Report/week10/gen_pptx.js

const pptxgen = require("pptxgenjs");

const pres = new pptxgen();
pres.layout = "LAYOUT_16x9";
pres.author = "Qian";
pres.title = "GRN-Guided Perturbation Prediction";

// ── Design Tokens ──────────────────────────────────────────────────
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// ====================================================================
// SLIDE 1 — Title
// ====================================================================
{
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  // accent bar left
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  s.addText("GRN-Guided Perturbation Prediction", {
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  s.addText("From Cascaded Flow Matching to RegFM", {
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  s.addText("Weekly Research Progress Report  |  2026-03-29", {
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// ====================================================================
// SLIDE 2 — Task Overview
// ====================================================================
{
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  addTitle(s, "Task: Single-Cell Perturbation Prediction", 2);

  // Left column — description
  s.addText(bullets([
    "Input: control expression + perturbed gene(s)",
    "Output: post-perturbation expression",
    "No cell-level pairing (destructive assay)",
    "Eval: DE overlap, Pearson, MSE, etc.",
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  s.addText(bullets([
    "~9,000 cells x 5,000 HVG",
    "105 CRISPR perturbations (KO + OE)",
    "39 held-out test perturbations",
    "Fold-1 split (additive)",
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    { text: "Given:  x_ctrl in R^G  (control expression),  p in {gene_1, gene_2}  (perturbation)", options: { fontSize: 12, fontFace: FONT_B, color: C.bodyText, breakLine: true, paraSpaceAfter: 2 } },
    { text: "Predict:  x_pert in R^G  (post-perturbation expression)", options: { fontSize: 12, fontFace: FONT_B, color: C.bodyText } },
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}

// ====================================================================
// SLIDE 3 — Baseline: scDFM
// ====================================================================
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  addTitle(s, "Baseline: scDFM (Flow Matching)", 3);

  s.addText(bullets([
    "Learns velocity field v(x, t) via flow matching",
    "Transports control distribution to perturbed",
    "DiffPerceiverBlock backbone (d=128, 4 layers)",
    "ODE solver: RK4, 20 steps",
  ]), { x: 0.6, y: 1.15, w: 5.0, h: 2.0 });

  // Metrics callout cards
  const metrics = [
    { label: "Pearson Delta", value: "0.866" },
    { label: "MSE", value: "0.0032" },
    { label: "DE Direction", value: "93.7%" },
    { label: "Discrimination", value: "0.980" },
  ];
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  // Limitation note
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    { text: "Limitation: ", options: { bold: true, fontSize: 13, color: C.red } },
    { text: "Genes treated as unstructured vector; no GRN modeling", options: { fontSize: 13, color: C.bodyText } },
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}

// ====================================================================
// SLIDE 4 — GRN-CCFM: Cascaded Approach
// ====================================================================
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  addTitle(s, "GRN-CCFM: Cascaded Approach", 4);

  // Core idea
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    { text: "Extract Attention-Delta from scGPT to capture GRN change, then use LatentForcing cascade to jointly generate GRN features and gene expression.", options: { fontSize: 13, fontFace: FONT_B, color: C.bodyText } },
  ], { x: 0.6, y: 1.15, w: 8.8, h: 1.0 });

  // Three pillars
  const pillars = [
    { title: "Attention-Delta", items: ["delta_attn = attn_tgt - attn_ctrl", "Captures regulatory change", "Sparse G x G matrix (~0.6%)"] },
    { title: "Cascaded Training", items: ["40% steps: latent flow only", "60% steps: expression flow only", "Two-stage ODE at inference"] },
    { title: "Architecture Fix", items: ["d_model: 128 -> 512", "Missing gene mask (7 sites)", "scGPT vocab alignment"] },
  ];
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// ====================================================================
// SLIDE 5 — Cascaded Variants Overview
// ====================================================================
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  addTitle(s, "Cascaded Variants: Design Space", 5);

