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conos  

Clustering on Network of Samples
View on CRAN: Click here


Download and install conos package within the R console
Install from CRAN:
install.packages("conos")

Install from Github:
library("remotes")
install_github("cran/conos")

Install by package version:
library("remotes")
install_version("conos", "1.5.2")



Attach the package and use:
library("conos")
Maintained by
Evan Biederstedt
[Scholar Profile | Author Map]
First Published: 2021-02-23
Latest Update: 2022-10-17
Description:
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 . The size of the 'conosPanel' package is approximately 12 MB.
How to cite:
Evan Biederstedt (2021). conos: Clustering on Network of Samples. R package version 1.5.2, https://cran.r-project.org/web/packages/conos. Accessed 30 Apr. 2025.
Previous versions and publish date:
1.4.0 (2021-02-23 10:00), 1.4.1 (2021-05-15 23:50), 1.4.2 (2021-06-29 08:20), 1.4.3 (2021-08-08 01:20), 1.4.4 (2021-11-08 23:40), 1.4.5 (2022-01-21 10:12), 1.4.6 (2022-04-12 00:12), 1.4.7 (2022-09-04 17:00), 1.4.8 (2022-09-26 09:50), 1.4.9 (2022-09-30 09:10), 1.5.0 (2022-10-17 09:02), 1.5.1 (2024-01-23 08:53)
Other packages that cited conos R package
View conos citation profile
Other R packages that conos depends, imports, suggests or enhances
Complete documentation for conos
Functions, R codes and Examples using the conos R package
Some associated functions: Conos . armaCor . basicSeuratProc . bestClusterThresholds . bestClusterTreeThresholds . buildWijMatrix . checkCountsWholeNumbers . convertToPagoda2 . edgeMat . estimateWeightEntropyPerCell . filter.genes.by.cluster.expression . findSubcommunities . getBetweenCellTypeCorrectedDE . getBetweenCellTypeDE . getCellNames . getClusterRelationshipConsistency . getClustering . getClusteringGroups . getCountMatrix . getEmbedding . getGeneExpression . getGenes . getGlobalClusterMarkers . getNeighborMatrix . getOdGenesUniformly . getOverdispersedGenes . getPca . getPerCellTypeDE . getPercentGlobalClusters . getRawCountMatrix . getSampleNamePerCell . greedyModularityCut . multimulti.community . multitrap.community . namedLevels . namedNames . p2app4conos . plotClusterBarplots . plotClusterBoxPlotsByAppType . plotComponentVariance . plotDEheatmap . plotEmbeddings . plotSamples . projectKNNs . propagateLabelsDiffusion . quickCCA . quickCPCA . quickPlainPCA . rawMatricesWithCommonGenes . reexports . saveConosForScanPy . saveDEasCSV . saveDEasJSON . scanKModularity . sgdBatches . small_panel.preprocessed . stableTreeClusters . velocityInfoConos . 
Some associated R codes: RcppExports.R . access_wrappers.R . communities.R . conclass.R . conos.R . data.R . de_functions.R . graph_embedding.R . integrations.R . largeVis.R . plot.R .  Full conos package functions and examples
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