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arXiv · 2004.08809

stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R

Lisa Amrhein·Christiane Fuchs
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Abstract

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. We introduce the \proglang{R} package \pkg{stochprofML} which is designed to parameterize heterogeneity from the cumulative expression of small random pools of cells. This method outweighs the demixing of mixed samples with a saving in cost and effort and less measurement error. The approach uses the maximum likelihood principle and was originally presented in Bajikar et al.(2014); its extension to varying pool sizes was used in Tirier et al. (2019). We evaluate the algorithm's performance in simulation studies and present further application opportunities.

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Lisa Amrhein, Christiane Fuchs. 2020-04-19. stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R. https://arxiv.org/abs/2004.08809

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