PT  - JOURNAL ARTICLE
AU  - Amir Erez
AU  - Robert Vogel
AU  - Andrew Mugler
AU  - Andrew Belmonte
AU  - Grégoire Altan-Bonnet
TI  - Modeling of cytometry data in logarithmic space: when is a bimodal distribution not bimodal?
AID  - 10.1101/150201
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 150201
4099  - http://biorxiv.org/content/early/2018/01/09/150201.short
4100  - http://biorxiv.org/content/early/2018/01/09/150201.full
AB  - 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 a 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.