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Pathway Enrichment Heatmap

Overview

The pathway enrichment heatmap visualization summarizes GSEA results across all trajectories and timepoints. The pipeline generates five different heatmaps to provide comprehensive views of pathway dynamics during cancer progression.

Heatmap Types

1. Top Varying Pathways

Shows the 50 pathways with the highest variance between first and last timepoints.

  • Purpose: Identify pathways that change most dramatically during progression
  • Colormap: RdBu_r (red-white-blue diverging)
  • Interpretation: Pathways with largest temporal dynamics

2. Top Upregulated Pathways

Shows the 50 pathways with highest average NES (most consistently upregulated).

  • Purpose: Identify pathways activated during progression
  • Colormap: YlGn (yellow-green sequential)
  • Interpretation: Consistently activated pathways across progression

3. Top Downregulated Pathways

Shows the 50 pathways with lowest average NES (most consistently downregulated).

  • Purpose: Identify pathways suppressed during progression
  • Colormap: YlOrBr (yellow-orange-brown sequential)
  • Interpretation: Consistently suppressed pathways across progression

4. High-Level Pathways

Shows 29 Reactome high-level pathways (top-level categories).

  • Purpose: Get broad overview of biological processes
  • Pathways: Cell Cycle, DNA Repair, Immune System, Metabolism, Signal Transduction, etc.
  • Colormap: RdBu_r
  • Interpretation: System-level view of cancer progression

5. Literature Pathways

Shows 33 pathways from kidney cancer literature.

  • Purpose: Focus on pathways known to be important in kidney cancer
  • Pathways: VHL/HIF pathway, PI3K/AKT/MTOR, Warburg effect, TCA cycle, etc.
  • Colormap: RdBu_r
  • Interpretation: Validation of known biology and discovery of new patterns

What the Heatmaps Show

  • Rows: Pathway names
  • Columns: Pseudotime progression (50 timepoints from early to late)
  • Values: Sum of NES (Normalized Enrichment Score) across all patients at each timepoint
  • Colors:
  • Red/Green/Brown (high values): Pathway enriched/upregulated
  • Blue/Yellow (low values): Pathway depleted/downregulated
  • White (middle): NES near zero or not significant

Filtering criteria

Only pathways meeting these criteria are included:

  1. FDR q-value < 0.05 (default, configurable)
  2. At least one significant enrichment across all trajectories

Files Generated

When you run the pathway heatmap generation, mainly, the following files are created:

Data File

  • pathway_heatmap_data.csv: Matrix of NES values (pathways × timepoints)

Visualization Files (for each heatmap type)

  1. top_varying_pathways.[pdf/png/svg]: Top 50 most varying pathways
  2. top_upregulated_pathways.[pdf/png/svg]: Top 50 upregulated pathways
  3. top_downregulated_pathways.[pdf/png/svg]: Top 50 downregulated pathways
  4. high_level_pathways.[pdf/png/svg]: 29 Reactome high-level pathways
  5. literature_pathways.[pdf/png/svg]: 33 kidney cancer literature pathways

All heatmaps are saved in PDF (publication quality), PNG (high-res), and SVG (vector) formats.

Implementation Details

The heatmaps are generated by:

  1. Filtering: Keep only results with FDR q-val < threshold
  2. Grouping: Group by Pathway name and Timepoint index
  3. Aggregation: Sum NES values across all patients at each timepoint

  4. Pathway Selection:

  5. Top varying: Calculate variance between first and last timepoint, select top 50

  6. Top upregulated: Calculate mean NES, select top 50 positive
  7. Top downregulated: Calculate mean NES, select top 50 negative
  8. High-level: Filter for 29 predefined Reactome pathways

  9. Literature: Filter for 33 kidney cancer-related pathways

  10. Pivoting: Create matrix with pathways as rows, timepoints as columns

  11. Visualization: Create heatmap with appropriate colormap and styling

Technical Notes

  • Uses PyDESeq2 for differential expression (not simple fold-change)
  • RSEM data is reverse log-transformed before DESeq2 analysis
  • Each trajectory timepoint compared against healthy controls
  • NES values summed across all patients for each (pathway, timepoint) pair

Troubleshooting

No significant pathways found

If heatmaps are empty or have few pathways:

  • Check FDR threshold (try 0.1 or 0.25)
  • Verify GSEA ran successfully for all trajectories
  • Check that PyDESeq2 analysis completed without errors
  • Ensure trajectory data has sufficient dynamic range

Missing literature/high-level pathways

If specific pathway categories are missing:

  • Pathway names must match exactly (case-sensitive)
  • Check pathway database (ReactomePathways.gmt) contains these pathways
  • Pathways may not be significant at current FDR threshold