  const tbl = styledTable(
    ["Variant", "Latent Dim", "Agg. Method", "delta_topk", "Key Idea"],
    [
      ["grn_att_only", "128 (bilinear)", "Bilinear head", "30", "Attention only, no SVD"],
      ["grn_svd", "128", "SVD dictionary", "30", "Fixed SVD basis"],
      ["grn_svd_cross", "128", "SVD + cross-attn", "30", "Learnable SVD queries"],
      ["grn_dense4", "4", "Multi-stats", "30", "Low-dim dense features"],
      ["grn_scalar", "1", "signed_L2 + norm", "100", "Scalar latent per gene"],
      ["dim1_ablation", "1", "Slice scGPT[0]", "30", "Ablation: 512d -> 1d"],
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    "Cascaded: choose_latent_p=0.4, latent_weight=1.0",
    "Inference: RK4 ODE, 20 steps each stage",
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// ====================================================================
// SLIDE 6 — Cascaded Results
// ====================================================================
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  s.background = { color: C.white };
  addTitle(s, "Cascaded Results: Evaluation Metrics", 6);

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    ["Model", "Pearson Delta", "MSE", "DE Direction", "Discrim."],
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      [{ text: "scDFM Baseline", bold: true }, { text: "0.866", bold: true }, { text: "0.0032", bold: true }, { text: "0.937", bold: true }, { text: "0.980", bold: true }],
      [{ text: "dim1_ablation", bold: true }, { text: "0.752", bold: true }, "0.0059", "0.878", "0.914"],
      ["grn_dense4", "0.122", "0.020", "0.780", "0.521"],
      ["grn_scalar (dtk100)", "0.087", "0.021", "0.793", "0.534"],
      ["grn_scalar (bs48)", "0.068", "0.026", "0.771", "0.533"],
      ["grn_att_only", "-0.097", "0.602", "0.747", "0.552"],
      ["grn_svd / svd_cross", "-0.096", "0.575", "0.746", "0.492"],
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  // Insight box
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    { text: "Only dim1 (d=1) approaches baseline; all high-dim cascaded variants fail", bold: true },
    "High-dim latent generation is the fundamental bottleneck",
    "grn_att_only / grn_svd: negative Pearson, MSE > 0.5",
  ], { fontSize: 12 }), { x: 0.8, y: 4.25, w: 8.5, h: 0.95 });
}

// ====================================================================
// SLIDE 7 — Failure Analysis
// ====================================================================
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  addTitle(s, "Failure Analysis: Why Cascaded Fails", 7);

  // Left: problem
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    "Latent loss stuck at ~1.0-2.0",
    "Target: sparse G x G matrix (~0.6% non-zero)",
    "Generating GRN is itself a hard problem",
    "Decoupled training prevents joint optimization",
    "Expression flow never benefits from GRN",
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  // Right: evidence
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  s.addText("Evidence from dim1 Ablation", {
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  s.addText(bullets([
    "scgpt_dim: 512 -> 1",
    "Latent loss converges normally",
    "Pearson delta: 0.752 (vs baseline 0.866)",
    "Confirms: high-dim target is the bottleneck",
    "But dim=1 loses most GRN information",
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  // Bottom conclusion
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    x: 0.5, y: 4.85, w: 9.0, h: 0.5,
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    x: 0.5, y: 4.85, w: 9.0, h: 0.5,
    fontSize: 13, fontFace: FONT_B, color: C.white, bold: true, align: "center", valign: "middle",
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}

// ====================================================================
// SLIDE 8 — Paradigm Shift
// ====================================================================
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  s.addShape(pres.shapes.RECTANGLE, {
    x: 0, y: 0, w: 0.12, h: 5.625, fill: { color: C.primary },
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  s.addText("Paradigm Shift", {
    x: 0.7, y: 0.8, w: 8.6, h: 0.6,
    fontSize: 32, fontFace: FONT_H, color: C.white, bold: true, margin: 0,
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  s.addText("From GRN Generation to Structural Supervision", {
    x: 0.7, y: 1.4, w: 8.6, h: 0.5,
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  // Key insight box
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    { text: "Key Insight: ", options: { bold: true, fontSize: 15, color: C.white } },
    { text: "Delta-attention is ", options: { fontSize: 15, color: C.white } },
    { text: "privileged information", options: { bold: true, italic: true, fontSize: 15, color: C.white } },
    { text: " -- available at training (from source + target), absent at inference (only source).", options: { fontSize: 15, color: C.white } },
  ], { x: 1.0, y: 2.5, w: 8.0, h: 0.8, fontFace: FONT_B });

