CyTOFmerge
integrating mass cytometry data across multiple panels
Tamim Abdelaal (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)
Thomas Hollt (TU Delft - Computer Graphics and Visualisation, Leiden University Medical Center)
Vincent van Unen (Leiden University Medical Center)
Boudewijn P.F. Lelieveldt (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)
Frits Koning (Leiden University Medical Center)
Marcel Reinders (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)
Ahmed Mahfouz (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
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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.
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