Lisa Amrhein1,2, Christiane Fuchs3,4,5. 1. Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany. 2. Department of Mathematics, Technical University Munich, Boltzmannstrasse 3, 85748, Garching, Germany. 3. Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany. christiane.fuchs@uni-bielefeld.de. 4. Department of Mathematics, Technical University Munich, Boltzmannstrasse 3, 85748, Garching, Germany. christiane.fuchs@uni-bielefeld.de. 5. Faculty of Business Administration and Economics, Bielefeld University, Universitätsstrasse 25, 33615, Bielefeld, Germany. christiane.fuchs@uni-bielefeld.de.
Abstract
BACKGROUND: Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue. RESULTS: We present the R package stochprofML which uses the maximum likelihood principle to parameterize heterogeneity from the cumulative expression of small random pools of cells. We evaluate the algorithm's performance in simulation studies and present further application opportunities. CONCLUSION: Stochastic profiling outweighs the necessary demixing of mixed samples with a saving in experimental cost and effort and less measurement error. It offers possibilities for parameterizing heterogeneity, estimating underlying pool compositions and detecting differences between cell populations between samples.
BACKGROUND: Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue. RESULTS: We present the R package stochprofML which uses the maximum likelihood principle to parameterize heterogeneity from the cumulative expression of small random pools of cells. We evaluate the algorithm's performance in simulation studies and present further application opportunities. CONCLUSION: Stochastic profiling outweighs the necessary demixing of mixed samples with a saving in experimental cost and effort and less measurement error. It offers possibilities for parameterizing heterogeneity, estimating underlying pool compositions and detecting differences between cell populations between samples.
Authors: Florian Buettner; Kedar N Natarajan; F Paolo Casale; Valentina Proserpio; Antonio Scialdone; Fabian J Theis; Sarah A Teichmann; John C Marioni; Oliver Stegle Journal: Nat Biotechnol Date: 2015-01-19 Impact factor: 54.908
Authors: Stephan M Tirier; Jeongbin Park; Friedrich Preußer; Lisa Amrhein; Zuguang Gu; Simon Steiger; Jan-Philipp Mallm; Teresa Krieger; Marcel Waschow; Björn Eismann; Marta Gut; Ivo G Gut; Karsten Rippe; Matthias Schlesner; Fabian Theis; Christiane Fuchs; Claudia R Ball; Hanno Glimm; Roland Eils; Christian Conrad Journal: Sci Rep Date: 2019-08-26 Impact factor: 4.379