Package: ILoReg 1.15.0
Johannes Smolander
ILoReg: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data
ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided.
Authors:
ILoReg_1.15.0.tar.gz
ILoReg_1.15.0.zip(r-4.5)ILoReg_1.15.0.zip(r-4.4)ILoReg_1.15.0.zip(r-4.3)
ILoReg_1.15.0.tgz(r-4.4-any)ILoReg_1.15.0.tgz(r-4.3-any)
ILoReg_1.15.0.tar.gz(r-4.5-noble)ILoReg_1.15.0.tar.gz(r-4.4-noble)
ILoReg_1.15.0.tgz(r-4.4-emscripten)ILoReg_1.15.0.tgz(r-4.3-emscripten)
ILoReg.pdf |ILoReg.html✨
ILoReg/json (API)
NEWS
| # Install 'ILoReg' in R: |
| install.packages('ILoReg', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/elolab/iloreg/issues
- pbmc3k_500 - A toy dataset with 500 cells downsampled from the pbmc3k dataset.
On BioConductor:ILoReg-1.15.0(bioc 3.20)ILoReg-1.14.0(bioc 3.19)
Last updated 2 months agofrom:c16069ec3f
Exports:AnnotationScatterPlotCalcSilhInfoClusteringScatterPlotFindAllGeneMarkersFindGeneMarkersGeneHeatmapGeneScatterPlotHierarchicalClusteringMergeClustersPCAElbowPlotPrepareILoRegRenameAllClustersRenameClusterRunParallelICPRunPCARunTSNERunUMAPSelectKClustersSelectTopGenesSilhouetteCurveVlnPlot
Dependencies:abindaricodeaskpassBiobaseBiocGenericsbootcellrangerclasscliclustercodetoolscolorspacecowplotcpp11crayoncurldata.tableDelayedArraydendextendDescToolsdigestdoRNGdoSNOWdplyre1071ExactexpmfansifarverfastclusterforeachgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gldgluegridExtragtableherehmshttrIRangesisobanditeratorsjsonlitelabelinglatticeLiblineaRlifecyclelmommagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemunsellmvtnormnlmeopensslparallelDistpheatmappillarpkgconfigplyrpngprettyunitsprogressproxyR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppTOMLreadxlrematchreshape2reticulaterlangrngtoolsrootSolverprojrootRSpectrarstudioapiRtsneS4ArraysS4VectorsscalesSingleCellExperimentsnowSparseArraySparseMstringistringrSummarizedExperimentsystibbletidyselectUCSC.utilsumaputf8vctrsviridisviridisLitewithrXVectorzlibbioc
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Visualiation of a custom annotation over nonlinear dimensionality reduction | AnnotationScatterPlot AnnotationScatterPlot,SingleCellExperiment-method AnnotationScatterPlot.SingleCellExperiment |
| Estimating optimal K using silhouette | CalcSilhInfo CalcSilhInfo,SingleCellExperiment-method CalcSilhInfo.SingleCellExperiment |
| Visualize the clustering over nonliner dimensionality reduction | ClusteringScatterPlot ClusteringScatterPlot,SingleCellExperiment-method ClusteringScatterPlot.SingleCellExperiment |
| Down- and oversample data | DownOverSampling |
| identification of gene markers for all clusters | FindAllGeneMarkers FindAllGeneMarkers,SingleCellExperiment-method FindAllGeneMarkers.SingleCellExperiment |
| Identification of gene markers for a cluster or two arbitrary combinations of clusters | FindGeneMarkers FindGeneMarkers,SingleCellExperiment-method FindGeneMarkers.SingleCellExperiment |
| Heatmap visualization of the gene markers identified by FindAllGeneMarkers | GeneHeatmap GeneHeatmap,SingleCellExperiment-method GeneHeatmap.SingleCellExperiment |
| Visualize gene expression over nonlinear dimensionality reduction | GeneScatterPlot GeneScatterPlot,SingleCellExperiment-method GeneScatterPlot.SingleCellExperiment |
| Hierarchical clustering using the Ward's method | HierarchicalClustering HierarchicalClustering,SingleCellExperiment-method HierarchicalClustering.SingleCellExperiment |
| Clustering projection using logistic regression from the LiblineaR R package | LogisticRegression |
| Merge clusters | MergeClusters MergeClusters,SingleCellExperiment-method MergeClusters.SingleCellExperiment |
| A toy dataset with 500 cells downsampled from the pbmc3k dataset. | pbmc3k_500 |
| Elbow plot of the standard deviations of the principal components | PCAElbowPlot PCAElbowPlot,SingleCellExperiment-method PCAElbowPlot.SingleCellExperiment |
| Prepare 'SingleCellExperiment' object for 'ILoReg' analysis | PrepareILoReg PrepareILoReg,SingleCellExperiment-method PrepareILoReg.SingleCellExperiment |
| Renaming all clusters at once | RenameAllClusters RenameAllClusters,SingleCellExperiment-method RenameAllClusters.SingleCellExperiment |
| Renaming one cluster | RenameCluster RenameCluster,SingleCellExperiment-method RenameCluster.SingleCellExperiment |
| Iterative Clustering Projection (ICP) clustering | RunICP |
| Run ICP runs parallerly | RunParallelICP RunParallelICP,SingleCellExperiment-method RunParallelICP.SingleCellExperiment |
| PCA transformation of the joint probability matrix | RunPCA 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 |
| Selecting K clusters from hierarchical clustering | SelectKClusters SelectKClusters,SingleCellExperiment-method SelectKClusters.SingleCellExperiment |
| Select top or bottom N genes based on a selection criterion | SelectTopGenes |
| Silhouette curve | SilhouetteCurve SilhouetteCurve,SingleCellExperiment-method SilhouetteCurve.SingleCellExperiment |
| Gene expression visualization using violin plots | VlnPlot VlnPlot,SingleCellExperiment-method VlnPlot.SingleCellExperiment |
