Traceable Claims in Bioinformatics

What I need to produce the requested Plotly graphs (raw inputs)

• Sample metadata: sample IDs, tissue (lung tumor vs normal if available), cohort/batch, replicate mapping.
• NRF2 ChIP data: either (a) peak-level tables (peak ID, chromosome, start/end, normalized score, FDR), or (b) a signal matrix (sample × region bins) or bigWig-derived values.
• BACH1 ChIP data: same structure as NRF2 so regions are comparable.
• Common region definition strategy (choose one):
  • Union peaks (NRF2 ∪ BACH1) with per-sample signal extracted into one matrix.
  • Intersection peaks (NRF2 ∩ BACH1) only.
  • A fixed promoter/enhancer set (e.g., NRF2/BACH1 motif regions) with extracted ChIP signal.
• Normalization definition: e.g., counts-per-million, RPKM, or normalized signal already computed upstream (I need to know which you used).

If you upload peak tables / count matrices / bigWig-derived matrices, I can generate the full set of graphs you likely want (heatmaps, paired scatter plots, differential enrichment, and co-occupancy).

Graph set (ready-to-render once raw numbers are provided)

1. Per-sample NRF2 vs BACH1 scatter: x = NRF2 enrichment, y = BACH1 enrichment (color = tumor subtype/batch if provided).
2. Co-occupancy quadrants: proportion of regions with (NRF2-high/BACH1-high), (NRF2-high/BACH1-low), etc.
3. Region-level heatmap: rows = top variable regions (ranked by variance across samples), columns = samples; annotation tracks for NRF2 vs BACH1 high/low.
4. Δ enrichment bar plot: for matched regions, plot Δ = NRF2 − BACH1 summary statistics across samples (paired if you have matched replicates).
5. Ranking plot: list of top NRF2-specific regions and top BACH1-specific regions (based on effect size or differential score).
6. Optional locus profile plot: if you provide a few genomic loci of interest (chr:start-end), Plotly line plots per factor across samples.

Critical check: the provided “research data” doesn’t include NRF2/BACH1 ChIP measurements

The research excerpts you provided focus on other biological topics (e.g., Treg-driven ECM remodeling in breast cancer mouse models, and lung cancer epigenetic or transcriptional mechanisms unrelated to NRF2/BACH1 ChIP signal in lung tumors). None of it contains NRF2/BACH1 ChIP tables, peaks, or signal matrices for lung tumor samples—so I can’t construct data-faithful comparisons.

Upload format suggestion (so graphs can be produced without assumptions)

Upload a single wide matrix CSV with columns like: region_id, factor (NRF2/BACH1), sample_id, normalized_signal (or separate matrices per factor with identical region_id ordering). With that, I can immediately generate the Plotly figures.