  // Old vs New
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    x: 0.7, y: 3.8, w: 4.0, h: 1.2,
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    { text: "OLD: Cascaded", options: { bold: true, fontSize: 14, color: C.red, breakLine: true, paraSpaceAfter: 4 } },
    { text: "Generate GRN features (latent flow)", options: { fontSize: 13, color: "E8E8E8", breakLine: true, paraSpaceAfter: 2 } },
    { text: "-> Use for expression (expr flow)", options: { fontSize: 13, color: "E8E8E8" } },
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    { text: "NEW: RegFM", options: { bold: true, fontSize: 14, color: "66BB6A", breakLine: true, paraSpaceAfter: 4 } },
    { text: "Embed GRN as structural bias", options: { fontSize: 13, color: "E8E8E8", breakLine: true, paraSpaceAfter: 2 } },
    { text: "-> Soft-supervise with delta_attn at train", options: { fontSize: 13, color: "E8E8E8" } },
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  addSlideNum(s, 8);
}

// ====================================================================
// SLIDE 9 — RegFM Architecture
// ====================================================================
{
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  s.background = { color: C.white };
  addTitle(s, "RegFM: Architecture", 9);

  // Equation box
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    x: 0.6, y: 1.15, w: 8.8, h: 0.7,
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  s.addText([
    { text: "v(x, t)  =  \u03B1 \u00B7 v", options: { fontFace: "Calibri", fontSize: 22, color: C.primary, bold: true } },
    { text: "reg", options: { fontFace: "Calibri", fontSize: 15, color: C.primary, bold: true } },
    { text: "  +  (1 \u2013 \u03B1) \u00B7 v", options: { fontFace: "Calibri", fontSize: 22, color: C.primary, bold: true } },
    { text: "int", options: { fontFace: "Calibri", fontSize: 15, color: C.primary, bold: true } },
  ], {
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    align: "center", valign: "middle",
  });

  // Three component cards
  const comps = [
    {
      title: "v_reg (Regulatory)",
      color: C.primary,
      items: [
        "RegulatoryHead module",
        "Q, K, V from backbone h",
        "R = tanh(QK^T / sqrt(d_r))",
        "v_reg = Linear(R @ V)",
        "d_r = 32, params = 12K",
      ],
    },
    {
      title: "v_int (Intrinsic)",
      color: C.accent,
      items: [
        "Original ExprDecoder",
        "3-layer MLP",
        "Per-gene autonomous dynamics",
        "No inter-gene interaction",
        "Reused from scDFM baseline",
      ],
    },
    {
      title: "Gate (alpha)",
      color: C.green,
      items: [
        "VelocityGate module",
        "Input: h + pert_emb + t_emb",
        "MLP(384 -> 128 -> 1)",
        "Init: bias=-3, alpha~0.05",
        "Safe fallback to v_int",
      ],
    },
  ];
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      x: cx, y: 2.15, w: 2.95, h: 3.0,
      fill: { color: C.offWhite },
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      x: cx, y: 2.15, w: 2.95, h: 0.42,
      fill: { color: c.color },
    });
    s.addText(c.title, {
      x: cx, y: 2.15, w: 2.95, h: 0.42,
      fontSize: 12, fontFace: FONT_B, color: C.white, bold: true, align: "center", valign: "middle",
    });
    s.addText(bullets(c.items, { fontSize: 10.5 }), {
      x: cx + 0.1, y: 2.7, w: 2.75, h: 2.3,
    });
  });
}

// ====================================================================
// SLIDE 10 — RegFM Training & Loss
// ====================================================================
{
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  s.background = { color: C.white };
  addTitle(s, "RegFM: Training & Loss Design", 10);

