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scapGNN (version 0.1.4)

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

Description

It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) ; Thomas N. Kipf (2017) ) 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.

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Version

Install

install.packages('scapGNN')

Monthly Downloads

194

Version

0.1.4

License

GPL (>= 2)

Maintainer

Xudong Han

Last Published

August 8th, 2023

Functions in scapGNN (0.1.4)

Install the pyhton module through the reticulate R package

Left-truncated mixed Gaussian

An S4 class to represent the input data for LTMG.

The internal functions of the scapGNN package

Visualize gene association network graph of a gene module or pathway at the specified cell phenotype

Visualize gene association network graph for activated gene modules under multiple cell phenotypes

Run Left-truncated mixed Gaussian

Function that performs a random Walk with restart (RWR) on a given graph

create_scapGNN_env

Create the create_scapGNN_env environment on miniconda

Infer pathway activation score matrix at single-cell resolution

Single cell pathway activity matrix

Identify cell phenotype activated gene module

Visualize cell cluster association network graph

load pathway or gene set's gmt file

Integrate network data from single-cell RNA-seq and ATAC-seq

Single-cell gene expression profiles

Fitting function for Left-truncated mixed Gaussian

H9_0h_cpGM_data

Cell-activated gene modules under the 0-hour phenotype

Construct association networks for gene-gene, cell-cell, and gene-cell based on graph neural network (GNN)

Data preprocessing

The results of ConNetGNN() function

H9_36h_cpGM_data

Cell-activated gene modules under the 36-hour phenotype

Results of ConNetGNN() for scATAC-seq data from SNARE-seq dataset

H9_24h_cpGM_data

Cell-activated gene modules under the 24-hour phenotype

RNA_ATAC_IntNet

Results of InteNet() for integrating scRNA-seq and scATAC-seq data.

Results of ConNetGNN() for scRNA-seq data from SNARE-seq dataset