Man pages for andreaskapou/SeuratPipe
Packaging common Seurat analysis tasks
| add_umap_embedding | Add UMAP embedding in Seurat object |
| cluster_analysis | Common clustering analysis steps |
| compute_module_score | Tailored module score calculation |
| create_seurat_object | Create Seurat object based on sample metadata. |
| dim_plot | Tailored dimensional reduction plot |
| dim_plot_tailored | Tailored dim plot |
| dimred_qc_plots | QC and general metadata plots visualised on dimensional... |
| dot_plot | Tailored dot plot |
| feature_plot | Tailored feature plot |
| feature_plot_tailored | Tailored feature plot |
| find_all_markers | Wrapper FindAllMarkers function |
| find_clusters | Wrapper FindClusters function |
| find_neighbors | Tailored function for finding neighbors in lower dimensional... |
| harmony_analysis | Analysis steps for Harmony integration |
| heatmap_plot | Tailored heatmap plot |
| install_scrublet | Install Scrublet Python Package |
| lognormalize_and_pca | Log normalisation and PCA computation |
| module_score_analysis | Module score analysis |
| pca_feature_cor_plot | PCA and feature metadata correlation heatmap plot |
| qc_filter_seurat_object | Filter Seurat object based on QC metrics. |
| run_cluster_pipeline | Pipeline for clustering analysis |
| run_harmony | Local implementation of RunHarmony function |
| run_harmony_pipeline | Pipeline for Harmony integration |
| run_qc_pipeline | QC pipeline |
| run_spatial_qc_pipeline | Spatial QC pipeline |
| run_umap | Tailored UMAP function |
| scatter_meta_plot | Tailored scatter plot of metadata |
| SeuratPipe | 'SeuratPipe': Streamlining Seurat analysis |
| spatial_create_seurat_object | Create Seurat object based on spatial sample metadata. |
| spatial_dim_plot | Tailored spatial dim plot |
| spatial_feature_plot | Tailored spatial feature plot |
| subset_dim_plot | Subset dim plot |
| subset_feature_plot | Subset feature plot |