  // Loss equation
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    x: 0.6, y: 1.15, w: 8.8, h: 0.6,
    fill: { color: C.offWhite },
  });
  s.addText([
    { text: "L  =  L", options: { fontFace: "Calibri", fontSize: 20, color: C.primary, bold: true } },
    { text: "vel", options: { fontFace: "Calibri", fontSize: 14, color: C.primary, bold: true } },
    { text: "  +  \u03BB \u00B7 L", options: { fontFace: "Calibri", fontSize: 20, color: C.primary, bold: true } },
    { text: "reg", options: { fontFace: "Calibri", fontSize: 14, color: C.primary, bold: true } },
    { text: "  +  \u03B3 \u00B7 L", options: { fontFace: "Calibri", fontSize: 20, color: C.primary, bold: true } },
    { text: "mmd", options: { fontFace: "Calibri", fontSize: 14, color: C.primary, bold: true } },
  ], {
    x: 0.6, y: 1.15, w: 8.8, h: 0.6,
    align: "center", valign: "middle",
  });

  // Loss details table
  const tbl = styledTable(
    ["Loss Term", "Target", "Weight", "Description"],
    [
      ["L_vel", "v_target = x1 - eps", "1.0", "Standard flow matching MSE"],
      ["L_reg", "delta_attention", "0.1", "R_theta aligned with GRN ground truth"],
      ["L_mmd", "Distribution matching", "0.5", "Sliced Wasserstein / MMD"],
    ]
  );
  s.addTable(tbl.rows, {
    x: 0.6, y: 2.0, w: 8.8,
    border: { pt: 0.5, color: "DDDDDD" },
    colW: [1.2, 2.5, 1.0, 4.1],
    rowH: [0.38, 0.35, 0.35, 0.35],
    autoPage: false,
  });

  // Training status
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    { text: "Training Status (RegFM + MMD)", options: { bold: true, fontSize: 14, fontFace: FONT_H, color: C.primary, breakLine: true, paraSpaceAfter: 6 } },
  ], { x: 0.6, y: 3.4, w: 4.5, h: 0.35 });

  const statusTbl = styledTable(
    ["Step", "L_vel", "L_reg", "L_mmd", "Total"],
    [
      ["5k", "0.169", "0.318", "0.025", "0.226"],
      ["20k", "0.126", "0.254", "0.017", "0.168"],
      ["32k", "0.112", "0.236", "0.016", "0.152"],
    ]
  );
  s.addTable(statusTbl.rows, {
    x: 0.6, y: 3.8, w: 5.5,
    border: { pt: 0.5, color: "DDDDDD" },
    colW: [0.8, 1.0, 1.0, 1.0, 1.0],
    rowH: [0.35, 0.3, 0.3, 0.3],
    autoPage: false,
  });

  // Key design notes
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    x: 6.5, y: 3.4, w: 3.0, h: 1.8,
    fill: { color: C.offWhite },
  });
  s.addShape(pres.shapes.RECTANGLE, {
    x: 6.5, y: 3.4, w: 0.07, h: 1.8,
    fill: { color: C.accent },
  });
  s.addText([
    { text: "Design Notes", options: { bold: true, fontSize: 12, fontFace: FONT_H, color: C.accent, breakLine: true, paraSpaceAfter: 4 } },
  ], { x: 6.75, y: 3.5, w: 2.6, h: 0.3 });
  s.addText(bullets([
    "Gate init: alpha ~ 0.05",
    "Diagonal removed from R",
    "Tanh bounds R to [-1, 1]",
    "Backbone: d_model=128",
  ], { fontSize: 10.5 }), { x: 6.75, y: 3.85, w: 2.6, h: 1.2 });
}

// ====================================================================
// SLIDE 11 — Schrodinger Bridge: Approach
// ====================================================================
{
  const s = pres.addSlide();
  s.background = { color: C.white };
  addTitle(s, "Schrodinger Bridge: Approach", 11);

  // Motivation
  s.addText(bullets([
    "FM: noise -> target (unpaired, indirect)",
    "SB: source -> target (optimal transport coupling)",
    "Natural fit for perturbation prediction",
  ], { fontSize: 13 }), { x: 0.6, y: 1.15, w: 9.0, h: 1.2 });

