CellCycleTRACER accounts for cell cycle and volume in mass cytometry data

Maria Anna Rapsomaniki, Xiao-Kang Lun, Stefan Woerner, Marco Laumanns, Bernd Bodenmiller () and María Rodríguez Martínez ()
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Maria Anna Rapsomaniki: IBM
Xiao-Kang Lun: University of Zürich
Stefan Woerner: IBM
Marco Laumanns: IBM
Bernd Bodenmiller: University of Zürich
María Rodríguez Martínez: IBM

Nature Communications, 2018, vol. 9, issue 1, 1-9

Abstract: Abstract Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03005-5

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DOI: 10.1038/s41467-018-03005-5

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