Journal
BIOINFORMATION
Volume 2, Issue 1, Pages 5-7Publisher
BIOMEDICAL INFORMATICS
DOI: 10.6026/97320630002005
Keywords
gene expression; soft clustering; software
Categories
Funding
- Deutsche Forschungsgemeinschaft [SFB 618]
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For the analysis of microarray data, clustering techniques are frequently used. Most of such methods are based on hard clustering of data wherein one gene (or sample) is assigned to exactly one cluster. Hard clustering, however, suffers from several drawbacks such as sensitivity to noise and information loss. In contrast, soft clustering methods can assign a gene to several clusters. They can overcome shortcomings of conventional hard clustering techniques and offer further advantages. Thus, we constructed an R package termed Mfuzz implementing soft clustering tools for microarray data analysis. The additional package Mfuzzgui provides a convenient TclTk based graphical user interface.
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