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-4.7-any)scapGNN_0.1.4.zip(r-4.6-any)scapGNN_0.1.4.zip(r-4.5-any)
scapGNN_0.1.4.tgz(r-4.6-any)scapGNN_0.1.4.tgz(r-4.5-any)
scapGNN_0.1.4.tar.gz(r-4.7-any)scapGNN_0.1.4.tar.gz(r-4.6-any)
scapGNN_0.1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
scapGNN/json (API)

# Install 'scapGNN' in R:

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

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 9 scripts 304 downloads 11 exports 92 dependencies

Last updated from:afde8b0524. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	186
source / vignettes	OK	214
linux-release	OK	182
macos-release	OK	183
macos-oldrel	OK	153
windows-devel	OK	162
windows-release	OK	145
windows-oldrel	OK	133
wasm-release	OK	113

Exports:ConNetGNN cpGModule InteNet load_path_data plotCCNetwork plotGANetwork plotMulPhenGM Preprocessing RunLTMG RWR scPathway

Dependencies:ActivePathways AdaptGauss askpass base64enc bslib cachem cli commonmark coop cpp11 crosstalk curl data.table DataVisualizations digest dplyr evaluate farver fastmap fontawesome fs generics ggplot2 glue gtable here highr htmltools htmlwidgets httpuv httr igraph isoband jquerylib jsonlite kernlab knitr labeling later lattice lazyeval lifecycle magrittr MASS Matrix memoise mime mixtools nlme openssl otel pillar pkgconfig plotly plyr png pracma promises purrr R6 rappdirs RColorBrewer Rcpp RcppParallel RcppTOML reshape2 reticulate rlang rmarkdown rprojroot S7 sass scales segmented shiny sourcetools sp stringi stringr survival sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun xtable yaml

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

Rendered fromvignette.Rmdusingknitr::rmarkdown

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

Help page	Topics
Results of ConNetGNN() for scATAC-seq data from SNARE-seq dataset	ATAC_net
BIC_LTMG	BIC_LTMG
BIC_ZIMG	BIC_ZIMG
Construct association networks for gene-gene, cell-cell, and gene-cell based on graph neural network (GNN)	ConNetGNN
The results of ConNetGNN() function	ConNetGNN_data
Identify cell phenotype activated gene module	cpGModule
Create the create_scapGNN_env environment on miniconda	create_scapGNN_env
Fitting function for Left-truncated mixed Gaussian	Fit_LTMG
Global_Zcut	Global_Zcut
Cell-activated gene modules under the 0-hour phenotype	H9_0h_cpGM_data
Cell-activated gene modules under the 24-hour phenotype	H9_24h_cpGM_data
Cell-activated gene modules under the 36-hour phenotype	H9_36h_cpGM_data
Single-cell gene expression profiles	Hv_exp
Install the pyhton module through the reticulate R package	instPyModule
Integrate network data from single-cell RNA-seq and ATAC-seq	InteNet
The internal functions of the 'scapGNN' package	isLoaded
load pathway or gene set's gmt file	load_path_data
Left-truncated mixed Gaussian	LTMG
An S4 class to represent the input data for LTMG.	LTMG-class
Visualize cell cluster association network graph	plotCCNetwork
Visualize gene association network graph of a gene module or pathway at the specified cell phenotype	plotGANetwork
Visualize gene association network graph for activated gene modules under multiple cell phenotypes	plotMulPhenGM
Data preprocessing	Preprocessing
Pure_CDF	Pure_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 dataset	RNA_net
Run Left-truncated mixed Gaussian	.RunLTMG RunLTMG RunLTMG,LTMG-method
Function that performs a random Walk with restart (RWR) on a given graph	RWR
Infer pathway activation score matrix at single-cell resolution	scPathway
Single cell pathway activity matrix	scPathway_data

Package: scapGNN 0.1.4

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

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Help Manual

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