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

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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

Original languageEnglish
Article numberlqad018
JournalNAR Genomics and Bioinformatics
Issue number1
Number of pages17
Publication statusPublished - 2023

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© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

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