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Peer-reviewedCC BY 4.0G3: Genes, Genomes, Genetics

Model-based inference of gene expression noise from single-cell RNA-sequencing data

  • Fernanda Giersdorf1
  • David W Rogers2
  • Sören Christensen3
  • Julien Y Dutheil1
  1. 1Max Planck Institute for Evolutionary Biology, Department of Theoretical Biology , 24306 Plön ,
  2. 2Max Planck Institute for Evolutionary Biology, Department of Microbial Population Biology , 24306 Plön ,
  3. 3Christian-Albrechts University Kiel, Department Of Mathematics , 24118 Kiel ,
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Abstract

The heterogeneity of expression levels among genetically identical cells, termed gene expression noise, is a property of the gene expression process whose importance in the biology of organisms and their evolution is increasingly recognized. Measuring gene expression noise requires single-cell expression data, as obtained from single-cell RNA sequencing (scRNASeq). Its estimation, however, is challenging owing to (i) the presence of technical noise in addition to biological noise, and (ii) the heterogeneity of cell types in the sampled population. We propose a maximum-likelihood framework to infer biological noise from scRNASeq data, while accounting for technical noise, dropout probabilities, and distinct cell sequencing depths. We demonstrate the parameter identifiability using simulations and that the resulting noise estimates are uncorrelated from the mean gene expression, and therefore do not need extra correction in downstream analyses, easing intra- and inter- genome comparisons. Using two technical replicates of scRNASeq data from the wild yeast Saccharomyces paradoxus, we show that expression noise can be inferred in a reproducible manner.

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Cite

BibTeXRIS
BibTeX
@article{giersdorf2026modelbased,
  title = {{Model-based inference of gene expression noise from single-cell RNA-sequencing data}},
  author = {Fernanda Giersdorf and David W Rogers and Sören Christensen and Julien Y Dutheil},
  journal = {G3: Genes, Genomes, Genetics},
  year = {2026},
  month = oct,
  doi = {10.1093/g3journal/jkag305},
  url = {https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkag305/9004288},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Model-based inference of gene expression noise from single-cell RNA-sequencing data
AU  - Fernanda Giersdorf
AU  - David W Rogers
AU  - Sören Christensen
AU  - Julien Y Dutheil
PY  - 2026
DA  - 2026/10/29
JO  - G3: Genes, Genomes, Genetics
DO  - 10.1093/g3journal/jkag305
UR  - https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkag305/9004288
AB  - The heterogeneity of expression levels among genetically identical cells, termed gene expression noise, is a property of the gene expression process whose importance in the biology of organisms and their evolution is increasingly recognized. Measuring gene expression noise requires single-cell expression data, as obtained from single-cell RNA sequencing (scRNASeq). Its estimation, however, is challenging owing to (i) the presence of technical noise in addition to biological noise, and (ii) the heterogeneity of cell types in the sampled population. We propose a maximum-likelihood framework to infer biological noise from scRNASeq data, while accounting for technical noise, dropout probabilities, and distinct cell sequencing depths. We demonstrate the parameter identifiability using simulations and that the resulting noise estimates are uncorrelated from the mean gene expression, and therefore do not need extra correction in downstream analyses, easing intra- and inter- genome comparisons. Using two technical replicates of scRNASeq data from the wild yeast Saccharomyces paradoxus, we show that expression noise can be inferred in a reproducible manner.
SN  - 2160-1836
ER  -