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De-novo reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms de novo and report them as small, easily interpretable transcriptional regulatory network modules. We demonstrate that DiNiro is able to uncover novel, relevant, and deep mechanistic models that not just predict but explain differential cellular gene expression programs. DiNiro is available at https://exbio.wzw.tum.de/diniro/.

SUBMITTER: Oubounyt M

PROVIDER: S-EPMC9985332 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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<i>De-novo</i> reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

Oubounyt Mhaned M Elkjaer Maria L ML Laske Tanja T Grønning Alexander G B AGB Moeller Marcus J MJ Baumbach Jan J

NAR genomics and bioinformatics 20230303 1

Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms <i>de ...[more]

PMID: 36879901

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