Soft clustering of time series gene expression data

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Documentation for package ‘Mfuzz’ version 2.64.0

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

Help Pages

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