Man pages for ff98li/scRGNet
Perform scRNA-seq Analysis using Single-cell Graphical Neural Network (scGNN) Framework

AEFeature auto encoder class
calculate_knn_graphIsolation forest based KNN graph
GCNModelAEGCN Model Auto-enccoder
GCNModelVAEGCN Model Variational Auto-enccoder auto-encoding variational...
gene_countsscRNA-seq gene expression counts from GSE138852
gene_counts_smallA subset of GSE138852 scRNA-seq gene expression counts
generateNetworkInfer a cell-cell network from the encoded space using...
GraphCovolutionGraph Convolutional Neural Network Layer
InnerProductDecoderInner Product Decoder
loss_function_gcnGCN Loss function
loss_function_graphLoss function regularised by the graph information
plotCellNetCell network plotting function
plotDegreeShow Degree Distribution in Network
plotLogLog-log plot of Network
preprocessCSVPre-process scRNA-seq Count Matrix in CSV File
regulation_mse_loss_functionRegulation MSE Loss Function
runFeatureAEMain function of running scGNN feature auto-encoder
runLTMGGet discretised regulatory signals from LTMG model
runscRGNetLaunch Shiny webtool
scDatasetscRNA-seq dataset class
setHardwareA hardware setter to run the model
setHyperParamsHyper-parameter setter
trainMain Training Model
vallina_mse_loss_functionVallina MSE
ff98li/scRGNet documentation built on Jan. 14, 2022, 4:58 a.m.