Data reduction for spectral clustering to analyze high throughput flow cytometry data

BMC Bioinformatics

Methodology Article

Habil Zare¹  Parisa Shooshtari¹  Arvind Gupta²  Ryan R Brinkman³ 

[1]Department of Computing Science, University of British Columbia, Vancouver, BC, Canada

[2]Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Ave, Vancouver, BC, Canada

[3]Faculty of Science, University of British Columbia, Vancouver, BC, Canada

[4]Terry Fox Laboratory, BC Cancer Agency, 675 W 10th Ave, Vancouver, BC, Canada

[5]Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada

关键词: Telomere Length;    Graft Versus Host Disease;    Spectral Cluster;    Flow Cytometry Data;    Spectral Cluster Algorithm;   

DOI  :  10.1186/1471-2105-11-403

 received in 2009-12-21, accepted in 2010-07-28,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundRecent biological discoveries have shown that clustering large datasets is essential for better understanding biology in many areas. Spectral clustering in particular has proven to be a powerful tool amenable for many applications. However, it cannot be directly applied to large datasets due to time and memory limitations. To address this issue, we have modified spectral clustering by adding an information preserving sampling procedure and applying a post-processing stage. We call this entire algorithm SamSPECTRAL.ResultsWe tested our algorithm on flow cytometry data as an example of large, multidimensional data containing potentially hundreds of thousands of data points (i.e., "events" in flow cytometry, typically corresponding to cells). Compared to two state of the art model-based flow cytometry clustering methods, SamSPECTRAL demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations.ConclusionsThis work is the first successful attempt to apply spectral methodology on flow cytometry data. An implementation of our algorithm as an R package is freely available through BioConductor.

【 授权许可】

Unknown   
© Zare et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.