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CyTOFmerge : integrating mass cytometry data across multiple panels. / Abdelaal, Tamim; Hollt, Thomas; van Unen, Vincent; Lelieveldt, Boudewijn P.F.; Koning, Frits; Reinders, Marcel; Mahfouz, Ahmed.

In: Bioinformatics, Vol. 35, No. 20, 2019, p. 4063-4071.

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@article{035d9501e58c4f769ddeaa01cff16f04,
title = "CyTOFmerge: integrating mass cytometry data across multiple panels",
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 byevaluating 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 markerswe can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.",
author = "Tamim Abdelaal and Thomas Hollt and {van Unen}, Vincent and Lelieveldt, {Boudewijn P.F.} and Frits Koning and Marcel Reinders and Ahmed Mahfouz",
year = "2019",
doi = "10.1093/bioinformatics/btz180",
language = "English",
volume = "35",
pages = "4063--4071",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "20",

}

RIS

TY - JOUR

T1 - CyTOFmerge

T2 - integrating mass cytometry data across multiple panels

AU - Abdelaal, Tamim

AU - Hollt, Thomas

AU - van Unen, Vincent

AU - Lelieveldt, Boudewijn P.F.

AU - Koning, Frits

AU - Reinders, Marcel

AU - Mahfouz, Ahmed

PY - 2019

Y1 - 2019

N2 - 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 byevaluating 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 markerswe can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.

AB - 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 byevaluating 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 markerswe can further untangle the heterogeneity of mass cytometry data, including rare cell-population detection.

UR - http://www.scopus.com/inward/record.url?scp=85073179136&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btz180

DO - 10.1093/bioinformatics/btz180

M3 - Article

VL - 35

SP - 4063

EP - 4071

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 20

ER -

ID: 56848237