Package: ASURAT 1.11.0

Keita Iida

ASURAT: Functional annotation-driven unsupervised clustering for single-cell data

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

Authors:Keita Iida [aut, cre], Johannes Nicolaus Wibisana [ctb]

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

# Install 'ASURAT' in R:
install.packages('ASURAT', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • 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.
  • pbmc_eg - A SingleCellExperiment object made from a gene expression table.
  • pbmcs_eg - A list of SingleCellExperiment objects made from sign-sample matrices.

On BioConductor:ASURAT-1.9.0(bioc 3.20)ASURAT-1.8.0(bioc 3.19)

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

geneexpressionsinglecellsequencingclusteringgenesignaling

4.36 score 23 scripts 139 downloads 17 exports 48 dependencies

Last updated 23 days agofrom:433833d9fc. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-win-x86_64NOTEOct 30 2024
R-4.5-linux-x86_64NOTEOct 30 2024
R-4.4-win-x86_64NOTEOct 30 2024
R-4.4-mac-x86_64NOTEOct 30 2024
R-4.4-mac-aarch64NOTEOct 30 2024
R-4.3-win-x86_64NOTEOct 30 2024
R-4.3-mac-x86_64NOTEOct 30 2024
R-4.3-mac-aarch64NOTEOct 30 2024

Exports:add_metadatabubble_sortcluster_genesetscompute_sepI_allcompute_sepI_clusterscreate_signsmakeSignMatrixplot_dataframe3Dplot_multiheatmapsremove_samplesremove_signsremove_signs_manuallyremove_signs_redundantremove_variablesremove_variables_secondselect_signs_manuallyswap_pass

Dependencies:abindaskpassBiobaseBiocGenericscirclizeclueclustercodetoolscolorspaceComplexHeatmapcrayoncurlDelayedArraydigestdoParallelforeachGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongGlobalOptionshttrIRangesiteratorsjsonlitelatticeMatrixMatrixGenericsmatrixStatsmimemisc3dopensslplot3DpngR6RColorBrewerRcpprjsonS4ArraysS4VectorsshapeSingleCellExperimentSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc

ASURAT

Keita Iida

Rendered fromASURAT.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2022-04-01
Started: 2022-02-03

Readme and manuals

Help Manual

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