R: Soft clustering of time series gene expression data

# Soft clustering of time series gene expression data

## Documentation for package ‘Mfuzz’ version 2.8.0

- DESCRIPTION file.
- Overview of user guides and package vignettes; browse directory.

## Help Pages

acore | Extraction of alpha cores for soft clusters
cselection | Repeated soft clustering for detection of empty 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
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 microarray 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