Function reference • digitalDLSorteR

All functions
`DigitalDLSorter-class` `DigitalDLSorter`	The DigitalDLSorter Class
`DigitalDLSorterDNN-class` `DigitalDLSorterDNN`	The DigitalDLSorterDNN Class
`ProbMatrixCellTypes-class` `ProbMatrixCellTypes`	The Class ProbMatrixCellTypes
`ZinbParametersModel-class` `ZinbParametersModel`	The Class ZinbParametersModel
`barErrorPlot()`	Generate bar error plots
`barPlotCellTypes()`	Bar plot of deconvoluted cell type proportions in bulk RNA-Seq samples
`blandAltmanLehPlot()`	Generate Bland-Altman agreement plots between predicted and expected cell type proportions from test data results
`bulk.simul()` `bulk.simul<-`()	Get and set `bulk.simul` slot in a `DigitalDLSorter` object
`calculateEvalMetrics()`	Calculate evaluation metrics for bulk RNA-Seq samples from test data
`cell.names()` `cell.names<-`()	Get and set `cell.names` slot in a `ProbMatrixCellTypes` object
`cell.types()` `cell.types<-`()	Get and set `cell.types` slot in a `DigitalDLSorterDNN` object
`corrExpPredPlot()`	Generate correlation plots between predicted and expected cell type proportions from test data
`createDDLSobject()`	Create a `DigitalDLSorter` object from single-cell RNA-seq and bulk RNA-seq data
`deconv.data()` `deconv.data<-`()	Get and set `deconv.data` slot in a `DigitalDLSorter` object
`deconv.results()` `deconv.results<-`()	Get and set `deconv.results` slot in a `DigitalDLSorter` object
`deconvDDLSObj()`	Deconvolute bulk gene expression samples (bulk RNA-Seq)
`deconvDDLSPretrained()`	Deconvolute bulk RNA-Seq samples using a pre-trained DigitalDLSorter model
`digitalDLSorteR`	digitalDLSorteR: an R package to deconvolute bulk RNA-Seq samples using single-cell RNA-seq data and neural networks
`distErrorPlot()`	Generate box plots or violin plots to show how the errors are distributed
`estimateZinbwaveParams()`	Estimate the parameters of the ZINB-WaVE model to simulate new single-cell RNA-Seq expression profiles
`features()` `features<-`()	Get and set `features` slot in a `DigitalDLSorterDNN` object
`generateBulkCellMatrix()`	Generate training and test cell composition matrices
`getProbMatrix()`	Getter function for the cell composition matrix
`installTFpython()`	Install Python dependencies for digitalDLSorteR
`interGradientsDL()`	Calculate gradients of predicted cell types/loss function with respect to input features for interpreting trained deconvolution models
`listToDDLS()`	Transform DigitalDLSorter-like list into an actual DigitalDLSorterDNN object
`listToDDLSDNN()`	Transform DigitalDLSorterDNN-like list into an actual DigitalDLSorterDNN object
`loadDeconvData()`	Load data to be deconvoluted into a DigitalDLSorter object
`loadTrainedModelFromH5()`	Load from an HDF5 file a trained Deep Neural Network model into a `DigitalDLSorter` object
`method()` `method<-`()	Get and set `method` slot in a `ProbMatrixCellTypes` object
`model()` `model<-`()	Get and set `model` slot in a `DigitalDLSorterDNN` object
`plotHeatmapGradsAgg()`	Plot a heatmap of gradients of classes / loss function wtih respect to the input
`plotTrainingHistory()`	Plot training history of a trained DigitalDLSorter Deep Neural Network model
`plots()` `plots<-`()	Get and set `plots` slot in a `ProbMatrixCellTypes` object
`preparingToSave()`	Prepare `DigitalDLSorter` object to be saved as an RDA file
`prob.cell.types()` `prob.cell.types<-`()	Get and set `prob.cell.types` slot in a `DigitalDLSorter` object
`prob.matrix()` `prob.matrix<-`()	Get and set `prob.matrix` slot in a `ProbMatrixCellTypes` object
`project()` `project<-`()	Get and set `project` slot in a `DigitalDLSorter` object
`saveRDS()`	Save `DigitalDLSorter` objects as RDS files
`saveTrainedModelAsH5()`	Save a trained `DigitalDLSorter` Deep Neural Network model to disk as an HDF5 file
`set()` `set<-`()	Get and set `set` slot in a `ProbMatrixCellTypes` object
`set.list()` `set.list<-`()	Get and set `set.list` slot in a `ProbMatrixCellTypes` object
`showProbPlot()`	Show distribution plots of the cell proportions generated by `generateBulkCellMatrix`
`simBulkProfiles()`	Simulate training and test pseudo-bulk RNA-Seq 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 `DigitalDLSorter` object
`single.cell.simul()` `single.cell.simul<-`()	Get and set `single.cell.simul` slot in a `DigitalDLSorter` object
`test.deconv.metrics()` `test.deconv.metrics<-`()	Get and set `test.deconv.metrics` slot in a `DigitalDLSorterDNN` object
`test.metrics()` `test.metrics<-`()	Get and set `test.metrics` slot in a `DigitalDLSorterDNN` object
`test.pred()` `test.pred<-`()	Get and set `test.pred` slot in a `DigitalDLSorterDNN` object
`topGradientsCellType()`	Get top genes with largest/smallest gradients per cell type
`trainDDLSModel()`	Train Deep Neural Network model
`trained.model()` `trained.model<-`()	Get and set `trained.model` slot in a `DigitalDLSorter` object
`training.history()` `training.history<-`()	Get and set `training.history` slot in a `DigitalDLSorterDNN` object
`zinb.params()` `zinb.params<-`()	Get and set `zinb.params` slot in a `DigitalDLSorter` object
`zinbwave.model()` `zinbwave.model<-`()	Get and set `zinbwave.model` slot in a `ZinbParametersModel` object

Reference

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