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