# ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8)

## Documentation for package ‘ccImpute’ version 1.15.0

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

## Help Pages

ccImpute | Impute Dropout Values in Single-Cell RNA Sequencing Data
ccImpute-method | Impute Dropout Values in Single-Cell RNA Sequencing Data
ccImpute.SingleCellExperiment | Impute Dropout Values in Single-Cell RNA Sequencing Data
colRanks_fast | Computes rankings for each column of a matrix in parallel.
computeDropouts | Impute Dropout Values in a Log-normalized Expression Count Matrix
cor_fast | Computes a Pearson
doSVD | Perform Truncated Singular Value Decomposition (SVD)
estkTW | Estimate the Number of Clusters (k) Using the Tracy-Widom Bound
findDropouts | Identify Dropout Events in Single-Cell Expression Data
getConsMtx | This function calculates an average consensus matrix from a set of clustering solutions. It filters out values below a specified minimum threshold ('consMin') and normalizes the remaining non-zero columns to sum to 1.
getCorM | Calculate Column-wise Correlation Matrix
getScale | Computes Means and Standard Deviations for Scaling
rowVars_fast | Computes Row Variances Efficiently
runKM | Perform Consensus K-Means Clustering
solver | Computes imputed expression matrix using linear eq solver
solver2 | Fast Calculation of "Dropout" values
sparseColRanks_fast | Computes rankings for each column of a matrix in parallel.
sparseSolver2 | Fast Calculation of "Dropout" values
wCor_fast | Computes a Weighted Pearson