Dataset Information

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 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.

SUBMITTER: Amrhein L

PROVIDER: S-EPMC7958472 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

stochprofML: stochastic profiling using maximum likelihood estimation in R.

Amrhein Lisa L Fuchs Christiane C

BMC bioinformatics 20210315 1

<h4>Background</h4>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.<h4>Results</h4>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 stu ...[more]

PMID: 33722188

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