# ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

## Documentation for package ‘ILoReg’ version 1.14.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

AnnotationScatterPlot | Visualiation of a custom annotation over nonlinear dimensionality reduction
AnnotationScatterPlot-method | Visualiation of a custom annotation over nonlinear dimensionality reduction
AnnotationScatterPlot.SingleCellExperiment | Visualiation of a custom annotation over nonlinear dimensionality reduction
CalcSilhInfo | Estimating optimal K using silhouette
CalcSilhInfo-method | Estimating optimal K using silhouette
CalcSilhInfo.SingleCellExperiment | Estimating optimal K using silhouette
ClusteringScatterPlot | Visualize the clustering over nonliner dimensionality reduction
ClusteringScatterPlot-method | Visualize the clustering over nonliner dimensionality reduction
ClusteringScatterPlot.SingleCellExperiment | Visualize the clustering over nonliner dimensionality reduction
DownOverSampling | Down- and oversample data
FindAllGeneMarkers | identification of gene markers for all clusters
FindAllGeneMarkers-method | identification of gene markers for all clusters
FindAllGeneMarkers.SingleCellExperiment | identification of gene markers for all clusters
FindGeneMarkers | Identification of gene markers for a cluster or two arbitrary combinations of clusters
FindGeneMarkers-method | Identification of gene markers for a cluster or two arbitrary combinations of clusters
FindGeneMarkers.SingleCellExperiment | Identification of gene markers for a cluster or two arbitrary combinations of clusters
GeneHeatmap | Heatmap visualization of the gene markers identified by FindAllGeneMarkers
GeneHeatmap-method | Heatmap visualization of the gene markers identified by FindAllGeneMarkers
GeneHeatmap.SingleCellExperiment | Heatmap visualization of the gene markers identified by FindAllGeneMarkers
GeneScatterPlot | Visualize gene expression over nonlinear dimensionality reduction
GeneScatterPlot-method | Visualize gene expression over nonlinear dimensionality reduction
GeneScatterPlot.SingleCellExperiment | Visualize gene expression over nonlinear dimensionality reduction
HierarchicalClustering | Hierarchical clustering using the Ward's method
HierarchicalClustering-method | Hierarchical clustering using the Ward's method
HierarchicalClustering.SingleCellExperiment | Hierarchical clustering using the Ward's method
LogisticRegression | Clustering projection using logistic regression from the LiblineaR R package
MergeClusters | Merge clusters
MergeClusters-method | Merge clusters
MergeClusters.SingleCellExperiment | Merge clusters
pbmc3k_500 | A toy dataset with 500 cells downsampled from the pbmc3k dataset.
PCAElbowPlot | Elbow plot of the standard deviations of the principal components
PCAElbowPlot-method | Elbow plot of the standard deviations of the principal components
PCAElbowPlot.SingleCellExperiment | Elbow plot of the standard deviations of the principal components
PrepareILoReg | Prepare 'SingleCellExperiment' object for 'ILoReg' analysis
PrepareILoReg-method | Prepare 'SingleCellExperiment' object for 'ILoReg' analysis
PrepareILoReg.SingleCellExperiment | Prepare 'SingleCellExperiment' object for 'ILoReg' analysis
RenameAllClusters | Renaming all clusters at once
RenameAllClusters-method | Renaming all clusters at once
RenameAllClusters.SingleCellExperiment | Renaming all clusters at once
RenameCluster | Renaming one cluster
RenameCluster-method | Renaming one cluster
RenameCluster.SingleCellExperiment | Renaming one cluster
RunICP | Iterative Clustering Projection (ICP) clustering
RunParallelICP | Run ICP runs parallerly
RunParallelICP-method | Run ICP runs parallerly
RunParallelICP.SingleCellExperiment | Run ICP runs parallerly
RunPCA | PCA transformation of the joint probability matrix
RunPCA-method | PCA transformation of the joint probability matrix
RunPCA.SingleCellExperiment | PCA transformation of the joint probability matrix
RunTSNE | Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE)
RunTSNE-method | Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE)
RunTSNE.SingleCellExperiment | Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE)
RunUMAP | Uniform Manifold Approximation and Projection (UMAP)
RunUMAP-method | Uniform Manifold Approximation and Projection (UMAP)
RunUMAP.SingleCellExperiment | Uniform Manifold Approximation and Projection (UMAP)
SelectKClusters | Selecting K clusters from hierarchical clustering
SelectKClusters-method | Selecting K clusters from hierarchical clustering
SelectKClusters.SingleCellExperiment | Selecting K clusters from hierarchical clustering
SelectTopGenes | Select top or bottom N genes based on a selection criterion
SilhouetteCurve | Silhouette curve
SilhouetteCurve-method | Silhouette curve
SilhouetteCurve.SingleCellExperiment | Silhouette curve
VlnPlot | Gene expression visualization using violin plots
VlnPlot-method | Gene expression visualization using violin plots
VlnPlot.SingleCellExperiment | Gene expression visualization using violin plots