Package index • SplitClusterTest

All functions

calc_acc(): Calculate the accuracy (FDR, Power, F1 score) if truth is available
calc_inc_rate(): Calculate the inclusion rate
calc_tau(): Calculate the cutoff the mirror statistics
cluster_diff(): Calculate the difference across two clusters for each feature
dd(): Wrapper for the naive double-dipping method
debias_symmetry(): Debias the statistics under the null for symmetry
ds(): DS procedure
ds(<SingleCellExperiment>): DS procedure for Seurat object
ds(<matrix>): DS procedure for Matrix
est.Sigma(): estimate the covariance matrix (assuming there are two clusters)
gen_data_normal(): Generate simulation data with two gaussians
gen_data_pois(): Generate Poisson data with latent structure
gen_data_pois.matrix(): Matrix method for gen_data_pois (S3)
mds(): MDS procedure
mds(<SingleCellExperiment>): Multiple data splitting
mds(<matrix>): MDS procedure for matrix
mds1(): Conduct multiple data splitting
mds1_parallel(): Conduct multiple data splitting in parallel
mds2(): Aggregate multiple data splitting results, and return a selection set
mirror_stat(): Calculate the mirror statistics
myDiffTTest(): Modified Seurat::DiffTTest by extending the output with statistics in addition to p-values
myFindMarkers(): Gene expression markers of identity classes
myGLMDETest(): Modified Seurat::GLMDETest by extending the output with statistics in addition to p-values
myWilcoxDETest(): Modified Seurat::WilcoxDETest by extending the output with statistics in addition to p-values
perform_clustering(): Perform Clustering on a SingleCellExperiment object
rankSumTestWithCorrelation(): Wilcoxon rank sum test (adapted from limma::rankSumTestWithCorrelation)
sel_inc_rate(): Perform selection based on inclusion rate
simdata_1ct: Datasets Demo synthetic scRNA-seq data with one cell type based on DuoClustering2018::sce_full_Zhengmix4eq()
simdata_2ct: Demo synthetic scRNA-seq data with two cell types based on DuoClustering2018::sce_full_Zhengmix4eq()