Kvik

Kvik performs explorative analysis of functional genomics data from large-scale epidemiological studies, enabling multistudy reuse and pathway-based interpretation of gene expression.

Key Features:

• Three-Tier Architecture: Kvik implements a three-tier architecture for separation of data, logic, and presentation layers to support scalable analyses.
• Back-end Statistical Analyses: The back end is capable of performing complex statistical analyses on gene expression data.
• Metadata Retrieval: The framework provides efficient retrieval of metadata to support comprehensive data exploration across studies.
• Integration with Biological Pathways (Kvik Pathways): Kvik Pathways analyzes gene expression data in the context of biological pathways to facilitate pathway-level interpretation.
• Support for Epidemiological Study Designs: The framework has been applied to cross-sectional and case-control analyses, preserving study-design context during exploration.
• Facilitation of Multistudy Exploration: Kvik enables reuse of data across multiple research projects to avoid redundant analysis code and integration efforts.

Scientific Applications:

• Multistudy Functional Genomics Exploration: Comparative and integrative exploration of functional genomics data across multiple epidemiological studies.
• Pathway-Level Gene Expression Analysis: Interpretation of gene expression changes within biological pathways using Kvik Pathways.
• Epidemiological Analysis in NOWAC: Analysis of gene expression from healthy women with varying plasma ratios of essential fatty acids in the Norwegian Women and Cancer (NOWAC) cohort.

Methodology:

Uses a three-tier architecture with a back end for complex statistical analyses, efficient metadata retrieval, and modules for pathway-based analysis of gene expression (Kvik Pathways).