SpaCEM3

SpaCEM3 performs spatial clustering and module detection on high-dimensional biological datasets to identify gene expression modules and spatial dependencies.

Key Features:

• Ad hoc algorithms: Specialized algorithms optimized for handling high-dimensional data and gene interactions.
• EM algorithm: Expectation-Maximization is used for soft clustering, providing probabilistic assignment of data points to clusters.
• Markov Random Fields (MRF): MRFs are used for spatial modeling to integrate spatial dependencies between neighboring observations.
• Handling missing data: Built-in mechanisms address missing observations to maintain robust analysis of incomplete datasets.
• Supervised and unsupervised classification: Supports both supervised and unsupervised approaches for classification of multidimensional and spatially-located data.
• Implementation: Implemented in C++ as part of the computational framework.

Scientific Applications:

• Genomics: Identification of modules or clusters within gene expression datasets.
• Systems biology: Discovery of patterns and interactions in high-dimensional biological data.
• Spatially-structured data analysis: Detection of spatial dependencies and modules in spatially-located gene expression data.

Methodology:

Uses the Expectation-Maximization algorithm for soft clustering, Markov Random Fields for spatial modeling, mechanisms for handling missing observations, and supports supervised and unsupervised classification; includes specialized algorithms for high-dimensional data and gene interaction modeling.