QFMatch: multidimensional flow and mass cytometry samples alignment

• Type of Material: Article
• Authors: Darya Y. Orlova, Stanford University; Stephen Meehan, Stanford University; David Parks, Stanford University; Wayne A. Moore, Stanford University; Connor Meehan, California Institute of Technology; Qian Zhao, Stanford University; Eliver Ghosn, Emory University; Leonore A. Herzenberg, Stanford University; Guenther Walther, Stanford University
• Language: English
• Date: 2018-02-19
• Publisher: Nature Publishing Group: Open Access Journals - Option C
• Publication Version: Final Published Version
• Copyright Statement: © 2018 The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-018-21444-4
• Title of Journal or Parent Work: Scientific Reports
• ISSN: 2045-2322
• Volume: 8
• Issue: 1
• Start Page: 3291
• End Page: 3291
• Grant/Funding Information: This work was supported by NIH Training Grant [5 T32 AI007290-32].
• Keywords: Multidisciplinary Sciences; Science & Technology - Other Topics; QUADRATIC FORM; Science & Technology
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Part of the flow/mass cytometry data analysis process is aligning (matching) cell subsets between relevant samples. Current methods address this cluster-matching problem in ways that are either computationally expensive, affected by the curse of dimensionality, or fail when population patterns significantly vary between samples. Here, we introduce a quadratic form (QF)-based cluster matching algorithm (QFMatch) that is computationally efficient and accommodates cases where population locations differ significantly (or even disappear or appear) from sample to sample. We demonstrate the effectiveness of QFMatch by evaluating sample datasets from immunology studies. The algorithm is based on a novel multivariate extension of the quadratic form distance for the comparison of flow cytometry data sets. We show that this QF distance has attractive computational and statistical properties that make it well suited for analysis tasks that involve the comparison of flow/mass cytometry samples.