Modeling of cytometry data in logarithmic space: When is a bimodal distribution not bimodal?

Amir Erez, Robert Vogel, Andrew Mugler, Andrew Belmonte, Grégoire Altan-Bonnet

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Recent efforts in systems immunology lead researchers to build quantitative models of cell activation and differentiation. One goal is to account for the distributions of proteins from single-cell measurements by flow cytometry or mass cytometry as readout of biological regulation. In that context, large cell-to-cell variability is often observed in biological quantities. We show here that these readouts, viewed in logarithmic scale may result in two easily-distinguishable modes, while the underlying distribution (in linear scale) is unimodal. We introduce a simple mathematical test to highlight this mismatch. We then dissect the flow of influence of cell-to-cell variability proposing a graphical model which motivates higher-dimensional analysis of the data. Finally we show how acquiring additional biological information can be used to reduce uncertainty introduced by cell-to-cell variability, helping to clarify whether the data is uni- or bimodal. This communication has cautionary implications for manual and automatic gating strategies, as well as clustering and modeling of single-cell measurements.

Original languageEnglish (US)
Pages (from-to)611-619
Number of pages9
JournalCytometry Part A
Issue number6
StatePublished - Jun 2018

All Science Journal Classification (ASJC) codes

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology


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