Man pages for scapGNN
Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis
| ATAC_net | Results of ConNetGNN() for scATAC-seq data from SNARE-seq... |
| BIC_LTMG | BIC_LTMG |
| BIC_ZIMG | BIC_ZIMG |
| ConNetGNN | Construct association networks for gene-gene, cell-cell, and... |
| ConNetGNN_data | The results of ConNetGNN() function |
| cpGModule | Identify cell phenotype activated gene module |
| create_scapGNN_env | Create the create_scapGNN_env environment on miniconda |
| Fit_LTMG | Fitting function for Left-truncated mixed Gaussian |
| Global_Zcut | Global_Zcut |
| 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 |
| instPyModule | Install the pyhton module through the reticulate R package |
| InteNet | Integrate network data from single-cell RNA-seq and ATAC-seq |
| isLoaded | The internal functions of the 'scapGNN' package |
| load_path_data | load pathway or gene set's gmt file |
| LTMG | Left-truncated mixed Gaussian |
| LTMG-class | An S4 class to represent the input data for LTMG. |
| plotCCNetwork | Visualize cell cluster association network graph |
| plotGANetwork | Visualize gene association network graph of a gene module or... |
| plotMulPhenGM | Visualize gene association network graph for activated gene... |
| Preprocessing | Data preprocessing |
| Pure_CDF | Pure_CDF |
| RNA_ATAC_IntNet | Results of InteNet() for integrating scRNA-seq and scATAC-seq... |
| RNA_net | Results of ConNetGNN() for scRNA-seq data from SNARE-seq... |
| RunLTMG | Run Left-truncated mixed Gaussian |
| RWR | Function that performs a random Walk with restart (RWR) on a... |
| scPathway | Infer pathway activation score matrix at single-cell... |
| scPathway_data | Single cell pathway activity matrix |
