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Amrhein, L. ; Fuchs, C.

stochprofML: Stochastic profiling using maximum likelihood estimation in R.

BMC Bioinformatics 22:123 (2021)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cell-to-cell Heterogeneity ; Deconvolution ; Gene Expression ; Maximum Likelihood Estimation ; Mixture Models ; R ; Stochastic Profiling ; Stochprofml
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Zeitschrift BMC Bioinformatics
Quellenangaben Band: 22, Heft: 1, Seiten: , Artikelnummer: 123 Supplement: ,
Verlag BioMed Central
Verlagsort Campus, 4 Crinan St, London N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Foundation for the National Institutes of Health
Helmholtz Initiating and Networking Funds

Deutsche Forschungsgemeinschaft
DOI 10.1186/s12859-021-03970-7
WOS ID WOS:000629225900001
Scopus ID 85102581255
PubMed ID 33722188
Erfassungsdatum 2021-05-18
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