Package index
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build.graph() - Build Graph Based on Principal Components and K-Nearest Neighbors
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build.meta.network()build.meta.response() - Build Meta-Networks (Meta-response) via Network Embeddings (PCA / Non-Negative PCA)
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find.significant.pcs() - Find Significant Principal Components
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gene.list - Gene List from Integrated Prior Knowledge Network
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get.gr.adj() - Create an Adjacency Matrix of Gene Regulation with Propagation
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get.network() - Extract Per-Cell Gene Co-Expression Networks
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get.prior.model() - Generate a Pruned Prior Model from Personalized PageRank Results
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gg.color.spec() - Generate a Color Palette for ggplot Visualizations
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npca() - Non-Negative PCA with Deflation (nPCA)
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gr.graphsig.graph - Integrated Weighted Directed Networks for Prior Knowledge in Gene Regulation and Signaling
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prepare.graph() - Build a K-Nearest Neighbors (KNN) Graph and Store in Seurat Object
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prepare.reg() - Prepare a Seurat Object for PC Regression Analysis
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prepare.seurat() - Prepare a Seurat Object with Selected Genes and PCA
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prepare.visualise() - Prepare Gene and Receptor Settings for NeighbourNet Visualisation
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receptor.activity() - Infer Receptor Activity from NeighbourNet TF–Target Networks
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receptor.ppr - Personalized PageRank Results for Signaling Network
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run.nn.reg() - Run Nearest-Neighbor PC Regression for Gene Co-Expression Analysis
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select.cell() - Select Cells for Downstream PC Regression Analysis
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select.central.genes() - Select Central Genes from Meta-Networks via Eigenvector Centrality
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select.gene() - Select Genes for Analysis
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set.defaults() - Set Default Parameters for Network Extraction
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topn() - Select indices of the top-n highest values
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visualise.network() - Visualise Receptor–TF–Target Pathways on NeighbourNet Cell-Specific Networks.