# A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

## Documentation for package ‘scBFA’ version 1.26.0

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

## Help Pages

BinaryPCA | Performs Binary PCA (as outlined in our paper). This function take the input of gene expression profile and perform PCA on gene detection pattern
celltype | Cell types as labels of example scRNA-seq dataset(exprdata)
celltype_toy | toy cell type vector with 3 cell types generated for 5 cells in toy dataset
diagnose | Perform diagnoisis of dispersion on the expression profile to check whether scBFA works on specific dataset
disperPlot | Reference dataset(disperPlot)
exprdata | scRNA-seq dataset(exprdata)
getGeneExpr | Function to extract gene expression matrix from input observation matrix
getLoading | Function to get low dimensional loading matrix
getScore | Function to get low dimensional embedding matrix
gradient | Calculate gradient of the negative log likelihood, used for calls to the optim() function.
gradient_chunk | Calculate gradient of the negative log likelihood, used for calls to the optim() function.
scBFA | Perform BFA model on the expression profile
scNoiseSim | simulation to generate scRNA-seq data with varying level of gene detection noise versus gene count noise
zinb_toy | example zinb object after fitting a toy dataset with 5 cells and 10 genes