2007, Comparison of multidimensional flow cytometric data by a novel data mining technique. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 6441, 64410N, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues V, San Jose, CA, United States, 1/22/07. https://doi.org/10.1117/12.700940
Leary JF, Smith J, Szaniszlo P, Reece LM. Comparison of multidimensional flow cytometric data by a novel data mining technique. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 6441. 2007. 64410N https://doi.org/10.1117/12.700940
Leary, James F. ; Smith, Jacob ; Szaniszlo, Peter ; Reece, Lisa M. / Comparison of multidimensional flow cytometric data by a novel data mining technique. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 6441 2007.
@inproceedings{99538c70806f4f06a1f367d728608686,
title = "Comparison of multidimensional flow cytometric data by a novel data mining technique",
abstract = "Most flow/image cytometric data analysis methods look for clusters in the data corresponding to specific cell subpopulations. Comparisons between different cytometry datafiles often use human pattern recognition visualization of all the different combinations of variables ({"}parameters{"}) two at a time in so-called bivariate scattergrams. Not only is this tedious, but it can miss potential clusters due to projection of higher dimensional dataspaces down onto two dimensional planes making them indiscernible as separate clusters. Novel data mining algorithms, implemented in software allow for the comparison of two or more higher dimensional datafiles without the requirement for reduction of dimensionality for human visualization. Of equal importance is the comparison of higher dimensional clusters which may move around slightly in space, yet still be {"}similar{"} according to algorithms which provide measures of similarity. This software, written in C/C++ and currently implemented in software with a Windows graphical user interface, allows for direct reading of FCS2.0 format flow cytometry datafiles of any number of parameters. In a few minutes or less, complex multiparameter data of two or more files can be compared on a personal computer or workstation. The software operates in either supervised or unsupervised mode, depending on whether the user wishes to include prior user knowledge or in a data mining discovery mode. Differences between these files can be exported as sub-datafiles which can be further analyzed using any other software that can read FCS2.0 data format.",
keywords = "Cluster analysis, Data mining, Exploratory data analysis, Flow cytometry, Subtractive clustering",
author = "Leary, {James F.} and Jacob Smith and Peter Szaniszlo and Reece, {Lisa M.}",
year = "2007",
doi = "10.1117/12.700940",
language = "English (US)",
isbn = "0819465542",
volume = "6441",
booktitle = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

}

TY - GEN

T1 - Comparison of multidimensional flow cytometric data by a novel data mining technique

AU - Leary, James F.

AU - Smith, Jacob

AU - Szaniszlo, Peter

AU - Reece, Lisa M.

PY - 2007

Y1 - 2007

N2 - Most flow/image cytometric data analysis methods look for clusters in the data corresponding to specific cell subpopulations. Comparisons between different cytometry datafiles often use human pattern recognition visualization of all the different combinations of variables ("parameters") two at a time in so-called bivariate scattergrams. Not only is this tedious, but it can miss potential clusters due to projection of higher dimensional dataspaces down onto two dimensional planes making them indiscernible as separate clusters. Novel data mining algorithms, implemented in software allow for the comparison of two or more higher dimensional datafiles without the requirement for reduction of dimensionality for human visualization. Of equal importance is the comparison of higher dimensional clusters which may move around slightly in space, yet still be "similar" according to algorithms which provide measures of similarity. This software, written in C/C++ and currently implemented in software with a Windows graphical user interface, allows for direct reading of FCS2.0 format flow cytometry datafiles of any number of parameters. In a few minutes or less, complex multiparameter data of two or more files can be compared on a personal computer or workstation. The software operates in either supervised or unsupervised mode, depending on whether the user wishes to include prior user knowledge or in a data mining discovery mode. Differences between these files can be exported as sub-datafiles which can be further analyzed using any other software that can read FCS2.0 data format.

AB - Most flow/image cytometric data analysis methods look for clusters in the data corresponding to specific cell subpopulations. Comparisons between different cytometry datafiles often use human pattern recognition visualization of all the different combinations of variables ("parameters") two at a time in so-called bivariate scattergrams. Not only is this tedious, but it can miss potential clusters due to projection of higher dimensional dataspaces down onto two dimensional planes making them indiscernible as separate clusters. Novel data mining algorithms, implemented in software allow for the comparison of two or more higher dimensional datafiles without the requirement for reduction of dimensionality for human visualization. Of equal importance is the comparison of higher dimensional clusters which may move around slightly in space, yet still be "similar" according to algorithms which provide measures of similarity. This software, written in C/C++ and currently implemented in software with a Windows graphical user interface, allows for direct reading of FCS2.0 format flow cytometry datafiles of any number of parameters. In a few minutes or less, complex multiparameter data of two or more files can be compared on a personal computer or workstation. The software operates in either supervised or unsupervised mode, depending on whether the user wishes to include prior user knowledge or in a data mining discovery mode. Differences between these files can be exported as sub-datafiles which can be further analyzed using any other software that can read FCS2.0 data format.

KW - Cluster analysis

KW - Data mining

KW - Exploratory data analysis

KW - Flow cytometry

KW - Subtractive clustering

UR - http://www.scopus.com/inward/record.url?scp=34247370271&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34247370271&partnerID=8YFLogxK

U2 - 10.1117/12.700940

DO - 10.1117/12.700940

M3 - Conference contribution

SN - 0819465542

SN - 9780819465542

VL - 6441

BT - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

ER -