RT Journal Article
SR Electronic
T1 Modeling of cytometry data in logarithmic space: when is a bimodal distribution not bimodal?
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 150201
DO 10.1101/150201
A1 Amir Erez
A1 Robert Vogel
A1 Andrew Mugler
A1 Andrew Belmonte
A1 Grégoire Altan-Bonnet
YR 2017
UL http://biorxiv.org/content/early/2017/06/14/150201.abstract
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 uni-modal. We introduce a simple mathematical test to highlight this mismatch. We then dissect the flow of influence of cell-to-cell variability using a graphical model and its effect on measurement noise. 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 bi-modal. This communication has cautionary implications for manual and automatic gating strategies, as well as clustering and modeling of single-cell measurements.