  // Variants table
  const tbl = styledTable(
    ["Variant", "Transport", "Score Head", "Anchoring", "Key Feature"],
    [
      ["A1 (baseline)", "SB-ODE", "None", "None", "Basic SB formulation"],
      ["A5 (full SDE)", "SB-SDE", "Full ASB", "None", "Score + velocity joint"],
      ["A6 (DSM aniso)", "SB-SDE", "Anisotropic", "None", "Per-gene noise scale"],
      ["SA1 (src-ODE)", "SB-ODE", "None", "Source cell", "Anchor at x_ctrl"],
      ["SA6 (src-SDE)", "SB-SDE", "Anisotropic", "Source cell", "Anchor + aniso score"],
    ]
  );
  s.addTable(tbl.rows, {
    x: 0.4, y: 2.55, w: 9.2,
    border: { pt: 0.5, color: "DDDDDD" },
    colW: [1.7, 1.2, 1.4, 1.3, 3.6],
    rowH: [0.38, 0.32, 0.32, 0.32, 0.32, 0.32],
    autoPage: false,
  });

  // Source-anchored note
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    x: 0.5, y: 4.35, w: 9.0, h: 0.75,
    fill: { color: C.offWhite },
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  s.addShape(pres.shapes.RECTANGLE, {
    x: 0.5, y: 4.35, w: 0.08, h: 0.75,
    fill: { color: C.accent },
  });
  s.addText([
    { text: "Source-Anchored: ", options: { bold: true, fontSize: 12, color: C.accent } },
    { text: "ODE starts from x_ctrl (not noise). Loss_v drops to ~0.0004 (vs ~0.3 for standard SB).", options: { fontSize: 12, color: C.bodyText } },
  ], { x: 0.8, y: 4.4, w: 8.5, h: 0.6, fontFace: FONT_B });
}

// ====================================================================
// SLIDE 12 — Schrodinger Bridge: Results
// ====================================================================
{
  const s = pres.addSlide();
  s.background = { color: C.white };
  addTitle(s, "Schrodinger Bridge: Results", 12);

  // Top table — use explicit large font cells
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    text: h, options: { bold: true, color: C.white, fill: { color: C.tableHead }, fontSize: 13, fontFace: FONT_B, align: "center", valign: "middle" },
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    [{ text: "scDFM Baseline", bold: true }, { text: "0.866", bold: true }, "0.0032", "0.937", "0.980"],
    [{ text: "SB A1 (baseline)", bold: true }, { text: "0.858", bold: true }, "0.0072", "0.902", "0.957"],
    ["SB A6 (aniso DSM)", "0.849", "0.0074", "0.901", "0.956"],
    ["SA1 / SA6", "Training...", "@ 195k", "-", "-"],
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    x: 0.5, y: 1.15, w: 9.0,
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    rowH: [0.42, 0.4, 0.4, 0.4, 0.4],
    autoPage: false,
  });

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    { text: "Training Loss Comparison", options: { bold: true, fontSize: 14, fontFace: FONT_H, color: C.primary, breakLine: true, paraSpaceAfter: 6 } },
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    text: h, options: { bold: true, color: C.white, fill: { color: C.tableHead }, fontSize: 12, fontFace: FONT_B, align: "center", valign: "middle" },
  }));
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    ["A1 Baseline", "0.26 - 0.40", "N/A", "Stable"],
    ["A6 DSM Aniso", "0.30 - 0.37", "0.76 - 0.80", "Better score"],
    ["SA1 Src-ODE", "~0.0005", "N/A", "Very low (anchored)"],
    ["SA6 Src-SDE", "~0.001", "~0.057", "Anchored + aniso"],
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    text: cell, options: { fontSize: 12, fontFace: FONT_B, color: C.darkText, fill: { color: ri % 2 === 0 ? C.white : C.tableAlt }, align: "center", valign: "middle" },
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  s.addTable([hdrL, ...dataL], {
    x: 0.5, y: 3.65, w: 9.0,
    border: { pt: 0.5, color: "DDDDDD" },
    colW: [2.2, 2.0, 1.8, 3.0],
    rowH: [0.38, 0.35, 0.35, 0.35, 0.35],
    autoPage: false,
  });
}

