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A

B

  • BLGGM is a Bayesian latent mixture Gaussian graphical model to obtain cell-type-specific gene regulatory networks from heterogeneous and zero-inflated single-cell expression data
  • Platform: R/C++
  • Code: https://github.com/WgitU/BLGGM
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  • License: GPL-2.0-or-later
  • Categories: Clustering, Gene Networks
  • Added: 2022-03-18, Updated: 2022-04-01

C

  • CellPhoneDB is a publicly available repository of curated receptors, ligands and interactions.

    • Publications
    • "Single-cell reconstruction of the early maternal–fetal interface in humans"
      DOI: 10.1038/s41586-018-0698-6, Published: 2018-11-14, Citations: 1669
    • "CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes"
      DOI: 10.1038/s41596-020-0292-x, Published: 2020-02-26, Citations: 2234
    • Preprints
    • "Reconstructing the human first trimester fetal–maternal interface using single cell transcriptomics"
      DOI: 10.1101/429589, Citations: 5
    • "CellPhoneDB v2.0: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes"
      DOI: 10.1101/680926, Citations: 54
    • "CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data"
      arXiv: 2311.04567, Citations: 0
  • Platform: Python
  • Code: https://github.com/Teichlab/cellphonedb
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  • License: MIT
  • Categories: Gene Networks, Visualisation
  • Added: 2018-11-16, Updated: 2023-11-17

D

  • A deep learning-based model for gene regulatary networks (GRNs) inferrence from scRNA-seq data that transforms gene expression matrix into a correlation-based co-expression network and decouples the non-linear gene regulation patterns using graph autoencoder model (GAE)

    • Publications
    • "Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model"
      DOI: 10.1371/journal.pgen.1010942, Published: 2023-09-13, Citations: 27
  • Platform: Python
  • Code: https://github.com/JChander/DeepRIG
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  • License: Apache-2.0
  • Categories: Gene Networks
  • Added: 2023-10-20, Updated: 2023-10-20

E

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I

  • inferCSN is an package for inferring cell-specific gene regulatory network from single-cell sequencing data
  • Platform: R/C/C++
  • Code: https://github.com/mengxu98/inferCSN
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  • License: MIT
  • Categories: Gene Networks
  • Added: 2023-10-20, Updated: 2023-10-20

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  • scFEA (single cell Flux Estimation Analysis) infers single cell fluxome from single cell RNA-sequencing (scRNA-seq) data

    • Publications
    • "FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data"
      DOI: 10.1093/nar/gkad444, Published: 2023-05-22, Citations: 4
    • "A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data"
      DOI: 10.1101/gr.271205.120, Published: 2021-07-22, Citations: 99
    • Preprints
    • "A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data"
      DOI: 10.1101/2020.09.23.310656, Citations: 4
    • "scFLUX: a web server for metabolic flux and variation prediction using transcriptomics data"
      DOI: 10.1101/2022.06.18.496660, Citations: 2
  • Platform: Python
  • Code: https://github.com/changwn/scFEA
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  • License: MIT
  • Categories: Gene Networks
  • Added: 2020-10-05, Updated: 2023-06-02

  • SCORPION (Single-Cell Oriented Reconstruction of PANDA Individually Optimized Gene Regulatory Networks), is an R package that uses coarse-graining of single-cell/nuclei RNA-seq data to reduce sparsity and improve the ability to detect the gene regulatory network's underlying correlation structure

    • Publications
    • "Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data"
      DOI: 10.1038/s43588-024-00597-5, Published: 2024-03-04, Citations: 5
    • Preprints
    • "Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data"
      DOI: 10.1101/2023.01.20.524974, Citations: 2
  • Platform: R
  • Code: https://github.com/kuijjerlab/SCORPION
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  • License: GPL-3.0
  • Categories: Gene Networks
  • Added: 2023-01-27, Updated: 2024-05-06

  • SEPIRA includes SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained.

    • Preprints
    • "Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data"
      DOI: 10.1101/553040, Citations: 2
  • Platform: R
  • Code: https://github.com/YC3/SEPIRA
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  • License: GPL-3.0
  • Categories: Gene Networks
  • Added: 2019-03-01, Updated: 2019-03-01

  • Provides a general framework to perform statistical inference of each gene pair and global inference of whole-scale gene pairs in gene networks using the well known Gaussian graphical model (GGM) in a time-efficient manner.

    • Publications
    • "SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks"
      DOI: 10.1371/journal.pcbi.1006369, Published: 2018-08-13, Citations: 37
  • Platform: R
  • Code: https://github.com/cran/SILGGM
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  • License: GPL-2.0-or-later
  • Categories: Gene Networks
  • Added: 2018-08-17, Updated: 2018-08-17

  • SINCERITIES is a tool for inferring gene regulatory networks from time-stamped cross-sectional single cell transcriptional expression profiles.

    • Publications
    • "SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles"
      DOI: 10.1093/bioinformatics/btx575, Published: 2017-09-14, Citations: 169
    • Preprints
    • "SINCERITIES: Inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles"
      DOI: 10.1101/089110, Citations: 5
  • Platform: MATLAB/R
  • Code: https://github.com/CABSEL/SINCERITIES
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  • License: BSD-3-Clause
  • Categories: Gene Networks
  • Added: 2017-09-21, Updated: 2017-09-21

T

  • A tool for reconstructing Transfer Entropy-based causal gene NETwork from pseudo-time ordered single cell transcriptomic data

    • Publications
    • "TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data"
      DOI: 10.1093/nar/gkaa1014, Published: 2020-11-10, Citations: 35
    • Preprints
    • "TENET: Gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data"
      DOI: 10.1101/2019.12.20.884163, Citations: 0
  • Platform: Python
  • Code: https://github.com/neocaleb/TENET
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  • Categories: Gene Networks
  • Added: 2019-11-12, Updated: 2020-11-15

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