Simcluster

Simcluster performs clustering analysis of transcript enumeration data within the simplex space to account for compositional constraints in count-based digital gene expression methods such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), and sequencing-by-synthesis EST "digital northern".

Key Features:

• Clustering of enumeration data: Performs clustering analysis on count-based transcript abundance data produced by enumeration methods.
• Simplex-aware analysis: Operates within the simplex space to respect the constant-sum compositional constraints of transcript enumeration data.
• Compositional data framework: Adheres to a well-established mathematical framework tailored for compositional data analysis.
• Targeted technologies: Explicitly addresses data characteristics from SAGE, MPSS, and sequencing-by-synthesis EST "digital northern".
• Euclidean comparison: Produces clustering results that avoid distortions introduced by traditional Euclidean-based pattern recognition methods.

Scientific Applications:

• Digital gene expression analysis: Quantification and clustering of transcript abundance in SAGE, MPSS, and sequencing-by-synthesis EST datasets.
• Compositional transcriptome analysis: Pattern discovery and comparative analysis in gene expression datasets constrained to the simplex.
• Clustering for transcriptomics: Derivation of biologically relevant clusters from count-based transcript enumeration data.

Methodology:

Clustering analysis is performed within the simplex using a mathematical framework for compositional data analysis.