Data reduction for spectral clustering to analyze high throughput flow cytometry data |
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| Authors: | Habil Zare Parisa Shooshtari Arvind Gupta Ryan R Brinkman |
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| Affiliation: | (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) Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada |
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| Abstract: | 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. |
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