stochprofML: stochastic profiling using maximum likelihood estimation in R
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...
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Online Access: | https://doi.org/10.1186/s12859-021-03970-7 |
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doaj-eed19de341d54302bd08d395e04cfd342021-03-21T12:53:09ZengBMCBMC Bioinformatics1471-21052021-03-0122113110.1186/s12859-021-03970-7stochprofML: stochastic profiling using maximum likelihood estimation in RLisa Amrhein0Christiane Fuchs1Institute of Computational Biology, Helmholtz Zentrum MünchenInstitute of Computational Biology, Helmholtz Zentrum MünchenAbstract 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.https://doi.org/10.1186/s12859-021-03970-7StochprofMLStochastic profilingGene expressionCell-to-cell heterogeneityMixture modelsDeconvolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lisa Amrhein Christiane Fuchs |
spellingShingle |
Lisa Amrhein Christiane Fuchs stochprofML: stochastic profiling using maximum likelihood estimation in R BMC Bioinformatics StochprofML Stochastic profiling Gene expression Cell-to-cell heterogeneity Mixture models Deconvolution |
author_facet |
Lisa Amrhein Christiane Fuchs |
author_sort |
Lisa Amrhein |
title |
stochprofML: stochastic profiling using maximum likelihood estimation in R |
title_short |
stochprofML: stochastic profiling using maximum likelihood estimation in R |
title_full |
stochprofML: stochastic profiling using maximum likelihood estimation in R |
title_fullStr |
stochprofML: stochastic profiling using maximum likelihood estimation in R |
title_full_unstemmed |
stochprofML: stochastic profiling using maximum likelihood estimation in R |
title_sort |
stochprofml: stochastic profiling using maximum likelihood estimation in r |
publisher |
BMC |
series |
BMC Bioinformatics |
issn |
1471-2105 |
publishDate |
2021-03-01 |
description |
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. |
topic |
StochprofML Stochastic profiling Gene expression Cell-to-cell heterogeneity Mixture models Deconvolution |
url |
https://doi.org/10.1186/s12859-021-03970-7 |
work_keys_str_mv |
AT lisaamrhein stochprofmlstochasticprofilingusingmaximumlikelihoodestimationinr AT christianefuchs stochprofmlstochasticprofilingusingmaximumlikelihoodestimationinr |
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1724210127116959744 |