Package: Coralysis 1.3.0

António Sousa

Coralysis: Coralysis sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration

Coralysis is an R package featuring a multi-level integration algorithm for sensitive integration, reference-mapping, and cell-state identification in single-cell data. The multi-level integration algorithm is inspired by the process of assembling a puzzle - where one begins by grouping pieces based on low-to high-level features, such as color and shading, before looking into shape and patterns. This approach progressively blends the batch effects and separates cell types across multiple rounds of divisive clustering.

Authors:António Sousa [cre, aut], Johannes Smolander [ctb, aut], Sini Junttila [aut], Laura L Elo [aut]

Coralysis_1.3.0.tar.gz
Coralysis_1.3.0.zip(r-4.7-any)Coralysis_1.3.0.zip(r-4.6-any)Coralysis_1.3.0.zip(r-4.5-any)
Coralysis_1.3.0.tgz(r-4.6-any)Coralysis_1.3.0.tgz(r-4.5-any)
Coralysis_1.3.0.tar.gz(r-4.7-any)Coralysis_1.3.0.tar.gz(r-4.6-any)
Coralysis_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Coralysis/json (API)

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

Bug tracker:https://github.com/elolab/coralysis/issues

Pkgdown/docs site:https://elolab.github.io

On BioConductor:Coralysis-1.3.0(bioc 3.24)Coralysis-1.2.0(bioc 3.23)

singlecellrnaseqproteomicstranscriptomicsgeneexpressionbatcheffectclusteringannotationclassificationdifferentialexpressiondimensionreductionsoftwaredata-integrationscrna-seq

4.00 score 5 stars 3 scripts 410 downloads 23 exports 122 dependencies

Last updated from:de2d3abc34. Checks:1 NOTE, 7 WARNING, 1 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE217
linux-develWARNING577
source / vignettesERROR523
linux-releaseWARNING516
macos-releaseWARNING530
macos-oldrelWARNING437
windows-develWARNING2404
windows-releaseWARNING1580
windows-oldrelWARNING2419
wasm-releaseOK178

Exports:AggregateDataByBatchBinCellClusterProbabilityCellBinsFeatureCorrelationCellClusterProbabilityDistributionFindAllClusterMarkersFindClusterMarkersGetCellClusterProbabilityGetFeatureCoefficientsHeatmapFeaturesMajorityVotingFeaturesPCAElbowPlotPlotClusterTreePlotDimRedPlotExpressionPrepareDataReferenceMappingRunParallelDivisiveICPRunPCARunTSNERunUMAPSummariseCellClusterProbabilityTabulateCellBinsByGroupVlnPlot

Dependencies:abindaricodeaskpassassortheadbase64encbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularblusterCairoclasscliclustercodetoolscowplotcpp11DelayedArraydigestdplyrdqrngedgeRfarverflexclustFNNformatRfsfutile.loggerfutile.optionsgenericsGenomicRangesggbeeswarmggforceggfunggplot2ggrastrggrepelgluegtablehereigraphIRangesirlbaisobandjsonlitelabelinglambda.rlatticeLiblineaRlifecyclelimmamagrittrMASSMatrixMatrixGenericsmatrixStatsmetapodmodeltoolsopensslpheatmappillarpkgconfigplyrpngpolyclippurrrR6raggRANNrappdirsRColorBrewerRcppRcppAnnoyRcppEigenRcppProgressRcppTOMLreshape2reticulaterlangrprojrootRSpectrarsvdRtsneS4ArraysS4VectorsS7ScaledMatrixscalesscatterpiescranscuttleSeqinfoSingleCellExperimentsitmosnowSparseArraySparseMsparseMatrixStatsstatmodstringistringrSummarizedExperimentsyssystemfontstextshapingtibbletidyrtidyselecttweenrumaputf8uwotvctrsviporviridisLitewithrXVectoryulab.utils

Readme and manuals

Help Manual

Help pageTopics
Aggregates feature expression by cell clusters, per batch if provided.AggregateDataByBatch AggregateDataByBatch,SingleCellExperiment-method AggregateDataByBatch.SingleCellExperiment
Bin cell cluster probabilityBinCellClusterProbability BinCellClusterProbability,SingleCellExperiment-method BinCellClusterProbability.SingleCellExperiment
Cell bins feature correlationCellBinsFeatureCorrelation CellBinsFeatureCorrelation,SingleCellExperiment-method CellBinsFeatureCorrelation.SingleCellExperiment
Cell cluster probability distributionCellClusterProbabilityDistribution CellClusterProbabilityDistribution,SingleCellExperiment-method CellClusterProbabilityDistribution.SingleCellExperiment
Identification of feature markers for all clustersFindAllClusterMarkers FindAllClusterMarkers,SingleCellExperiment-method FindAllClusterMarkers.SingleCellExperiment
Differential expression between cell clustersFindClusterMarkers FindClusterMarkers,SingleCellExperiment-method FindClusterMarkers.SingleCellExperiment
Get ICP cell cluster probabilityGetCellClusterProbability GetCellClusterProbability,SingleCellExperiment-method GetCellClusterProbability.SingleCellExperiment
Get feature coefficientsGetFeatureCoefficients GetFeatureCoefficients,SingleCellExperiment-method GetFeatureCoefficients.SingleCellExperiment
Heatmap visualization of the expression of features by clustersHeatmapFeatures HeatmapFeatures,SingleCellExperiment-method HeatmapFeatures.SingleCellExperiment
Majority voting features by labelMajorityVotingFeatures MajorityVotingFeatures,SingleCellExperiment-method MajorityVotingFeatures.SingleCellExperiment
Elbow plot of the standard deviations of the principal componentsPCAElbowPlot PCAElbowPlot,SingleCellExperiment-method PCAElbowPlot.SingleCellExperiment
Plot cluster treePlotClusterTree PlotClusterTree,SingleCellExperiment-method PlotClusterTree.SingleCellExperiment
Plot dimensional reduction categorical variablesPlotDimRed PlotDimRed,SingleCellExperiment-method PlotDimRed.SingleCellExperiment
Plot dimensional reduction feature expressionPlotExpression PlotExpression,SingleCellExperiment-method PlotExpression.SingleCellExperiment
Prepare 'SingleCellExperiment' object for analysisPrepareData PrepareData,SingleCellExperiment-method PrepareData.SingleCellExperiment
Reference mappingReferenceMapping ReferenceMapping,SingleCellExperiment,SingleCellExperiment-method ReferenceMapping.SingleCellExperiment
Multi-level integrationRunParallelDivisiveICP RunParallelDivisiveICP,SingleCellExperiment-method RunParallelDivisiveICP.SingleCellExperiment
Principal Component AnalysisRunPCA RunPCA,SingleCellExperiment-method RunPCA.SingleCellExperiment
Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE)RunTSNE RunTSNE,SingleCellExperiment-method RunTSNE.SingleCellExperiment
Uniform Manifold Approximation and Projection (UMAP)RunUMAP RunUMAP,SingleCellExperiment-method RunUMAP.SingleCellExperiment
Summarise ICP cell cluster probabilitySummariseCellClusterProbability SummariseCellClusterProbability,SingleCellExperiment-method SummariseCellClusterProbability.SingleCellExperiment
Tabulate cell bins by groupTabulateCellBinsByGroup TabulateCellBinsByGroup,SingleCellExperiment-method TabulateCellBinsByGroup.SingleCellExperiment
Visualization of feature expression using violin plotsVlnPlot VlnPlot,SingleCellExperiment-method VlnPlot.SingleCellExperiment