Package: scapGNN 0.1.4

scapGNN: Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis

It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arxiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.

Authors:Xudong Han [aut, cre, cph], Xujiang Guo [fnd]

scapGNN_0.1.4.tar.gz
scapGNN_0.1.4.zip(r-4.5)scapGNN_0.1.4.zip(r-4.4)scapGNN_0.1.4.zip(r-4.3)
scapGNN_0.1.4.tgz(r-4.4-any)scapGNN_0.1.4.tgz(r-4.3-any)
scapGNN_0.1.4.tar.gz(r-4.5-noble)scapGNN_0.1.4.tar.gz(r-4.4-noble)
scapGNN_0.1.4.tgz(r-4.4-emscripten)scapGNN_0.1.4.tgz(r-4.3-emscripten)
scapGNN.pdf |scapGNN.html✨
scapGNN/json (API)

# Install 'scapGNN' in R:
install.packages('scapGNN', repos = c('https://xudonghan-bio.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • ATAC_net - Results of ConNetGNN() for scATAC-seq data from SNARE-seq dataset
  • ConNetGNN_data - The results of ConNetGNN() function
  • H9_0h_cpGM_data - Cell-activated gene modules under the 0-hour phenotype
  • H9_24h_cpGM_data - Cell-activated gene modules under the 24-hour phenotype
  • H9_36h_cpGM_data - Cell-activated gene modules under the 36-hour phenotype
  • Hv_exp - Single-cell gene expression profiles
  • RNA_ATAC_IntNet - Results of InteNet() for integrating scRNA-seq and scATAC-seq data.
  • RNA_net - Results of ConNetGNN() for scRNA-seq data from SNARE-seq dataset
  • scPathway_data - Single cell pathway activity matrix

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 7 scripts 150 downloads 11 exports 95 dependencies

Last updated 1 years agofrom:afde8b0524. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:ConNetGNNcpGModuleInteNetload_path_dataplotCCNetworkplotGANetworkplotMulPhenGMPreprocessingRunLTMGRWRscPathway

Dependencies:ActivePathwaysAdaptGaussaskpassbase64encbslibcachemclicolorspacecommonmarkcoopcpp11crayoncrosstalkcurldata.tableDataVisualizationsdigestdplyrevaluatefansifarverfastmapfontawesomefsgenericsggplot2gluegtableherehighrhtmltoolshtmlwidgetshttpuvhttrigraphisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemgcvmimemixtoolsmunsellnlmeopensslpillarpkgconfigplotlyplyrpngpracmapromisespurrrR6rappdirsRColorBrewerRcppRcppArmadilloRcppTOMLreshape2reticulaterlangrmarkdownrprojrootsassscalessegmentedshinysourcetoolsspstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml

scapGNN: Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis

Xudong Han and Xuejiang Guo

Rendered fromvignette.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2023-08-08
Started: 2022-06-10

Readme and manuals

Help Manual

Help pageTopics
Results of ConNetGNN() for scATAC-seq data from SNARE-seq datasetATAC_net
BIC_LTMGBIC_LTMG
BIC_ZIMGBIC_ZIMG
Construct association networks for gene-gene, cell-cell, and gene-cell based on graph neural network (GNN)ConNetGNN
The results of ConNetGNN() functionConNetGNN_data
Identify cell phenotype activated gene modulecpGModule
Create the create_scapGNN_env environment on minicondacreate_scapGNN_env
Fitting function for Left-truncated mixed GaussianFit_LTMG
Global_ZcutGlobal_Zcut
Cell-activated gene modules under the 0-hour phenotypeH9_0h_cpGM_data
Cell-activated gene modules under the 24-hour phenotypeH9_24h_cpGM_data
Cell-activated gene modules under the 36-hour phenotypeH9_36h_cpGM_data
Single-cell gene expression profilesHv_exp
Install the pyhton module through the reticulate R packageinstPyModule
Integrate network data from single-cell RNA-seq and ATAC-seqInteNet
The internal functions of the 'scapGNN' packageisLoaded
load pathway or gene set's gmt fileload_path_data
Left-truncated mixed GaussianLTMG
An S4 class to represent the input data for LTMG.LTMG-class
Visualize cell cluster association network graphplotCCNetwork
Visualize gene association network graph of a gene module or pathway at the specified cell phenotypeplotGANetwork
Visualize gene association network graph for activated gene modules under multiple cell phenotypesplotMulPhenGM
Data preprocessingPreprocessing
Pure_CDFPure_CDF
Results of InteNet() for integrating scRNA-seq and scATAC-seq data.RNA_ATAC_IntNet
Results of ConNetGNN() for scRNA-seq data from SNARE-seq datasetRNA_net
Run Left-truncated mixed Gaussian.RunLTMG RunLTMG RunLTMG,LTMG-method
Function that performs a random Walk with restart (RWR) on a given graphRWR
Infer pathway activation score matrix at single-cell resolutionscPathway
Single cell pathway activity matrixscPathway_data