// ====================================================================
// SLIDE 13 — Comprehensive Comparison
// ====================================================================
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  const s = pres.addSlide();
  s.background = { color: C.white };
  addTitle(s, "Comprehensive Comparison", 13);

  const tbl = styledTable(
    ["Method", "Approach", "Pearson", "MSE", "DE Dir.", "Discrim."],
    [
      [{ text: "scDFM Baseline", bold: true }, "Flow Matching", { text: "0.866", bold: true }, { text: "0.003", bold: true }, { text: "0.937", bold: true }, { text: "0.980", bold: true }],
      [{ text: "SB A1", bold: true }, "Schrodinger Bridge", "0.858", "0.007", "0.902", "0.957"],
      ["SB A6", "SB + Aniso DSM", "0.849", "0.007", "0.901", "0.956"],
      [{ text: "dim1 ablation", bold: true }, "Cascaded (d=1)", "0.752", "0.006", "0.878", "0.914"],
      ["grn_dense4", "Cascaded (d=4)", "0.122", "0.020", "0.780", "0.521"],
      ["grn_scalar", "Cascaded (d=1, L2)", "0.087", "0.021", "0.793", "0.534"],
      ["grn_att_only", "Cascaded (bilinear)", "-0.097", "0.602", "0.747", "0.552"],
      ["grn_svd_cross", "Cascaded (SVD)", "-0.096", "0.575", "0.746", "0.492"],
      ["RegFM (20k)", "Structural Bias", "0.040", "0.128", "0.748", "0.505"],
    ]
  );
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    rowH: [0.38, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35],
    autoPage: false,
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  // Color legend
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    x: 0.5, y: 4.85, w: 9.0, h: 0.5,
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    { text: "Baseline (0.866), SB A1 (0.858)    ", options: { fontSize: 11, color: C.bodyText } },
    { text: "Mid tier: ", options: { bold: true, fontSize: 11, color: C.accent } },
    { text: "dim1 (0.752)    ", options: { fontSize: 11, color: C.bodyText } },
    { text: "Failed: ", options: { bold: true, fontSize: 11, color: C.red } },
    { text: "All high-dim cascaded variants", options: { fontSize: 11, color: C.bodyText } },
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}

// ====================================================================
// SLIDE 14 — Key Takeaways
// ====================================================================
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  const s = pres.addSlide();
  s.background = { color: C.white };
  addTitle(s, "Key Takeaways", 14);

  const takeaways = [
    { num: "1", title: "Cascaded GRN Generation Fails", desc: "High-dim latent target (G x G sparse) is fundamentally too hard to generate via flow matching." },
    { num: "2", title: "SB Competitive with FM Baseline", desc: "Schrodinger Bridge (A1: 0.858) nearly matches scDFM (0.866); source-anchored variants training." },
    { num: "3", title: "RegFM: New Paradigm", desc: "Treat delta_attn as privileged info; embed GRN as structural bias, not generation target." },
    { num: "4", title: "dim1 Confirms the Diagnosis", desc: "Only 1d latent converges; validates that task difficulty scales with latent dimensionality." },
  ];

  takeaways.forEach((t, i) => {
    const ty = 1.15 + i * 1.05;
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    s.addText(t.desc, {
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      fontSize: 12, fontFace: FONT_B, color: C.bodyText, margin: 0,
    });
  });
}

// ====================================================================
// SLIDE 15 — Next Steps
// ====================================================================
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  s.addShape(pres.shapes.RECTANGLE, {
    x: 0, y: 0, w: 0.12, h: 5.625, fill: { color: C.primary },
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  s.addText("Next Steps", {
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    fontSize: 32, fontFace: FONT_H, color: C.white, bold: true, margin: 0,
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  const steps = [
    { title: "RegFM Full Training", desc: "Continue to 100k+ steps; evaluate and compare with baseline at convergence" },
    { title: "SB Source-Anchored Eval", desc: "Evaluate SA1 / SA6 at 200k; compare ODE vs SDE transport" },
    { title: "RegFM vs SB vs Baseline", desc: "Head-to-head comparison on cell-eval + distributional metrics" },
    { title: "Distributional Evaluation", desc: "Apply new metrics (MMD, Energy Distance, C2ST, kNN) beyond conditional mean" },
    { title: "Interpretability Analysis", desc: "Visualize R_theta from RegFM; compare with known GRN structure" },
  ];

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// ── Write file ─────────────────────────────────────────────────────
pres.writeFile({ fileName: "/home/hp250092/ku50001222/qian/aivc/lfj/Report/week10/GRN_Progress_Report.pptx" })
  .then(() => console.log("PPTX saved successfully."))
  .catch((err) => console.error("Error:", err));