دورية أكاديمية
أعرض في EDS

scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.

التفاصيل البيبلوغرافية
العنوان: scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.
المؤلفون: Magaña-López G; Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France., Calzone L; Institut Curie, Université PSL, Paris, France.; INSERM, U900, Paris, France.; Mines ParisTech, Université PSL, Paris, France., Zinovyev A; In silico R&D, Evotec, Toulouse, France., Paulevé L; Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France.
المصدر: PLoS computational biology [PLoS Comput Biol] 2024 Jul 08; Vol. 20 (7), pp. e1011620. Date of Electronic Publication: 2024 Jul 08 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science, [2005]-
مواضيع طبية MeSH: Computational Biology*/methods , Single-Cell Analysis*/methods , Single-Cell Analysis*/statistics & numerical data, Humans ; RNA-Seq/methods ; RNA-Seq/statistics & numerical data ; Gene Expression Profiling/methods ; Gene Expression Profiling/statistics & numerical data ; Sequence Analysis, RNA/methods ; Sequence Analysis, RNA/statistics & numerical data ; Algorithms ; Gene Regulatory Networks/genetics ; Models, Statistical ; Software ; Single-Cell Gene Expression Analysis
مستخلص: Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Magaña-López et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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تواريخ الأحداث: Date Created: 20240708 Date Completed: 20240718 Latest Revision: 20240720
رمز التحديث: 20240720
مُعرف محوري في PubMed: PMC11257695
DOI: 10.1371/journal.pcbi.1011620
PMID: 38976751
قاعدة البيانات: MEDLINE
الوصف
تدمد:1553-7358
DOI:10.1371/journal.pcbi.1011620