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Lazzardi, S.* ; Valle, F.* ; Mazzolini, A.* ; Scialdone, A. ; Caselle, M.* ; Osella, M.*

Emergent statistical laws in single-cell transcriptomic data.

Phys. Rev. E 107:044403 (2023)
DOI PMC
Large-scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology, or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Gene-expression; Rna-seq; Distributions; Features; Reveals; Origins; Systems; Growth
ISSN (print) / ISBN 1063-651X
e-ISSN 1550-2376
Zeitschrift Physical Review E
Quellenangaben Band: 107, Heft: 4-1, Seiten: , Artikelnummer: 044403 Supplement: ,
Verlag American Physical Society (APS)
Verlagsort Melville, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epigenetics and Stem Cells (IES)
Institute of Computational Biology (ICB)
Institute of Functional Epigenetics (IFE)