Functional annotation-driven unsupervised clustering for single-cell data

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Documentation for package ‘ASURAT’ version 1.9.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

add_metadata	Add metadata of variables and samples.
ASURAT	Functional annotation-driven unsupervised clustering of SingleCell data.
bubble_sort	Perform bubble sorting, counting the number of steps.
cluster_genesets	Cluster each functional gene set into three groups.
compute_sepI_all	Compute separation indices for each cluster against the others.
compute_sepI_clusters	Compute separation indices of sign scores for given two clusters.
create_signs	Define signs for strongly and variably correlated gene sets.
human_COMSig_eg	A list of small Cell Ontology and MSigDB databases for human.
human_GO_eg	A list of small Gene Ontology database for human.
human_KEGG_eg	A list of small KEGG database for human.
makeSignMatrix	Create a new SingleCellExperiment object for sign-by-sample matrices.
pbmcs_eg	A list of SingleCellExperiment objects made from sign-sample matrices.
pbmc_eg	A SingleCellExperiment object made from a gene expression table.
plot_dataframe3D	Visualize a three-dimensional data with labels and colors.
plot_multiheatmaps	Visualize multivariate data by heatmaps.
remove_samples	Remove samples based on expression profiles across variables.
remove_signs	Remove signs including too few or too many genes.
remove_signs_manually	Remove signs by specifying keywords.
remove_signs_redundant	Remove redundant signs using semantic similarity matrices.
remove_variables	Remove variables based on expression profiles across samples.
remove_variables_second	Remove variables based on the mean expression levels across samples.
select_signs_manually	Select signs by specifying keywords.
swap_pass	Perform one-shot adjacent swapping for each element.