fCI

fCI identifies differentially expressed genes (DEGs) across transcriptomic, proteomic and proteogenomic datasets by comparing distributional differences of fold-changes using f-divergence to detect condition-specific regulation.

Key Features:

• Integration of 'Omics' Data: Integrates transcriptomic, proteomic and proteogenomic datasets, including multi-dimensional and time-course data.
• DEG Identification via f-divergence: Identifies DEGs by computing differences in the distribution of fold-changes between control-control samples and case-control samples, using f-divergence as the information-theoretic measure.
• Data Type Versatility: Operates on both continuous and discrete data types.
• Detection of Distinct Regulation Patterns: Detects genes with distinct regulation patterns such as functional modulation, developmental changes, or misregulation in proteogenomics datasets.
• Publication: Described by Steen et al. (PMID: 26980280).

Scientific Applications:

• Gene Regulation Analysis: Analysis of gene expression regulation across conditions and time points.
• Regulatory Gene Identification: Identifying regulatory genes implicated in development, disease progression, or response to treatment.
• Biomarker and Target Discovery: Facilitating discovery of novel biomarkers and therapeutic targets via differential expression patterns.
• Proteogenomics Investigations: Application to proteogenomics datasets to reveal genes with distinct regulation patterns across molecular layers.

Methodology:

Computes distributional differences of fold-changes between control-control and case-control non-differentially expressed samples and quantifies these differences using f-divergence, an information-theoretic measure.