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stochprofML: stochastic profiling using maximum likelihood estimation in R.

Lisa Amrhein1,2, Christiane Fuchs3,4,5

  • 1Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany.

BMC Bioinformatics
|March 16, 2021
PubMed
Summary
This summary is machine-generated.

We developed stochprofML, an R package for quantifying cell heterogeneity. This method efficiently analyzes pooled cell expression data, reducing costs and errors compared to traditional approaches.

Keywords:
Cell-to-cell heterogeneityDeconvolutionGene expressionMaximum likelihood estimationMixture modelsRStochastic profilingStochprofML

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Cellular heterogeneity is crucial for understanding development and disease.
  • Quantifying molecular expression heterogeneity in tissues is essential.

Purpose of the Study:

  • To introduce the R package stochprofML for parameterizing cellular heterogeneity.
  • To enable quantification of heterogeneity from cumulative expression of cell pools.

Main Methods:

  • Utilizes the maximum likelihood principle.
  • Analyzes cumulative expression from small, random pools of cells.
  • Evaluated performance through simulation studies.

Main Results:

  • stochprofML effectively parameterizes heterogeneity.
  • The method was validated using simulation studies.
  • Demonstrated potential for various applications.

Conclusions:

  • Stochastic profiling offers advantages over sample demixing, reducing cost, effort, and measurement error.
  • Enables parameterization of heterogeneity, estimation of pool compositions, and detection of cell population differences.