clusterStab

clusterStab estimates the number of clusters and assesses their stability in high-throughput microarray gene expression data to support robust cluster analysis.

Key Features:

• Cluster Estimation: Determines the optimal number of clusters in microarray gene expression datasets.
• Stability Testing: Assesses the robustness of identified clusters across different datasets or experimental conditions.
• Bioconductor and R integration: Operates within the Bioconductor project and uses the R programming environment for analysis.
• Advanced statistical methods: Applies advanced statistical methods to quantify cluster number and stability in microarray data.

Scientific Applications:

• Gene expression pattern discovery: Identifies stable gene expression clusters to infer functional relationships among genes.
• Biomarker identification: Supports identification of biomarkers by isolating reproducible cluster-defined groups.
• Sample classification: Enables classification of samples based on molecular profiles derived from cluster assignments.

Methodology:

Uses R and Bioconductor packages and employs advanced statistical methods to analyze microarray gene expression data and assess cluster number and stability.