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

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 clos...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Habil Zare, Parisa Shooshtari, Arvind Gupta, Ryan R. Brinkman
• Keywords: BMCBI 2010, Flow Cytometry, Flow Cytometry Data, Large Datasets