Open Access Software

Simcluster: clustering enumeration gene expression data on the simplex space

Ricardo ZN Vêncio1*, Leonardo Varuzza2, Carlos A de B Pereira2, Helena Brentani3 and Ilya Shmulevich1

  • * Corresponding author: Ricardo ZN Vêncio

  • † Equal contributors

Author Affiliations

1 Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA

2 BIOINFO-USP – Núcleo de Pesquisas em Bioinformática, Universidade de São Paulo, São Paulo, Brazil

3 Hospital do Câncer A. C. Camargo, São Paulo, Brazil

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BMC Bioinformatics 2007, 8:246  doi:10.1186/1471-2105-8-246

Published: 11 July 2007



Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space.


Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: webcite.


Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.