Mfuzz webpage

Mfuzz webpage

Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise.

To overcome the limitations of hard clustering, we have implemented soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis.

Further information

Studies which have used Mfuzz can be found here and here.


Soft clustering was implemented here using the fuzzy c-means algorithm. A software package termed Mfuzz for soft clustering has been developed based on the open-source statistical language R. The Mfuzzgui-package provides a convient graphical user interface for most functions implemented in Mfuzz.

Note that most current packages can be obtained at the Bioconductor site .

The latest version also includes functions for the estimation of clustering parameters c and m as proposed in a recent publication.

Older versions

Mfuzz v.2.3.1(with Mfuzzgui included)


Using ExpressionSet objects (v.1.9.2)

Using exprSet objects (v.1.8.0) for older versions of R/Bioconductor)

Introduction to Mfuzz package (including instructions for installation): Pdf


Mfuzzgui requiring Mfuzz based on ExpressionSet objects. Mfuzzgui requiring Mfuzz based on exprSet objects.

Introduction to Mfuzzgui package (including instructions for installation): Pdf

For more information about about R and the related Bioconductor project: Questions and comments regarding the Mfuzz package can be addressed to Matthias Futschik.

Matthias Futschik
Last modified: Fri Mar 30 17:01:39 CEST 2012