Package index • scDesignPop

All functions

calcParaVectors(): Generic function to compute model parameter vectors
calcParaVectors(<gam>): A calcParaVectors Method for gam (mgcv package) Objects
calcParaVectors(<gamlss>): A calcParaVectors Method for gamlss Objects
calcParaVectors(<glmmTMB>): A calcParaVectors Method for glmmTMB Objects
checkVectorContain(): Check membership of first vector compared to other vectors
checkVectorEqual(): Check if multiple vectors have same elements
constructDataPop(): Construct a list of input data
constructDesignMatrix(): Construct a Design Matrix Dataframe
constructEqtlGeno(): Construct eQTL annotation and genotype dataframe
constructFormula(): Constructs a Model Formula
constructPAFormula(): Construct a model formula for power analysis
constructSCE(): Construct a SingleCellExperiment object with specified covariates.
example_eqtlgeno: Example genotype data for cell-type-specific eQTLs
example_eqtlgeno_Bcell: Example B cell eQTL genotype data
example_genopc_new: Genotype principal components for new individuals
example_genopc_train: Genotype principal components for training individuals
example_sce: Example single-cell RNA-seq data (multi–cell type)
example_sce_Bcell: Example B cell single-cell RNA-seq data with pseudotime
extractFromSCE(): Extract data from SingleCellExperiment object
extractFromSeurat(): Extract data from Seurat object
extractParaPop(): Extract parameter matrix for a new covariate data frame
fitCopulaPop(): Fits copula for input
fitMarginalPop(): Fit marginal models for every feature
fitModel(): Fit a Marginal Model
fitPAModel(): Fit a marginal model for power analysis
marginal_list_sel: Example list of scDesignPop's marginal models
modifyMarginalModels(): Modify marginal models
modifyModelPara(): Modify parameters of a glmmTMB model object
plotCellProp(): Visualize the cell type proportions across individuals
plotReducedDimPop(): Dimensionality reduction and visualization for population-scale data
powerAnalysis(): Perform a cell-type-specific power analysis on eQTL effects
powerCICalculation(): Calculate a bootstrap confidence interval for each power
runPowerAnalysis(): The wrapper function for power analysis
simuCellProportion(): Simulate cell proportions using genotype principal components and population-level covariates
simuNewPop(): Simulate new data
simulatePADesignMatrix(): Simulate new design matrix for power analysis
visualizePowerCurve(): Visualize power as curves across study designs
visualizePowerHeatmap(): Visualize power as heatmaps across study designs