Library
Repository Home account_circle Login

CyTOFmerge

integrating mass cytometry data across multiple panels

Journal Article (2019)
Author(s)

Tamim Abdelaal (Leiden University Medical Center, TU Delft - Electrical Engineering, Mathematics and Computer Science)

Thomas Hollt (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)

Vincent van Unen (Leiden University Medical Center)

Boudewijn P.F. Lelieveldt (Leiden University Medical Center, TU Delft - Electrical Engineering, Mathematics and Computer Science)

Frits Koning (Leiden University Medical Center)

Marcel Reinders (Leiden University Medical Center, TU Delft - Electrical Engineering, Mathematics and Computer Science)

Ahmed Mahfouz (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)

Research Group
Pattern Recognition and Bioinformatics
DOI related publication
https://doi.org/10.1093/bioinformatics/btz180 Final published version
More Info
expand_more
Publication Year
2019
Language
English
Related content
Research Group
Pattern Recognition and Bioinformatics
Issue number
20
Volume number
35
Pages (from-to)
4063-4071
Downloads counter
354
Collections
Institutional Repository
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Motivation: High-dimensional mass cytometry (CyTOF) allows the simultaneous measurement of multiple cellular markers at single-cell level, providing a comprehensive view of cell compositions.
However, the power of CyTOF to explore the full heterogeneity of a biological sample at the singlecell level is currently limited by the number of markers measured simultaneously on a single panel.
Results: To extend the number of markers per cell, we propose an in silico method to integrate CyTOF datasets measured using multiple panels that share a set of markers. Additionally, we present an approach to select the most informative markers from an existing CyTOF dataset to be used as a shared marker set between panels. We demonstrate the feasibility of our methods by
evaluating the quality of clustering and neighborhood preservation of the integrated dataset, on two public CyTOF datasets. We illustrate that by computationally extending the number of markers
we can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.