Man pages for beyergroup/ADImpute
Adaptive Dropout Imputer (ADImpute)

ArrangeData	Data trimming
CenterData	Data centering
CheckArguments_Impute	Argument check to Impute()
ChooseMethod	Method choice per gene
Combine	Combine imputation methods
ComputeMSEGenewise	Computation of MSE per gene
CreateArgCheck	Argument check
CreateTrainData	Preparation of training data for method evaluation
DataCheck_Matrix	Data check (matrix)
DataCheck_Network	Data check (network)
DataCheck_SingleCellExperiment	Data check (SingleCellExperiment)
DataCheck_TrLength	Data check (transcript length)
demo_data	Small dataset for example purposes
demo_net	Small regulatory network for example purposes
demo_sce	Small dataset for example purposes
EvaluateMethods	Imputation method evaluation on training set
GetDropoutProbabilities	Get dropout probabilities
HandleBiologicalZeros	Get dropout probabilities
Impute	Dropout imputation using different methods
ImputeBaseline	Impute using average expression across all cells
ImputeDrImpute	Use DrImpute
ImputeNetParallel	Network-based parallel imputation
ImputeNetwork	Network-based imputation
ImputeNPDropouts	Helper function to PseudoInverseSolution_percell
ImputePredictiveDropouts	Helper function to PseudoInverseSolution_percell
ImputeSAVER	Use SAVER
MaskData	Masking of entries for performance evaluation
MaskerPerGene	Helper mask function
network.coefficients	Transcriptome wide gene regulatory network
NormalizeRPM	RPM normalization
NormalizeTPM	TPM normalization
PseudoInverseSolution_percell	Network-based parallel imputation - Moore-Penrose...
ReadData	Data read
ReturnChoice	Wrapper for return of EvaluateMethods()
ReturnOut	Wrapper for return of Impute()
SetBiologicalZeros	Set biological zeros
SplitData	Selection of samples for training
transcript_length	Table for transcript length calculations
WriteCSV	Write csv file
WriteTXT	Write txt file