Package: conos 1.5.2
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:
conos_1.5.2.tar.gz
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conos_1.5.2.tgz(r-4.4-x86_64)conos_1.5.2.tgz(r-4.4-arm64)conos_1.5.2.tgz(r-4.3-x86_64)conos_1.5.2.tgz(r-4.3-arm64)
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conos_1.5.2.tgz(r-4.4-emscripten)conos_1.5.2.tgz(r-4.3-emscripten)
conos.pdf |conos.html✨
conos/json (API)
# Install 'conos' in R: |
install.packages('conos', repos = c('https://kharchenkolab.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kharchenkolab/conos/issues
- small_panel.preprocessed - Small pre-processed data from Pagoda2, two samples, each dimension
batch-correctionscrna-seqsingle-cell-rna-seq
Last updated 9 months agofrom:91110bafc0. Checks:OK: 8 ERROR: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 22 2024 |
R-4.5-win-x86_64 | OK | Nov 22 2024 |
R-4.5-linux-x86_64 | OK | Nov 22 2024 |
R-4.4-win-x86_64 | OK | Nov 22 2024 |
R-4.4-mac-x86_64 | OK | Nov 22 2024 |
R-4.4-mac-aarch64 | OK | Nov 22 2024 |
R-4.3-win-x86_64 | OK | Nov 22 2024 |
R-4.3-mac-x86_64 | OK | Nov 22 2024 |
R-4.3-mac-aarch64 | ERROR | Nov 22 2024 |
Exports:basicSeuratProcbestClusterThresholdsbestClusterTreeThresholdsbuildWijMatrixConosconvertToPagoda2edgeMatedgeMat<-embeddingPlotestimateWeightEntropyPerCellfindSubcommunitiesgetBetweenCellTypeCorrectedDEgetBetweenCellTypeDEgetCellNamesgetClusteringgetCountMatrixgetEmbeddinggetGeneExpressiongetGenesgetOverdispersedGenesgetPcagetPerCellTypeDEgetRawCountMatrixgetSampleNamePerCellgreedyModularityCutleiden.communityp2app4conosplotClusterBarplotsplotClusterBoxPlotsByAppTypeplotComponentVarianceplotDEheatmapprojectKNNsrawMatricesWithCommonGenessaveConosForScanPysaveDEasCSVsaveDEasJSONscanKModularitysgdBatchesstableTreeClustersvelocityInfoConos
Dependencies:abindBHBiocGenericscirclizecliclueclustercodetoolscolorspaceComplexHeatmapcowplotcpp11crayondendextenddigestdoParalleldplyrdqrngfansifarverFNNforeachgenericsGetoptLongggplot2ggrepelGlobalOptionsgluegridExtragtableigraphIRangesirlbaisobanditeratorslabelinglatticeleidenAlglifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellN2RnlmepbmcapplypillarpkgconfigplyrpngpROCR6RColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppProgressRcppSpdlogreshape2rjsonrlangRSpectraRtsneS4Vectorsscalessccoreshapesitmostringistringrtibbletidyselectutf8uwotvctrsviridisviridisLitewithr