stochprofML: stochastic profiling using maximum likelihood estimation in R

BMC Bioinformatics

Lisa Amrhein¹  Christiane Fuchs² 

[1] Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany;Department of Mathematics, Technical University Munich, Boltzmannstrasse 3, 85748, Garching, Germany;Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany;Department of Mathematics, Technical University Munich, Boltzmannstrasse 3, 85748, Garching, Germany;Faculty of Business Administration and Economics, Bielefeld University, Universitätsstrasse 25, 33615, Bielefeld, Germany;

关键词: StochprofML;    Stochastic profiling;    Gene expression;    Cell-to-cell heterogeneity;    Mixture models;    Deconvolution;    Maximum likelihood estimation;    R;   

DOI  :  10.1186/s12859-021-03970-7

来源: Springer

【 摘 要 】

BackgroundTissues 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.ResultsWe 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.ConclusionStochastic 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.

【 授权许可】

CC BY