Predicting cell states and their variability in single-cell or spatial omics data


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Documentation for package ‘SVP’ version 1.0.2

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A C D E F G H I L M N P R S misc

-- A --

as_tbl_df convert the square matrix to long tidy table

-- C --

c-method Gene Set Variation Analysis Experiment methods
cal_lisa_f1 calculate the F1 value based on LISA result in the specified category.
cal_lisa_f1,SingleCellExperiment calculate the F1 value based on LISA result in the specified category.
cal_lisa_f1-method calculate the F1 value based on LISA result in the specified category.
CancerSEAEnsemble The Gene List of Cancer Single-cell State Atlas (CancerSEA)
CancerSEASymbol The Gene List of Cancer Single-cell State Atlas (CancerSEA)
CellCycle.Hs the Cell Cycle gene set
cluster.assign clusting and assign the label for each feature(specify the gene sets).
cluster.assign,SingleCellExperiment clusting and assign the label for each feature(specify the gene sets).
cluster.assign,SVPExperiment clusting and assign the label for each feature(specify the gene sets).
cluster.assign-method clusting and assign the label for each feature(specify the gene sets).
coerce-method The SVPExperiment class

-- D --

data_CacerSEA The Gene List of Cancer Single-cell State Atlas (CancerSEA)
data_CancerSEA The Gene List of Cancer Single-cell State Atlas (CancerSEA)
data_CellCycle.Hs the Cell Cycle gene set
data_hpda_spe_cell_dec an example of result of runSGSA by extracting with gsvaExp
data_sceSubPbmc a subset data of pbmck3 from SeuratData
data_SenMayo A gene set identifies senescent cells and predicts senescence-associated pathways across tissues

-- E --

extract_weight_adj extract the cell adjacent matrix from spatial space or reduction space
extract_weight_adj,SingleCellExperiment extract the cell adjacent matrix from spatial space or reduction space
extract_weight_adj-method extract the cell adjacent matrix from spatial space or reduction space

-- F --

fast_cor Calculation of correlations and associated p-values
fscoreDf features score matrix extract method
fscoreDf-method features score matrix extract method
fscoreDf<- features score matrix extract method
fscoreDf<--method features score matrix extract method
fscoreDfNames features score matrix extract method
fscoreDfNames-method features score matrix extract method
fscoreDfNames<- features score matrix extract method
fscoreDfNames<--method features score matrix extract method
fscoreDfs features score matrix extract method
fscoreDfs-method features score matrix extract method
fscoreDfs<- features score matrix extract method
fscoreDfs<--method features score matrix extract method

-- G --

gsvaExp Gene Set Variation Analysis Experiment methods
gsvaExp-method Gene Set Variation Analysis Experiment methods
gsvaExp<- Gene Set Variation Analysis Experiment methods
gsvaExp<--method Gene Set Variation Analysis Experiment methods
gsvaExpNames Gene Set Variation Analysis Experiment methods
gsvaExpNames-method Gene Set Variation Analysis Experiment methods
gsvaExpNames<- Gene Set Variation Analysis Experiment methods
gsvaExpNames<--method Gene Set Variation Analysis Experiment methods
gsvaExps Gene Set Variation Analysis Experiment methods
gsvaExps-method Gene Set Variation Analysis Experiment methods
gsvaExps<- Gene Set Variation Analysis Experiment methods
gsvaExps<--method Gene Set Variation Analysis Experiment methods

-- H --

hpda_spe_cell_dec an example of result of runSGSA by extracting with gsvaExp

-- I --

imgData-method Some accessor functions to get the internal slots of SVPExperiment
imgData<--method Some accessor functions to get the internal slots of SVPExperiment

-- L --

length-method Gene Set Variation Analysis Experiment methods
LISAResult LISAResult
LISAsce convert LISA result to SVPExperiment.
LISAsce,SingleCellExperiment convert LISA result to SVPExperiment.
LISAsce-method convert LISA result to SVPExperiment.

-- M --

mainGsvaExpName Gene Set Variation Analysis Experiment methods
mainGsvaExpName-method Gene Set Variation Analysis Experiment methods
mainGsvaExpName<- Gene Set Variation Analysis Experiment methods
mainGsvaExpName<--method Gene Set Variation Analysis Experiment methods
mob_marker_genes the marker genes of mouse olfactory bulb
mob_sce the single cell gene profiler of a mouse olfactory bulb

-- N --

names-method Gene Set Variation Analysis Experiment methods
names<--method Gene Set Variation Analysis Experiment methods

-- P --

plot_heatmap_globalbv plot_heatmap_globalbv
pred.cell.signature predict the cell signature according the gene sets or pathway activity score.
pred.cell.signature,SingleCellExperiment predict the cell signature according the gene sets or pathway activity score.
pred.cell.signature,SVPExperiment predict the cell signature according the gene sets or pathway activity score.
pred.cell.signature-method predict the cell signature according the gene sets or pathway activity score.

