Purpose | Installation | Installation from Bioconductor (Release) | Installation from GitHub (Development) | Preliminaries | Loading Datasets | Subsetting the Datasets | Visualization of Cell Type Annotations | Visualization of Cell Type Annotations in Reduced Dimensions | plotCellTypePCA() | calculateDiscriminantSpace() | Visualization of Marker Expressions | Visualization of QC and Annotation Scores | Evaluation of Dataset and Marker Gene Alignment | comparePCASubspace() | plotPairwiseDistancesDensity() | calculateWassersteinDistance() | calculateVarImpOverlap() | calculateAveragePairwiseCorrelation() | Detection and Analysis of Annotation Anomalies | Detection of Annotation Anomalies | Analysis of Annotation Anomalies | R Session Info

Introduction | Functions for Evaluation of Dataset Alignment | Statistical Measures to Assess Dataset Alignment | Marker Gene Alignment | Purpose and Applications | Preliminaries | Evaluation of Dataset Alignment | comparePCA() | comparePCASubspace() | plotPairwiseDistancesDensity() | Purpose | Functionality | Interpretation | calculateWassersteinDistance() | Code Example | calculateCramerPValue() | calculateHotellingPValue() | calculateAveragePairwiseCorrelation() | regressPC() | Query-only with Batch Information | Query + Reference with Batch Information | Diagnostic Value | calculateHVGOverlap() | How the Function Operates | calculateVarImpOverlap() | Overview | Usage | Interpretation: | R Session Info

Introduction | Preliminaries | Visualization of Query vs. Reference Dataset | Plot Reference and Query Cell Types Using MDS | Plot Principal Components for Different Cell Types | Plot Principal Components as Boxplots | Project Query Data onto Discriminant Space of Reference Data | Function Details | Example Application | Using Mahalanobis Distance for Anomaly Detection in Single-Cell RNA-Seq Data | Project Data onto Sliced Inverse Regression (SIR) Space of Reference Data | Visualization of Marker Expressions | Visualizing Gene Expression in Reduced Dimensions | Plotting Gene Expression Distribution | Visualization of QC and Annotation Scores | Scatter Plot: QC Stats vs Cell Type Annotation Scores | Histograms: QC Stats and Annotation Scores Visualization | Visualization of Gene Sets or Pathway Scores on Dimensional Reduction Plots | R Session Info

Introduction | Preliminaries | The detectAnomaly() Function | Function Overview | Description | Parameters | Return Value | detectAnomaly() Examples | Anomaly Detection with Reference and Query Data | Example 1: Cell-Type Specific Anomaly Detection | Example 2: Global Anomaly Detection | Anomaly Detection on Reference Data | Integrating Anomaly Detection with Cell Similarity Analysis Using PCA Loadings | Analyzing Cell Distances | calculateCellDistances() | Function Usage | Output | Example Workflow | calculateCellDistancesSimilarity() | R Session Info

Abstract | 1 Introduction | 2 RPESE Component Packages | 3 How to Use RPESE | References

1 Influence Functions Theoretical Background | 2 RPEIF Package Estimators and their Influence Functions | 3 Using the RPEIF Package to Evaluate Influence Functions and Compute Influence-Function Transformed Returns | 4 Outlier Cleaning | 5 Robust Esimator of Mean Influence Function | 6 Prewhitening