Package: Mfuzz 2.67.0

Matthias Futschik

Mfuzz: Soft clustering of omics time series data

The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface.

Authors:Matthias Futschik <[email protected]>

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Mfuzz.pdf |Mfuzz.html✨
Mfuzz/json (API)

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

Peer review:

Datasets:
  • yeast - Gene expression data of the yeast cell cycle
  • yeast.table - Gene expression data of the yeast cell cycle as table
  • yeast.table2 - Gene expression data of the yeast cell cycle as table

On BioConductor:Mfuzz-2.65.0(bioc 3.20)Mfuzz-2.64.0(bioc 3.19)

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

microarrayclusteringtimecoursepreprocessingvisualization

7.61 score 4 packages 320 scripts 2.1k downloads 160 mentions 23 exports 9 dependencies

Last updated 14 days agofrom:c8822617d1. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winNOTEOct 30 2024
R-4.5-linuxNOTEOct 30 2024
R-4.4-winNOTEOct 30 2024
R-4.4-macNOTEOct 30 2024
R-4.3-winNOTEOct 30 2024
R-4.3-macNOTEOct 30 2024

Exports:acorecselectionDminfill.NAfilter.NAfilter.stdkmeans2kmeans2.plotmembershipmestimatemfuzzmfuzz.plotmfuzz.plot2mfuzzColorBarMfuzzguioverlapoverlap.plotpartcoefrandomisestandardisestandardise2table2esettop.count

Dependencies:BiobaseBiocGenericsclassDynDoce1071MASSproxytkWidgetswidgetTools

Introduction to Mfuzz

Rendered fromMfuzz.Rnwusingutils::Sweaveon Oct 30 2024.

Last update: 2022-05-26
Started: 2013-11-01

Readme and manuals

Help Manual

Help pageTopics
Extraction of alpha cores for soft clustersacore
Repeated soft clustering for detection of empty clusters for estimation of optimised number of clusterscselection
Calculation of minimum centroid distance for a range of cluster numbers for estimation of optimised number of clustersDmin
Replacement of missing valuesfill.NA
Filtering of genes based on number of non-available expression values.filter.NA
Filtering of genes based on their standard deviation.filter.std
K-means clustering for gene expression datakmeans2
Plotting results for k-means clusteringkmeans2.plot
Calculating of membership values for new data based on existing clusteringmembership
Estimate for optimal fuzzifier mmestimate
Function for soft clustering based on fuzzy c-means.mfuzz
Plotting results for soft clusteringmfuzz.plot
Plotting results for soft clustering with additional optionsmfuzz.plot2
Plots a colour barmfuzzColorBar
Graphical user interface for Mfuzz packageMfuzzgui
Calculation of the overlap of soft clustersoverlap
Visualisation of cluster overlap and global clustering structureoverlap.plot
Calculation of the partition coefficient matrix for soft clusteringpartcoef
Randomisation of datarandomise
Standardization of expression data for clustering.standardise
Standardization in regards to selected time-pointstandardise2
Conversion of table to Expression set object.table2eset
Determines the number for which each gene has highest membership value in all clustertop.count
Gene expression data of the yeast cell cycleyeast
Gene expression data of the yeast cell cycle as tableyeast.table
Gene expression data of the yeast cell cycle as tableyeast.table2