Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs a total of 62,999,692 cell type-specific TF-target gene pairs, covering 163 tissues/cell lines and 6,808,724 cells from 1,324 single-cell multi-omics samples.

2024年1月5日 · The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data.

The function, scGRN_getNt predicts the final gene regulatory network based on the TF-target gene expression relationships in the reference network. The reference gene regulatory network from the previous step provides all possible regulatory relationships (wires) between TF, enhancers, and target genes.

2024年3月29日 · scGRN是第一个记录从单细胞多组学数据推断的不同人类和小鼠条件下大规模细胞类型特异性GRN信息的资源。 作者已经实现了多种有效的GRN分析在线工具，包括差异TF-target网络分析，TF富集分析和pathway下游分析。

Gene regulatory network is a functional module driven by specific regulators (TF or ncRNA). It's the basis of cell phenotype. scGRN is a tool for gene regulatory network (GRN) construction and visualization

All gene interactions could be depicted as gene regulatory networks (GRNs) that summarize genetic communication and regulation as a graph network. The focus of this repository is to …

2024年11月25日 · In this work, we introduce a novel method, scGRN-Entropy, for inferring cell differentiation trajectories and pseudotime from scRNA-seq data. Unlike existing approaches, scGRN-Entropy improves inference accuracy by incorporating dynamic changes in gene regulatory networks (GRN).

scGRN is a comprehensive resource for scGRNs with the largest samples and the most comprehensive annotation information. We provide a convenient platform for researchers to explore cell type-specific GRNs and GRNs-associated analyses.

Based on the TFs and data type, users can query single sample. 1) Species: Select Homo sapiens or Mus musculus. 2) Data Type: Select the single cell omics data type of interest. 3) TFs: Input the TFs of interest. Based on the Genes and data type, users can query single sample.

2023年10月27日 · scGRN 是第一个记录从单细胞多组学数据推断的不同人类和小鼠病症的大规模细胞类型特异性 GRN 信息的资源。 我们已经实施了多种在线工具进行有效的 GRN 分析，包括差异 TF-目标网络分析、TF 富集分析和通路下游分析。