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


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Documentation for package ‘scFeatureFilter’ version 1.24.0

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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.