-- R --

runCORR runCORR
runCORR,SingleCellExperiment runCORR
runCORR,SVPExperiment runCORR
runCORR-method runCORR
runDetectMarker Detecting the specific cell features with nearest distance of cells in MCA space
runDetectMarker,SingleCellExperiment Detecting the specific cell features with nearest distance of cells in MCA space
runDetectMarker-method Detecting the specific cell features with nearest distance of cells in MCA space
runDetectSVG Detecting the spatially or single cell variable features with Moran's I or Geary's C
runDetectSVG,SingleCellExperiment Detecting the spatially or single cell variable features with Moran's I or Geary's C
runDetectSVG,SVPExperiment Detecting the spatially or single cell variable features with Moran's I or Geary's C
runDetectSVG-method Detecting the spatially or single cell variable features with Moran's I or Geary's C
runENCODE One hot encode for the specified cell category.
runENCODE,SingleCellExperiment One hot encode for the specified cell category.
runENCODE-method One hot encode for the specified cell category.
runGLOBALBV Global Bivariate analysis for spatial autocorrelation
runGLOBALBV,SingleCellExperiment Global Bivariate analysis for spatial autocorrelation
runGLOBALBV,SVPExperiment Global Bivariate analysis for spatial autocorrelation
runGLOBALBV-method Global Bivariate analysis for spatial autocorrelation
runKldSVG Detecting the spatially or single cell variable features with Kullback–Leibler divergence of 2D weighted kernel density estimation
runKldSVG,SingleCellExperiment Detecting the spatially or single cell variable features with Kullback–Leibler divergence of 2D weighted kernel density estimation
runKldSVG,SVPExperiment Detecting the spatially or single cell variable features with Kullback–Leibler divergence of 2D weighted kernel density estimation
runKldSVG-method Detecting the spatially or single cell variable features with Kullback–Leibler divergence of 2D weighted kernel density estimation
runLISA Local indicators of spatial association analysis
runLISA,SingleCellExperiment Local indicators of spatial association analysis
runLISA,SVPExperiment Local indicators of spatial association analysis
runLISA-method Local indicators of spatial association analysis
runLOCALBV Local Bivariate analysis with spatial autocorrelation
runLOCALBV,SingleCellExperiment Local Bivariate analysis with spatial autocorrelation
runLOCALBV,SVPExperiment Local Bivariate analysis with spatial autocorrelation
runLOCALBV-method Local Bivariate analysis with spatial autocorrelation
runMCA Run Multiple Correspondence Analysis
runMCA,SingleCellExperiment Run Multiple Correspondence Analysis
runMCA-method Run Multiple Correspondence Analysis
runSGSA Calculate the activity of gene sets in spatial or single-cell data with restart walk with restart and hyper test weighted.
runSGSA,SingleCellExperiment Calculate the activity of gene sets in spatial or single-cell data with restart walk with restart and hyper test weighted.
runSGSA-method Calculate the activity of gene sets in spatial or single-cell data with restart walk with restart and hyper test weighted.
runWKDE Calculating the 2D Weighted Kernel Density Estimation
runWKDE,SingleCellExperiment Calculating the 2D Weighted Kernel Density Estimation
runWKDE,SVPExperiment Calculating the 2D Weighted Kernel Density Estimation
runWKDE-method Calculating the 2D Weighted Kernel Density Estimation

-- S --

sceSubPbmc a subset data of pbmck3 from SeuratData
SenMayoSymbol A gene set identifies senescent cells and predicts senescence-associated pathways across tissues
show-method Some accessor functions to get the internal slots of SVPExperiment
spatialCoords-method Some accessor functions to get the internal slots of SVPExperiment
spatialCoords<-,SVPExperiment Some accessor functions to get the internal slots of SVPExperiment
spatialCoords<--method Some accessor functions to get the internal slots of SVPExperiment
spatialCoordsNames-method Some accessor functions to get the internal slots of SVPExperiment
spatialCoordsNames<--method Some accessor functions to get the internal slots of SVPExperiment
svDf spatial or single cell variable features matrix extract method
svDf-method spatial or single cell variable features matrix extract method
svDf<- spatial or single cell variable features matrix extract method
svDf<--method spatial or single cell variable features matrix extract method
svDfNames spatial or single cell variable features matrix extract method
svDfNames-method spatial or single cell variable features matrix extract method
svDfNames<- spatial or single cell variable features matrix extract method
svDfNames<--method spatial or single cell variable features matrix extract method
svDfs spatial or single cell variable features matrix extract method
svDfs-method spatial or single cell variable features matrix extract method
svDfs<- spatial or single cell variable features matrix extract method
svDfs<--method spatial or single cell variable features matrix extract method
SVP-accessors Some accessor functions to get the internal slots of SVPExperiment
SVPExperiment The SVPExperiment class
SVPExperiment-class The SVPExperiment class

-- misc --

[-method Gene Set Variation Analysis Experiment methods
[<--method Gene Set Variation Analysis Experiment methods