title: Improved statistical analysis of low abundance phenomena in bimodal bacterial populations.
abstract: Accurate detection of subpopulation size determinations in bimodal populations remains problematic yet it represents a powerful way by which cellular heterogeneity under different environmental conditions can be compared. So far, most studies have relied on qualitative descriptions of population distribution patterns, on population-independent descriptors, or on arbitrary placement of thresholds distinguishing biological ON from OFF states. We found that all these methods fall short of accurately describing small population sizes in bimodal populations. Here we propose a simple, statistics-based method for the analysis of small subpopulation sizes for use in the free software environment R and test this method on real as well as simulated data. Four so-called population splitting methods were designed with different algorithms that can estimate subpopulation sizes from bimodal populations. All four methods proved more precise than previously used methods when analyzing subpopulation sizes of transfer competent cells arising in populations of the bacterium Pseudomonas knackmussii B13. The methods' resolving powers were further explored by bootstrapping and simulations. Two of the methods were not severely limited by the proportions of subpopulations they could estimate correctly, but the two others only allowed accurate subpopulation quantification when this amounted to less than 25% of the total population. In contrast, only one method was still sufficiently accurate with subpopulations smaller than 1% of the total population. This study proposes a number of rational approximations to quantifying small subpopulations and offers an easy-to-use protocol for their implementation in the open source statistical software environment R.
authors.affiliation: Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland.
authors.forename: Friedrich
authors.initials: F
authors.lastname: Reinhard
authors.forename: Jan Roelof
authors.initials: JR
authors.lastname: van der Meer
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000235:genetics* / Q000254:growth & development*; D015233:Models, Statistical; D011153:Population; D012984:Software