title: Uncovering distinct protein-network topologies in heterogeneous cell populations.
abstract: BACKGROUND
  Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation.
  
  
  RESULTS
  Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins.
  
  
  CONCLUSIONS
  Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.
authors.affiliation: Faculty of Statistics, TU Dortmund University, Dortmund, Germany. Jakob.Wieczorek@uni-dortmund.de.
authors.forename: Jakob
authors.initials: J
authors.lastname: Wieczorek
authors.affiliation: Department of Systemic Cell Biology, Max-Planck Institute of Molecular Physiology, Dortmund, Germany. mrahumansheriff@gmail.com.|Present address: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK. mrahumansheriff@gmail.com.|Present address: MRC Clinical Sciences Centre, Imperial College London, London, UK. mrahumansheriff@gmail.com.
authors.forename: Rahuman S
authors.initials: RS
authors.lastname: Malik-Sheriff
authors.affiliation: Faculty of Statistics, TU Dortmund University, Dortmund, Germany. fermin@statistik.tu-dortmund.de.
authors.forename: Yessica
authors.initials: Y
authors.lastname: Fermin
authors.affiliation: Department of Systemic Cell Biology, Max-Planck Institute of Molecular Physiology, Dortmund, Germany. grecco@mpi-dortmund.mpg.de.|Present address: Department of Physics, FCEN, University of Buenos Aires and IFIBA, CONICET, Buenos Aires, Argentina. grecco@mpi-dortmund.mpg.de.
authors.forename: Hernán E
authors.initials: HE
authors.lastname: Grecco
authors.affiliation: Department of Systemic Cell Biology, Max-Planck Institute of Molecular Physiology, Dortmund, Germany. eli.zamir@mpi-dortmund.mpg.de.
authors.forename: Eli
authors.initials: E
authors.lastname: Zamir
authors.affiliation: Faculty of Statistics, TU Dortmund University, Dortmund, Germany. ickstadt@statistik.uni-dortmund.de.
authors.forename: Katja
authors.initials: K
authors.lastname: Ickstadt
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D004815:Epidermal Growth Factor / Q000494:pharmacology; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism; D008954:Models, Biological; D020932:Nerve Growth Factor / Q000494:pharmacology; D016716:PC12 Cells; D060066:Protein Interaction Maps / Q000187:drug effects; D051381:Rats; D048490:raf Kinases / Q000378:metabolism
chemical_list: D004815:Epidermal Growth Factor; D020932:Nerve Growth Factor; D048490:raf Kinases; D020928:Mitogen-Activated Protein Kinases