Package index • CoPro

Object Creation

Create CoPro objects from spatial transcriptomics data

newCoProSingle(): Function to create a new object
newCoProMulti(): Create a new CoProMulti object for Multi-Slide Analysis
CreateCoPro(): Create a CoPro object, automatically choosing Single vs Multi and splitting large slices.
CoPro-class: CoPro object of spatial transcriptomics data
subsetData(): subsetData
show(<CoPro>): Show method for CoPro objects

The main CoPro analysis workflow

computePCA(): Compute PCA on Integrated Multi-Slide Data
computePCA(<CoProSingle>) computePCA(<CoProMulti>): Compute PCA on Single-Slide Data
computeDistance(): computeDistance between pairs of cell types
computeKernelMatrix(): Compute Kernel Matrix for CoPro
runSkrCCA(): runSkrCCA
computeNormalizedCorrelation(): Compute Normalized Correlation (approximation)
computeGeneAndCellScores(): computeGeneAndCellScores
computeRegressionGeneScores(): Compute regression-based gene scores

Retrieve analysis results

getCellScores(): Get cell scores from CoPro object
getCellScoresInSitu(): Get cell score and location information as a data.frame
getNormCorr(): Get normalized correlation vs Sigma squared values
getCorrOneType(): Retrieve the Correlation within one cell types
getCorrTwoTypes(): Retrieve the Correlation between two cell types
getDistMat(): Get Distance Matrix
getSelfDistMat(): Get Self-Distance Matrix
getKernelMatrix(): Get Kernel Matrix
getSelfKernelMatrix(): Get Self-Kernel Matrix
getColocScores(): Compute Colocalization Scores for All Cell Type Pairs
getSlideID(): Get slide IDs from CoPro object
getSlideList(): Get slide list from CoPro object
isMultiSlide(): Check if object is multi-slide

Transfer learned patterns to new slides or datasets

getTransferCellScores(): Get cell score by transferring gene weights from another slide By default, quantile normalization is used to ensure distribution match
getTransferNormCorr(): Compute Normalized Correlation from Transferred Cell Scores
getTransferBidirCorr(): Compute Bidirectional Correlation from Transferred Cell Scores
getTransferSelfBidirCorr(): Compute Self-Bidirectional Correlation from Transferred Cell Scores
quantile_normalize(): quantile normalize data
transfer_scores(): Transfer cell scores between matrices using gene weights

Self-correlation and single cell type spatial patterns

computeSelfDistance(): Compute Self-Distance Matrices for Multiple Cell Types
computeSelfKernel(): Compute Self-Kernel Matrices for Multiple Cell Types
computeSelfBidirCorr(): Compute Self-Bidirectional Correlation using skrCCA Results
computeBidirCorrelation(): Compute Bidirectional Correlation
ensureBidirCorrelationSlot(): Ensure object has bidirCorrelation slot

Gene scoring, testing, and regression

testGeneGLM(): testGeneGLM
smoothCellScoresMatrix(): Smooth the cell scores based on expression neighborhood

Statistical significance via spatial permutations

runSkrCCAPermu(): Run Spatial CCA with Permutation Testing
runSkrCCAPermu_FairSigma(): Run Permutation Test with Fair Sigma Selection
computeNormalizedCorrelationPermu(): Compute Normalized Correlation for Permutation Results
resample_spatial(): Spatial Resampling for Permutation Testing
generate_toroidal_permutations(): Generate Toroidal Shift Permutation Indices
compute_ground_truth_ncorr(): Compute Normalized Correlation from Ground Truth Scores
calculate_pvalue(): Calculate P-value from Permutation Results

Plotting functions

plotG12Functions(): Plot g_12(r) pair correlation functions for colocalization analysis
diagnose_bin_distribution(): Diagnose Bin Distribution

Helper and optimization functions

copro_download_data(): Download example datasets for CoPro vignettes
assignDistanceManually(): Assign distance matrix manually
optimize_bilinear(): SkrCCA optimization function for multiple groups (Single Slide) - First Component Uses flat kernel structure for consistent data access
optimize_bilinear_multi_slides(): Multi-slide SkrCCA optimization - First Component
optimize_bilinear_n(): Run multi version of skrCCA to detect subsequent components (Single Slide) Uses flat kernel structure for consistent data access
optimize_bilinear_n_multi_slides(): Multi-slide SkrCCA optimization - Multiple Components