SWIFT—scalable clustering for automated identification of rare cell populations in large, high‐dimensional flow cytometry datasets, Part 1: Algorithm design

• Published in: Cytometry. Part A Vol. 85; no. 5; pp. 408 - 421
• Main Authors: Naim, Iftekhar, Datta, Suprakash, Rebhahn, Jonathan, Cavenaugh, James S., Mosmann, Tim R., Sharma, Gaurav
• Format: Journal Article
• Language: English
• Published: United States BlackWell Publishing Ltd 01.05.2014
• Subjects: Algorithms; automated multivariate clustering; Cell Lineage; Cluster Analysis; Computational Biology; Flow Cytometry - methods; Gaussian mixture models; ground truth data; Models, Theoretical; Original; rare subpopulation detection; weighted sampling; weighted sampling; Gaussian mixture models; rare subpopulation detection; ground truth data; automated multivariate clustering
• ISSN: 1552-4922; 1552-4930; 1552-4930
• DOI: 10.1002/cyto.a.22446

We present a model‐based clustering method, SWIFT (Scalable Weighted Iterative Flow‐clustering Technique), for digesting high‐dimensional large‐sized datasets obtained via modern flow cytometry into more compact representations that are well‐suited for further automated or manual analysis. Key attributes of the method include the following: (a) the analysis is conducted in the multidimensional space retaining the semantics of the data, (b) an iterative weighted sampling procedure is utilized to maintain modest computational complexity and to retain discrimination of extremely small subpopulations (hundreds of cells from datasets containing tens of millions), and (c) a splitting and merging procedure is incorporated in the algorithm to preserve distinguishability between biologically distinct populations, while still providing a significant compaction relative to the original data. This article presents a detailed algorithmic description of SWIFT, outlining the application‐driven motivations for the different design choices, a discussion of computational complexity of the different steps, and results obtained with SWIFT for synthetic data and relatively simple experimental data that allow validation of the desirable attributes. A companion paper (Part 2) highlights the use of SWIFT, in combination with additional computational tools, for more challenging biological problems. © 2014 The Authors. Published by Wiley Periodicals Inc.