title: MEMO: multi-experiment mixture model analysis of censored data.
abstract: The statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary.
  
  
  
  We present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine.
  
  
  
  MEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO).
  
  
  
  eva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.de
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart 70550, Germany.
authors.forename: Eva-Maria
authors.initials: EM
authors.lastname: Geissen
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany Department of Mathematics, Technische Universität München, Garching 85748, Germany.
authors.forename: Jan
authors.initials: J
authors.lastname: Hasenauer
authors.affiliation: Friedrich Miescher Laboratory of the Max Planck Society, Tübingen 72076, Germany.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Heinrich
authors.affiliation: Friedrich Miescher Laboratory of the Max Planck Society, Tübingen 72076, Germany.
authors.forename: Silke
authors.initials: S
authors.lastname: Hauf
authors.affiliation: Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany Department of Mathematics, Technische Universität München, Garching 85748, Germany.
authors.forename: Fabian J
authors.initials: FJ
authors.lastname: Theis
authors.affiliation: Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart 70550, Germany.
authors.forename: Nicole E
authors.initials: NE
authors.lastname: Radde
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D011336:Probability