Package: Mfuzz 2.73.0

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]>

Mfuzz_2.73.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Mfuzz/json (API)

# Install 'Mfuzz' in R:

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

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-2.73.0(bioc 3.24)Mfuzz-2.72.0(bioc 3.23)

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

microarray clustering timecourse preprocessing visualization

6.92 score 3 packages 458 scripts 160 mentions 23 exports 10 dependencies

Last updated from:d8800aaa36. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	142
linux-devel-x86_64	NOTE	147
source / vignettes	OK	165
linux-release-x86_64	NOTE	150
macos-release-arm64	NOTE	93
macos-oldrel-arm64	NOTE	73
windows-devel	NOTE	88
windows-release	NOTE	82
windows-oldrel	NOTE	82
wasm-release	OK	97

Exports:acore cselection Dmin fill.NA filter.NA filter.std kmeans2 kmeans2.plot membership mestimate mfuzz mfuzz.plot mfuzz.plot2 mfuzzColorBar Mfuzzgui overlap overlap.plot partcoef randomise standardise standardise2 table2eset top.count

Dependencies:Biobase BiocGenerics class DynDoc e1071 generics MASS proxy tkWidgets widgetTools

Introduction to Mfuzz

Rendered fromMfuzz.Rnwusingutils::Sweave

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Extraction of alpha cores for soft clusters	acore
Repeated soft clustering for detection of empty clusters for estimation of optimised number of clusters	cselection
Calculation of minimum centroid distance for a range of cluster numbers for estimation of optimised number of clusters	Dmin
Replacement of missing values	fill.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 data	kmeans2
Plotting results for k-means clustering	kmeans2.plot
Calculating of membership values for new data based on existing clustering	membership
Estimate for optimal fuzzifier m	mestimate
Function for soft clustering based on fuzzy c-means.	mfuzz
Plotting results for soft clustering	mfuzz.plot
Plotting results for soft clustering with additional options	mfuzz.plot2
Plots a colour bar	mfuzzColorBar
Graphical user interface for Mfuzz package	Mfuzzgui
Calculation of the overlap of soft clusters	overlap
Visualisation of cluster overlap and global clustering structure	overlap.plot
Calculation of the partition coefficient matrix for soft clustering	partcoef
Randomisation of data	randomise
Standardization of expression data for clustering.	standardise
Standardization in regards to selected time-point	standardise2
Conversion of table to Expression set object.	table2eset
Determines the number for which each gene has highest membership value in all cluster	top.count
Gene expression data of the yeast cell cycle	yeast
Gene expression data of the yeast cell cycle as table	yeast.table
Gene expression data of the yeast cell cycle as table	yeast.table2