Package: conos 1.5.4

Evan Biederstedt

conos: Clustering on Network of Samples

Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.

Authors:Viktor Petukhov [aut], Nikolas Barkas [aut], Peter Kharchenko [aut], Weiliang Qiu [ctb], Evan Biederstedt [aut, cre]

conos_1.5.4.tar.gz
conos_1.5.4.tar.gz(r-4.7-arm64)conos_1.5.4.tar.gz(r-4.7-x86_64)conos_1.5.4.tar.gz(r-4.6-arm64)conos_1.5.4.tar.gz(r-4.6-x86_64)
conos_1.5.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
conos/json (API)

# Install 'conos' in R:
install.packages('conos', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kharchenkolab/conos/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

3.40 score 238 scripts 1.0k downloads 1 mentions 40 exports 92 dependencies

Last updated from:38f9a4b504. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK385
linux-devel-x86_64OK426
source / vignettesOK403
linux-release-arm64OK398
linux-release-x86_64OK411
wasm-releaseOK323

Exports:basicSeuratProcbestClusterThresholdsbestClusterTreeThresholdsbuildWijMatrixConosconvertToPagoda2edgeMatedgeMat<-embeddingPlotestimateWeightEntropyPerCellfindSubcommunitiesgetBetweenCellTypeCorrectedDEgetBetweenCellTypeDEgetCellNamesgetClusteringgetCountMatrixgetEmbeddinggetGeneExpressiongetGenesgetOverdispersedGenesgetPcagetPerCellTypeDEgetRawCountMatrixgetSampleNamePerCellgreedyModularityCutleiden.communityp2app4conosplotClusterBarplotsplotClusterBoxPlotsByAppTypeplotComponentVarianceplotDEheatmapprojectKNNsrawMatricesWithCommonGenessaveConosForScanPysaveDEasCSVsaveDEasJSONscanKModularitysgdBatchesstableTreeClustersvelocityInfoConos

Dependencies:abindBHBiocGenericsbrewcirclizecliclueclustercodetoolscolorspaceComplexHeatmapcowplotcpp11crayondendextenddendsortdigestdoParalleldplyrdqrngdratfarverfastclusterFNNforeachgenericsGetoptLongggplot2ggrepelGlobalOptionsgluegridExtragtableigraphIRangesirlbaisobanditeratorslabelinglatticeleidenAlglifecyclemagrittrMASSMatrixmatrixStatsmgcvN2Rnlmepagoda2pbmcapplypillarpkgconfigplyrpngpROCR.methodsS3R.ooR.utilsR6RColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppProgressRcppSpdlogreshape2rjsonrlangRMTstatRookRSpectraRtsneS4VectorsS7scalessccoreshapesitmostringistringrtibbletidyselecttriebeardurltoolsutf8uwotvctrsviridisviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Create and preprocess a Seurat objectbasicSeuratProc
Find threshold of cluster detectabilitybestClusterThresholds
Find threshold of cluster detectability in trees of clustersbestClusterTreeThresholds
Rescale the weights in an edge matrix to match a given perplexity.buildWijMatrix buildWijMatrix.CsparseMatrix buildWijMatrix.TsparseMatrix
Conos R6 classConos
Convert Conos object to Pagoda2 objectconvertToPagoda2
Set edge matrix edgeMat with certain values on sampleedgeMat edgeMat,Pagoda2-method edgeMat,Seurat-method edgeMat,seurat-method edgeMat<- edgeMat<-,Pagoda2-method edgeMat<-,Seurat-method edgeMat<-,seurat-method
Estimate entropy of edge weights per cell according to the specified factor. Can be used to visualize alignment quality according to this factor.estimateWeightEntropyPerCell
Increase resolution for a specific set of clustersfindSubcommunities
Compare two cell types across the entire panelgetBetweenCellTypeCorrectedDE
Compare two cell types across the entire panelgetBetweenCellTypeDE
Access cell names from samplegetCellNames getCellNames,Conos-method getCellNames,Pagoda2-method getCellNames,Seurat-method getCellNames,seurat-method
Access clustering from samplegetClustering getClustering,Conos-method getClustering,Pagoda2-method getClustering,Seurat-method getClustering,seurat-method
Access count matrix from samplegetCountMatrix getCountMatrix,Pagoda2-method getCountMatrix,Seurat-method getCountMatrix,seurat-method
Access embedding from samplegetEmbedding getEmbedding,Conos-method getEmbedding,Pagoda2-method getEmbedding,Seurat-method getEmbedding,seurat-method
Access gene expression from samplegetGeneExpression getGeneExpression,Conos-method getGeneExpression,Pagoda2-method getGeneExpression,Seurat-method getGeneExpression,seurat-method
Access genes from samplegetGenes getGenes,Conos-method getGenes,Pagoda2-method getGenes,Seurat-method getGenes,seurat-method
Access overdispersed genes from samplegetOverdispersedGenes getOverdispersedGenes,Conos-method getOverdispersedGenes,Pagoda2-method getOverdispersedGenes,Seurat-method getOverdispersedGenes,seurat-method
Access PCA from samplegetPca getPca,Pagoda2-method getPca,Seurat-method getPca,seurat-method
Do differential expression for each cell type in a conos object between the specified subsets of appsgetPerCellTypeDE
Access raw count matrix from samplegetRawCountMatrix getRawCountMatrix,Conos-method getRawCountMatrix,Pagoda2-method getRawCountMatrix,Seurat-method getRawCountMatrix,seurat-method
Retrieve sample names per cellgetSampleNamePerCell
Performs a greedy top-down selective cut to optmize modularitygreedyModularityCut
Utility function to generate a pagoda2 app from a conos objectp2app4conos
Plots barplots per sample of composition of each pagoda2 application based on selected clusteringplotClusterBarplots
Generate boxplot per cluster of the proportion of cells in each celltypeplotClusterBoxPlotsByAppType
Plot fraction of variance explained by the successive reduced space components (PCA, CPCA)plotComponentVariance
Plot a heatmap of differential genesplotDEheatmap
Project a distance matrix into a lower-dimensional space.projectKNNs
Get raw matrices with common genesrawMatricesWithCommonGenes
Save Conos object on disk to read it from ScanPysaveConosForScanPy
Save differential expression as table in *csv formatsaveDEasCSV
Save differential expression results as JSONsaveDEasJSON
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)scanKModularity
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.sgdBatches
Small pre-processed data from Pagoda2, two samples, each dimension (1000, 100)small_panel.preprocessed
Determine number of detectable clusters given a reference walktrap and a bunch of permuted walktrapsstableTreeClusters
RNA velocity analysis on samples integrated with conos Create a list of objects to pass into gene.relative.velocity.estimates function from the velocyto.R packagevelocityInfoConos