R: A correlation-based method for quality filtering of single-cell RNAseq data

# A correlation-based method for quality filtering of single-cell RNAseq data

## Documentation for package ‘scFeatureFilter’ version 1.32.0

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

## Help Pages

bin_scdata | Bin genes by mean expression.
calculate_cvs | Compute mean expression level, standard deviation and coefficient of variation of each feature.
correlate_windows | Calculate correlations against top window.
correlations_to_densities | Transform the correlation table to density distributions of correlation values
define_top_genes | Define the reference window using the most highly expressed features.
determine_bin_cutoff | Determine a threshold for selecting bins of features based on the metric table
filter_expression_table | Filter binned expression matrix
get_mean_median | Extract mean and median correlation coefficient values
plot_correlations_distributions | Produce a density plot of correlation values for each window of feature
plot_mean_variance | Produce a mean expression x coefficient of variation scatter plot.
plot_metric | Produce a bar chart of mean (or median) correlation coefficient per bin of feature.
plot_top_window_autocor | Utility plot to choose a top_window size
scData_hESC | Expression data from 32 human embryonic stem cells
sc_feature_filter | Filter scRNA-seq expression matrix to keep only highly informative features. Integrated pipeline.