R: Cell Type Identification and Discovery from Single Cell Gene Expression Data

# Cell Type Identification and Discovery from Single Cell Gene Expression Data

## Documentation for package ‘SignacX’ version 2.2.5

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

CID.entropy | Normalized Shannon entropy-based "unclassified" assignment
CID.GetDistMat | Computes distance matrix from edge list
CID.IsUnique | Extracts unique elements
CID.LoadData | Load data file from directory
CID.LoadEdges | Load edges from edge list for single cell network
CID.Louvain | Detects community substructure by Louvain community detection
CID.Normalize | Library size normalize
CID.smooth | Smoothing function
CID.writeJSON | Writes JSON file for SPRING integration
GenerateLabels | Generates cellular phenotype labels
Genes_Of_Interest | Genes of interest for drug discovery / disease biology research
GetModels_HPCA | Loads neural network models from GitHub
GetTrainingData_HPCA | Loads bootstrapped HPCA training data from GitHub
KSoftImpute | KNN-based imputation
MASC | Mixed effect modeling
ModelGenerator | Generates an ensemble of neural network models.
SaveCountsToH5 | Save count_matrix.h5 files for SPRING integration
Signac | Classification of cellular phenotypes in single cell data
SignacBoot | Generates bootstrapped single cell data
SignacFast | Fast classification of cellular phenotypes