Cell Type Identification and Discovery from Single Cell Gene Expression Data
Normalized Shannon entropy-based "unclassified" assignment
Computes distance matrix from edge list
Extracts unique elements
Load data file from directory
Load edges from edge list for single cell network
Detects community substructure by Louvain community detection
Library size normalize
Smoothing function
Writes JSON file for SPRING integration
Generates cellular phenotype labels
Get HEX colors
Loads neural network models from GitHub
Loads bootstrapped HPCA training data from GitHub
KNN-based imputation
Mixed effect modeling
Generates an ensemble of neural network models.
Save count_matrix.h5 files for SPRING integration
Classification of cellular phenotypes in single cell data
Generates bootstrapped single cell data
Fast classification of cellular phenotypes
An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.
Useful links