Clustering on Network of Samples


[Up] [Top]

Documentation for package ‘conos’ version 1.4.3

Help Pages

basicSeuratProc Create and preprocess a Seurat object
bestClusterThresholds Find threshold of cluster detectability
bestClusterTreeThresholds Find threshold of cluster detectability in trees of clusters
buildWijMatrix Rescale the weights in an edge matrix to match a given perplexity.
buildWijMatrix.CsparseMatrix Rescale the weights in an edge matrix to match a given perplexity.
buildWijMatrix.TsparseMatrix Rescale the weights in an edge matrix to match a given perplexity.
Conos Conos R6 class
convertToPagoda2 Convert Conos object to Pagoda2 object
edgeMat Set edge matrix edgeMat with certain values on sample
edgeMat-method Set edge matrix edgeMat with certain values on sample
edgeMat<- Set edge matrix edgeMat with certain values on sample
edgeMat<--method Set edge matrix edgeMat with certain values on sample
estimateWeightEntropyPerCell Estimate entropy of edge weights per cell according to the specified factor. Can be used to visualize alignment quality according to this factor.
findSubcommunities Increase resolution for a specific set of clusters
getBetweenCellTypeCorrectedDE Compare two cell types across the entire panel
getBetweenCellTypeDE Compare two cell types across the entire panel
getCellNames Access cell names from sample
getCellNames-method Access cell names from sample
getClustering Access clustering from sample
getClustering-method Access clustering from sample
getCountMatrix Access count matrix from sample
getCountMatrix-method Access count matrix from sample
getEmbedding Access embedding from sample
getEmbedding-method Access embedding from sample
getGeneExpression Access gene expression from sample
getGeneExpression-method Access gene expression from sample
getGenes Access genes from sample
getGenes-method Access genes from sample
getOverdispersedGenes Access overdispersed genes from sample
getOverdispersedGenes-method Access overdispersed genes from sample
getPca Access PCA from sample
getPca-method Access PCA from sample
getPerCellTypeDE Do differential expression for each cell type in a conos object between the specified subsets of apps
getRawCountMatrix Access raw count matrix from sample
getRawCountMatrix-method Access raw count matrix from sample
getSampleNamePerCell Retrieve sample names per cell
greedyModularityCut Performs a greedy top-down selective cut to optmize modularity
p2app4conos Utility function to generate a pagoda2 app from a conos object
plotClusterBarplots Plots barplots per sample of composition of each pagoda2 application based on selected clustering
plotClusterBoxPlotsByAppType Generate boxplot per cluster of the proportion of cells in each celltype
plotComponentVariance Plot fraction of variance explained by the successive reduced space components (PCA, CPCA)
plotDEheatmap Plot a heatmap of differential genes
projectKNNs Project a distance matrix into a lower-dimensional space.
rawMatricesWithCommonGenes Get raw matrices with common genes
saveConosForScanPy Save Conos object on disk to read it from ScanPy
saveDEasCSV Save differential expression as table in *csv format
saveDEasJSON Save differential expression results as JSON
scanKModularity Scan joint graph modularity for a range of k (or k.self) values Builds graph with different values of k (or k.self if scan.k.self=TRUE), evaluating modularity of the resulting multilevel clustering NOTE: will run evaluations in parallel using con$n.cores (temporarily setting con$n.cores to 1 in the process)
sgdBatches Calculate the default number of batches for a given number of vertices and edges. The formula used is the one used by the 'largeVis' reference implementation. This is substantially less than the recommendation E * 10000 in the original paper.
small_panel.preprocessed Small pre-processed data from Pagoda2, two samples, each dimension (1000, 100)
stableTreeClusters Determine number of detectable clusters given a reference walktrap and a bunch of permuted walktraps