flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding

@article{Ge2012flowPeaksAF,
  title={flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding},
  author={Yongchao Ge and Stuart C. Sealfon},
  journal={Bioinformatics},
  year={2012},
  volume={28 15},
  pages={2052-8}
}
MOTIVATION For flow cytometry data, there are two common approaches to the unsupervised clustering problem: one is based on the finite mixture model and the other on spatial exploration of the histograms. The former is computationally slow and has difficulty to identify clusters of irregular shapes. The latter approach cannot be applied directly to high-dimensional data as the computational time and memory become unmanageable and the estimated histogram is unreliable. An algorithm without these… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 22 references

Rapid cell population identification in flow cytometry data.

Cytometry. Part A : the journal of the International Society for Analytical Cytology • 2011
View 5 Excerpts
Highly Influenced

Automated gating of flow cytometry data via robust model-based clustering.

Cytometry. Part A : the journal of the International Society for Analytical Cytology • 2008
View 1 Excerpt

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