IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles

Jiang, Junyao; Lyu, Pin; Li, Jinlian; Huang, Sunan; Tao, Jiawang; Blackshaw, Seth; Qian, Jiang; Wang, Jie

doi:10.1016/j.isci.2022.105359

Cited by 19 publications

(15 citation statements)

References 47 publications

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“…Using CACIMAR, we examined cell type-specific regulatory networks and regulatory subnetworks (also known as regulatory modules). Initially, we reconstructed regulatory networks for each retinal cell type in mice and zebrafish using IReNA 15 . CSRN was then calculated for each pair of cell types between two species.…”

Section: Resultsmentioning

confidence: 99%

“…First, we reconstructed cell type-specific gene regulatory networks for each species. Network inference was performed using the random forest-based GENIE3 algorithm from IReNA 15 , with 500 randomly selected cells for each cell type. Then, we selected gene regulation pairs which contained at least one transcription factor from the TRANSFAC database 16 .…”

Section: Methodsmentioning

confidence: 99%

“…To identify conserved regulatory subnetworks within cell type-specific regulatory networks, we built modularized regulatory networks for resting and activated Müller glia in mice and zebrafish using IReNA 15 . In IReNA, the K-means algorithm was used to gene expression profiles for dividing each regulatory network from mice or zebrafish into ten modules, which represent different cell states and biological functions.…”

Section: Methodsmentioning

confidence: 99%

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CACIMAR: Cross-species Analysis of Cell Identities, Markers, Regulations and Interactions Using Single-cell RNA Sequencing Data

Jiang,

Li,

et al. 2024

Preprint

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Transcriptomic analysis across species is increasingly used to reveal conserved gene regulations which implicate crucial regulators. Cross-species analysis of single-cell RNA sequencing (scRNA-seq) data provides new opportunities to identify the cellular and molecular conservations especially for cell types and cell type-specific gene regulations. However, few methods have been developed to analyze cross-species scRNA-seq data to uncover both molecular and cellular conservation patterns. Here, we built a tool called CACIMAR, which can perform cross-species analysis of cell identities, markers, regulations and interactions using scRNA-seq profiles. Based on the weighted sum models of the conserved features, we developed different conservation scores to measure the conservation of cell types, regulatory networks and intercellular interactions. Using publicly available scRNA-seq data on retinal regeneration in mice and zebrafish, we demonstrated four main functions of CACIMAR. First, CACIMAR allows to identify evolutionarily conserved cell types, including poorly conserved cell types. Second, the tool facilitates the identification of evolutionarily conserved or species-specific marker genes. Third, CACIMAR enables the identification of conserved intracellular regulations, including cell type-specific regulatory subnetworks and regulators. Lastly, CACIMAR provides a unique feature on the identification of conserved intercellular interactions. Overall, CACIMAR facilitates the identification of evolutionarily conserved cell types, marker genes, intracellular regulations and intercellular interactions, providing insights on the cellular and molecular mechanisms of species evolution.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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CACIMAR: Cross-species Analysis of Cell Identities, Markers, Regulations and Interactions Using Single-cell RNA Sequencing Data

Jiang,

Li,

et al. 2024

Preprint

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“…To address this gap in knowledge, we next sought to construct a gene regulatory network (GRN) for Acinar, Ductal, and endocrine cells of the Alpha and Beta lineages ( Figure 4 A). We utilized the computational pipeline Integrated Regulatory Network Analysis (IReNA) v2 [ 48 ] ( Figure 4 B, Fig. S3A ), which combines both scRNA-Seq and snATAC-Seq data to predict TF binding of downstream target genes in a cell type-specific manner.…”

Section: Resultsmentioning

confidence: 99%

Single-cell chromatin accessibility of developing murine pancreas identifies cell state-specific gene regulatory programs

Yao

Chang

et al. 2023

Molecular Metabolism

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“…Many methods integrate unpaired scRNA-seq and scATACseq data, which are measured not on the same cell but on two batches of the same mixed population, to infer trans-regulation. Those methods, including IReNA [30], SOMatic [31], UnpairReg [32], CoupledNMF [33,34], DC3 [34], and Wang et al [35], link TFs to REs by motif matching and link REs to TGs using the covariation of RE-TG across cell types or physical base pair distance. Recently, scJoint [36] was developed to transfer label from scRNA-seq to scATAC-seq data, which may enable improve cell GRN inference.…”

Section: Introductionmentioning

confidence: 99%

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data

Duren

2023

Preprint

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Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 4-10 fold relative increase in accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from single-cell multiome data, empowering deeper insights into cellular mechanisms.

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IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles

Cited by 19 publications

References 47 publications

CACIMAR: Cross-species Analysis of Cell Identities, Markers, Regulations and Interactions Using Single-cell RNA Sequencing Data

CACIMAR: Cross-species Analysis of Cell Identities, Markers, Regulations and Interactions Using Single-cell RNA Sequencing Data

Single-cell chromatin accessibility of developing murine pancreas identifies cell state-specific gene regulatory programs

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data

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