Inferring Boolean Networks from Single-Cell Human Embryo Datasets - Archive ouverte HAL
Communication Dans Un Congrès Année : 2023

Inferring Boolean Networks from Single-Cell Human Embryo Datasets

Résumé

Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios.
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Dates et versions

hal-04601243 , version 1 (04-06-2024)

Identifiants

Citer

Mathieu Bolteau, Jérémie Bourdon, Laurent David, Carito Guziolowski. Inferring Boolean Networks from Single-Cell Human Embryo Datasets. 19th International Symposium on Bioinformatics Research and Applications, ISBRA 2023, Oct 2023, Wrocław, Poland. pp.431-441, ⟨10.1007/978-981-99-7074-2_34⟩. ⟨hal-04601243⟩
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