Package: scFeatureFilter 1.27.0

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-1.27.0(bioc 3.21)scFeatureFilter-1.26.0(bioc 3.20)

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

immunooncologysinglecellrnaseqpreprocessinggeneexpression

4.30 score 20 scripts 298 downloads 13 exports 31 dependencies

Last updated 5 months agofrom:ef82e3e77e. Checks:6 OK, 3 NOTE. Indexed: yes.

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Exports:bin_scdatacalculate_cvscorrelate_windowscorrelations_to_densitiesdefine_top_genesdetermine_bin_cutofffilter_expression_tableget_mean_medianplot_correlations_distributionsplot_mean_varianceplot_metricplot_top_window_autocorsc_feature_filter

Dependencies:clicolorspacedplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbletidyselectutf8vctrsviridisLitewithr

Introduction to the scFeatureFilter package

Rendered fromIntroduction.Rmdusingknitr::rmarkdownon Mar 30 2025.

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