Function reference • SpatialDDLS

All functions

DeconvDLModel-class DeconvDLModel: The DeconvDLModel Class

PropCellTypes-class PropCellTypes: The PropCellTypes Class

SpatialDDLS-Rpackage: SpatialDDLS: an R package to deconvolute spatial transcriptomics data using deep neural networks

SpatialDDLS-class SpatialDDLS: The SpatialDDLS Class

ZinbParametersModel-class ZinbParametersModel: The Class ZinbParametersModel

barErrorPlot(): Generate bar error plots

barPlotCellTypes(): Bar plot of deconvoluted cell type proportions

blandAltmanLehPlot(): Generate Bland-Altman agreement plots between predicted and expected cell type proportions of test data

calculateEvalMetrics(): Calculate evaluation metrics on test mixed transcriptional profiles

cell.names() `cell.names<-`(): Get and set cell.names slot in a PropCellTypes object

cell.types() `cell.types<-`(): Get and set cell.types slot in a DeconvDLModel object

corrExpPredPlot(): Generate correlation plots between predicted and expected cell type proportions of test data

createSpatialDDLSobject(): Create a SpatialDDLS object

deconv.spots() `deconv.spots<-`(): Get and set deconv.spots slot in a SpatialDDLS object

deconvSpatialDDLS(): Deconvolute spatial transcriptomics data using trained model

distErrorPlot(): Generate box or violin plots showing error distribution

estimateZinbwaveParams(): Estimate parameters of the ZINB-WaVE model to simulate new single-cell RNA-Seq expression profiles

features() `features<-`(): Get and set features slot in a DeconvDLModel object

genMixedCellProp(): Generate training and test cell type composition matrices

getProbMatrix(): Getter function for the cell composition matrix

installTFpython(): Install Python dependencies for SpatialDDLS

interGradientsDL(): Calculate gradients of predicted cell types/loss function with respect to input features for interpreting trained deconvolution models

loadSTProfiles(): Loads spatial transcriptomics data into a SpatialDDLS object

loadTrainedModelFromH5(): Load from an HDF5 file a trained deep neural network model into a SpatialDDLS object

method() `method<-`(): Get and set method slot in a PropCellTypes object

mixed.profiles() `mixed.profiles<-`(): Get and set mixed.profiles slot in a SpatialDDLS object

model() `model<-`(): Get and set model slot in a DeconvDLModel object

plotDistances(): Plot distances between intrinsic and extrinsic profiles

plotHeatmapGradsAgg(): Plot a heatmap of gradients of classes / loss function wtih respect to the input

plotSpatialClustering(): Plot results of clustering based on predicted cell proportions

plotSpatialGeneExpr(): Plot normalized gene expression data (logCPM) in spatial coordinates

plotSpatialProp(): Plot predicted proportions for a specific cell type using spatial coordinates of spots

plotSpatialPropAll(): Plot predicted proportions for all cell types using spatial coordinates of spots

plotTrainingHistory(): Plot training history of a trained SpatialDDLS deep neural network model

plots() `plots<-`(): Get and set plots slot in a PropCellTypes object

preparingToSave(): Prepare SpatialDDLS object to be saved as an RDA file

prob.cell.types() `prob.cell.types<-`(): Get and set prob.cell.types slot in a SpatialDDLS object

prob.matrix() `prob.matrix<-`(): Get and set prob.matrix slot in a PropCellTypes object

project() `project<-`(): Get and set project slot in a SpatialDDLS object

saveRDS(): Save SpatialDDLS objects as RDS files

saveTrainedModelAsH5(): Save a trained SpatialDDLS deep neural network model to disk as an HDF5 file

set() `set<-`(): Get and set set slot in a PropCellTypes object

set.list() `set.list<-`(): Get and set set.list slot in a PropCellTypes object

showProbPlot(): Show distribution plots of the cell proportions generated by genMixedCellProp

simMixedProfiles(): Simulate training and test mixed spot profiles

simSCProfiles(): Simulate new single-cell RNA-Seq expression profiles using the ZINB-WaVE model parameters

single.cell.real() `single.cell.real<-`(): Get and set single.cell.real slot in a SpatialDDLS object

single.cell.simul() `single.cell.simul<-`(): Get and set single.cell.simul slot in a SpatialDDLS object

spatial.experiments() `spatial.experiments<-`(): Get and set spatial.experiments slot in a SpatialDDLS object

spatialPropClustering(): Cluster spatial data based on predicted cell proportions

test.deconv.metrics() `test.deconv.metrics<-`(): Get and set test.deconv.metrics slot in a DeconvDLModel object

test.metrics() `test.metrics<-`(): Get and set test.metrics slot in a DeconvDLModel object

test.pred() `test.pred<-`(): Get and set test.pred slot in a DeconvDLModel object

topGradientsCellType(): Get top genes with largest/smallest gradients per cell type

trainDeconvModel(): Train deconvolution model for spatial transcriptomics data

trained.model() `trained.model<-`(): Get and set trained.model slot in a SpatialDDLS object

training.history() `training.history<-`(): Get and set training.history slot in a DeconvDLModel object

zinb.params() `zinb.params<-`(): Get and set zinb.params slot in a SpatialDDLS object

zinbwave.model() `zinbwave.model<-`(): Get and set zinbwave.model slot in a ZinbParametersModel object