Package: vsclust 1.7.0

Veit Schwaemmle

vsclust: Feature-based variance-sensitive quantitative clustering

Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis.

Authors:Veit Schwaemmle [aut, cre]

vsclust_1.7.0.tar.gz
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vsclust_1.7.0.tar.gz(r-4.5-noble)vsclust_1.7.0.tar.gz(r-4.4-noble)
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vsclust.pdf |vsclust.html✨
vsclust/json (API)
NEWS

# Install 'vsclust' in R:

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

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

artificial_clusters - Synthetic/artificial data comprising 5 clusters
protein_expressions - Data from a typical proteomics experiment

On BioConductor:vsclust-1.7.0(bioc 3.20)vsclust-1.6.0(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

18 exports 0.61 score 88 dependencies

Last updated 2 months agofrom:42dd5bf9b4

Exports:averageCond calcBHI ClustComp cvalidate.xiebeni determine_fuzz estimClust.plot estimClustNum mfuzz.plot optimalClustNum pcaWithVar PrepareForVSClust PrepareSEForVSClust runClustWrapper runVSClustApp SignAnalysis SignAnalysisPaired SwitchOrder vsclust_algorithm

Dependencies:abind askpass base64enc Biobase BiocBaseUtils BiocGenerics bslib cachem cli colorspace commonmark cpp11 crayon curl DelayedArray digest dplyr fansi farver fastmap fontawesome fs generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 glue gtable htmltools httpuv httr IRanges isoband jquerylib jsonlite labeling later lattice lifecycle limma magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime MultiAssayExperiment munsell nlme openssl pillar pkgconfig plyr promises purrr qvalue R6 rappdirs RColorBrewer Rcpp reshape2 rlang S4Arrays S4Vectors sass scales shiny sourcetools SparseArray statmod stringi stringr SummarizedExperiment sys tibble tidyr tidyselect UCSC.utils utf8 vctrs viridisLite withr xtable XVector zlibbioc

VSClust with Bioconductor objects

Veit Schwämmle

Rendered fromIntegrate_With_Bioconductor_Objects.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2023-03-26
Started: 2022-09-23

VSClust workflow

Veit Schwämmle

Rendered fromRun_VSClust_Workflow.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2023-03-26
Started: 2022-03-23

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
VSClust provides a powerful method to run variance-sensitive clustering	vsclust-package vsclust
Synthetic/artificial data comprising 5 clusters	artificial_clusters
Calculate mean over replicates	averageCond
Calculate "biological homogeneity index"	calcBHI
Function to run clustering with automatic fuzzifier settings (might become obsolete)	ClustComp
Xie Beni Index of clustering object	cvalidate.xiebeni
Determine individual fuzzifier values	determine_fuzz
Plotting results from estimating the cluster number	estimClust.plot
Wrapper for estimation of cluster number	estimClustNum
Plotting vsclust results	mfuzz.plot
Determine optimal cluster number from validity index	optimalClustNum
Visualize using principal component analysis (both loadings and scoring) including the variance from the replicates	pcaWithVar
Wrapper for statistical analysis	PrepareForVSClust
Wrapper for statistical analysis for SummarizedExperiment object	PrepareSEForVSClust
Data from a typical proteomics experiment	protein_expressions
Wrapper for running cluster analysis	runClustWrapper
Run VSClust as Shiny app	runVSClustApp
Unpaired statistical testing	SignAnalysis
Paired statistical testing	SignAnalysisPaired
arrange cluster member numbers from largest to smallest	SwitchOrder
Run the vsclust clustering algorithm	vsclust_algorithm