Package: scFeatureFilter 1.25.0

Guillaume Devailly

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

An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise.

Authors:Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut]

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scFeatureFilter.pdf |scFeatureFilter.html✨
scFeatureFilter/json (API)
NEWS

# Install 'scFeatureFilter' in R:

install.packages('scFeatureFilter', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

scData_hESC - Expression data from 32 human embryonic stem cells

On BioConductor:scFeatureFilter-1.25.0(bioc 3.20)scFeatureFilter-1.24.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

13 exports 0.82 score 31 dependencies

Last updated 2 months agofrom:14c90ee3fe

Exports:bin_scdata calculate_cvs correlate_windows correlations_to_densities define_top_genes determine_bin_cutoff filter_expression_table get_mean_median plot_correlations_distributions plot_mean_variance plot_metric plot_top_window_autocor sc_feature_filter

Dependencies:cli colorspace dplyr fansi farver generics ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig R6 RColorBrewer rlang scales tibble tidyselect utf8 vctrs viridisLite withr

Introduction to the scFeatureFilter package

Angeles Arzalluz-Luque, Guillaume Devailly, Anagha Joshi

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2019-12-09
Started: 2017-09-26

Citation

Development and contributors

Readme and manuals

Help Manual

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