tKOIAgent — Transcriptomics Knowledge Graph–Driven Omics Integration Agent

To integrate transcriptomics differential expression data with a knowledge graph, this Skill converts your dataset into knowledge-graph embeddings using the SPOKE KG to contextualize gene-level changes to pathways and diseases.

Network propagation for differential expression results works by applying the tkoi R package workflow to map gene-level changes onto a SPOKE knowledge graph, validating relationships across pathways, diseases, and cell types.

Yes, you need R and the tkoi package installed to run this network propagation analysis. The Skill enforces tkoi-only execution to ensure reproducible R-script workflows and structured multi-tab Excel reporting.

The best way to contextualize gene-level changes to pathways using SPOKE is running the tkoi workflow, which validates gene-disease-pathway relationships and outputs publication-ready multi-tab summaries for downstream interpretation.

Your differential expression dataset should be a cleaned CSV file, such as dge_data.csv. Providing this input allows the tkoi workflow to generate tkoi_result objects and structured multi-tab summaries.

No, you cannot use other tools as this Skill enforces tkoi-only analysis. It strictly orchestrates the tkoi package to ensure reproducible knowledge-graph-aware insights and SPOKE KG validation within the MCP framework.