Mfuzz: A software package for soft clustering of microarray data

By Lokesh Kumar, Matthias E. Futschik. Bioinformation, 2007

1,648 citations recorded by the source corpus.

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. Availability The R package Mfuzz and Mfuzzgui are available at http://itb1.biologie.hu-berlin.de/~futschik/software/R/Mfuzz/index.html. Their distribution is subject to GPL version 2 license.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2937d984594cacc6e43ee4d0f100186c75edcb94

Citations in the corpus

• clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters (2012, neutral): “identified eight gene clusters from these DEGs, which have different expression patterns across samples, using soft clustering algorithm (Kumar and Futschik, 2007).”