基于 HuMMuS 从单细胞多组学数据中重建分子机制。

Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS.

机构信息

Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015 Paris, France.

Institut de Biologie de l'Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005 Paris, France.

出版信息

Bioinformatics. 2024 May 2;40(5). doi: 10.1093/bioinformatics/btae143.

DOI:10.1093/bioinformatics/btae143

PMID:38460192

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11065476/

Abstract

MOTIVATION

The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation.

RESULTS

Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation. We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMuS in terms of transcription factor (TF) targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs.

AVAILABILITY AND IMPLEMENTATION

HuMMuS is available at https://github.com/cantinilab/HuMMuS.

摘要

动机

细胞的分子特征源于异质分子层之间的复杂相互作用。单细胞测序技术的最新进展为测量这种调控的分子层提供了可能。

结果

在这里，我们提出了 HuMMuS，这是一种从单细胞多组学数据中推断调控机制的新方法。与现有技术不同的是，HuMMuS 捕捉了生物大分子之间的合作，并且可以轻松地包括额外的分子调控层。我们在配对和非配对多组学数据集上对 HuMMuS 进行了基准测试。我们的结果证明了 HuMMuS 在转录因子 (TF) 靶标、TF 结合基序和调控区域预测方面提供的改进。最后，一旦将其应用于来自小鼠大脑皮层的 snmC-seq、scATAC-seq 和 scRNA-seq 数据，HuMMuS 能够准确地对 scRNA 图谱进行聚类，并识别潜在的驱动 TF。