HDAnalyzeR: streamlining data analysis for biomarker research

Area of Science:

• Computational biology
• Bioinformatics
• Genomics

Background:

• Analyzing large-scale biological datasets is challenging due to fragmented tools and limited reproducibility.
• Existing computational biology tools are often developed in isolation, hindering unified workflows.
• A need exists for standardized solutions for exploratory data analysis and biomarker discovery.

Purpose of the Study:

• To present HDAnalyzeR, an R package for streamlined high-dimensional biological data analysis.
• To provide modular and reproducible workflows for various analytical tasks.
• To support both novice and expert users in biological data analysis.

Main Methods:

• Developed HDAnalyzeR as an extensible R package.
• Integrated quality control, dimensionality reduction, differential expression, and enrichment analysis.
• Incorporated visualization, metadata-aware modeling, and interactive app integration.

Main Results:

• HDAnalyzeR significantly reduced analysis time and code complexity in case studies.
• Achieved accurate classification of blood cancer types (AUC=1.0).
• Identified thousands of genes associated with solid tumors.

Conclusions:

• HDAnalyzeR promotes transparency, reproducibility, and publication-quality results in biological data analysis.
• The package enhances exploratory data analysis and biomarker discovery.
• HDAnalyzeR is a valuable tool for bioinformaticians and researchers working with high-dimensional data.