A Python Clustering Analysis Protocol of Genes Expression Data Sets.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene expression and Single Nucleotide Polymorphism (SNP) data are crucial for understanding disease prognosis, drug response, and toxicity.
• Cluster analysis is vital for discovering inherent subgroups and patterns within complex omics datasets without predefined categories.
• Effective analysis of microarray data requires understanding computational tools to ensure accurate interpretation and experimental design.

Purpose of the Study:

• To present a standardized protocol for analyzing gene expression datasets using K-means and DBSCAN clustering algorithms.
• To enable the identification of feature subsets with reduced redundancy and enhanced robustness in omics data.
• To accelerate the discovery of novel biomarkers through pathway enrichment analysis.

Main Methods:

• Application of K-means and DBSCAN clustering algorithms for omics data analysis.
• Development of a general protocol for unsupervised learning on gene expression and SNP data.
• Utilizing pathway enrichment analysis to identify significant biological pathways associated with identified clusters.

Main Results:

• The proposed protocol effectively analyzes omics data to identify robust and informative subgroups.
• Demonstrated the protocol's efficacy by analyzing a real-world dataset from the GEO database.
• The analysis facilitated the identification of potential biomarkers and hidden relationships between genes and phenotypes.

Conclusions:

• The developed clustering analysis protocol offers a systematic approach to omics data analysis.
• This method aids in biomarker discovery and the generation of predictive models for disease and drug response.
• Best practices and tips are provided to address common challenges in unsupervised learning for omics data.