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

Habil Zare, Parisa Shooshtari, Arvind Gupta, Ryan R. Brinkman

Research output: Contribution to journalArticlepeer-review

132 Scopus citations


Background: Recent 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.Results: We 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.Conclusions: This 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.

Original languageEnglish (US)
Article number403
JournalBMC bioinformatics
StatePublished - Jul 28 2010
Externally publishedYes

ASJC Scopus subject areas

  • Applied Mathematics
  • Molecular Biology
  • Structural Biology
  • Biochemistry
  • Computer Science Applications


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