const pptxgen = require("pptxgenjs");

const pres = new pptxgen();
pres.layout = "LAYOUT_16x9";
pres.author = "Qian";
pres.title = "GRN-Guided Cascaded Flow Matching";

// === Color Palette ===
const C = {
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  deepBlue:  "0E3B5C",
  teal:      "0D7377",
  seafoam:   "14B8A6",
  mint:      "99F6E4",       // brightened for dark bg readability
  gold:      "F59E0B",
  orange:    "F97316",
  coral:     "EF4444",
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  darkGray:  "334155",
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  accent1:   "3B82F6",  // blue for expression
  accent2:   "F59E0B",  // gold for GRN/latent
  accent3:   "10B981",  // green for bio
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let slideNum = 0;

// ============================================================
// SLIDE 1: Title
// ============================================================
slideNum++;
{
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  s.addText("GRN-Guided\nCascaded Flow Matching\nfor Single-Cell Perturbation Prediction", {
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  s.addText("Gene Regulatory Network meets Flow Matching", {
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// ============================================================
// SLIDE 2: Section — Task
// ============================================================
slideNum++;
addDividerSlide("1. Task", "Single-Cell Perturbation Prediction", slideNum);

// ============================================================
// SLIDE 3: Virtual Cell + Perturbation Types
// ============================================================
slideNum++;
{
  const s = addContentSlide("Virtual Cell & Perturbation Types", slideNum);

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  s.addText([
    { text: "Virtual Cell", options: { bold: true, fontSize: 13, color: C.teal, breakLine: true } },
    { text: "AI model simulating real cell behavior: given genotype, environment, perturbation \u2192 predict molecular state changes. Perturbation prediction is its most critical subtask.", options: { fontSize: 10.5, color: C.textMid } },
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    { title: "Cytokine Perturbation", desc: "Cytokines (IL-6, TNF-a, IFN-g) signaling", color: C.accent3 },
    { title: "Genetic Perturbation", desc: "CRISPR KO / CRISPRa OE / RNAi KD", color: C.accent2 },
  ];
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    { text: "x_ctrl + perturbation ID  \u2192  predict  x_pert        (x \u2208 R^G,  G \u2248 5000 HVG)", options: { color: C.textDark, fontSize: 12, fontFace: "Consolas" } },
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}

// ============================================================
// SLIDE 4: Section — Existing Methods
// ============================================================
slideNum++;
addDividerSlide("2. Existing Methods", "And their common blind spot", slideNum);

// ============================================================
// SLIDE 5: Methods Overview Table
// ============================================================
slideNum++;
{
  const s = addContentSlide("Existing Methods: Overview", slideNum);

  const methods = [
    { name: "Additive Shift",   cat: "Baseline",       approach: "Mean shift: x = x_ctrl + delta_mean", issue: "Ignores cell heterogeneity" },
    { name: "scGPT",            cat: "Foundation Model", approach: "Masked token completion (fine-tune)", issue: "Encodes absolute state, not change" },
    { name: "Geneformer",       cat: "Foundation Model", approach: "In-silico: delete gene token", issue: "Heuristic, no learned dynamics" },
    { name: "CPA",              cat: "Dedicated Model", approach: "VAE: basal + perturbation (additive)", issue: "Linear additivity too strong" },
    { name: "GEARS",            cat: "Dedicated Model", approach: "GNN on GO graph + cross-attention", issue: "Static prior graph, deterministic" },
    { name: "STATE",            cat: "Dedicated Model", approach: "Stacked attention on expression", issue: "Deterministic, no GRN modeling" },
    { name: "CellFlow",         cat: "Flow Matching",   approach: "FM + pretrained embedding cond.", issue: "Embedding = absolute state" },
    { name: "scDFM",            cat: "Flow Matching",   approach: "Conditional FM + DiffPerceiver", issue: "No GRN understanding" },
  ];

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    { text: "Perturbation \u2192 [black box] \u2192 Expression change", options: { color: C.white, fontSize: 13, breakLine: true } },
    { text: "No method explicitly models:  Perturbation \u2192 GRN rewiring \u2192 Expression change", options: { color: C.subtitleOnDark, fontSize: 11 } },
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}

// ============================================================
// SLIDE 6: Section — Motivation
// ============================================================
slideNum++;
addDividerSlide("3. Motivation", "Why GRN + Flow Matching?", slideNum);

// ============================================================
// SLIDE 7: Motivation 1 — Flow Matching
// ============================================================
slideNum++;
{
  const s = addContentSlide("Motivation 1: Flow Matching for Unpaired Data", slideNum);

  // Problem card (left)
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    { text: "The Pairing Problem", options: { bold: true, fontSize: 13, color: C.coral, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "Perturbation is destructive:", options: { fontSize: 11, color: C.textDark, breakLine: true } },
    { text: "One cell measured ONCE only", options: { fontSize: 11, color: C.textDark, breakLine: true } },
    { text: "No (x_ctrl, x_pert) pairs available", options: { fontSize: 11, color: C.coral, bold: true, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "Mean matching \u2192 loses heterogeneity", options: { bullet: true, fontSize: 10, color: C.textMid, breakLine: true } },
    { text: "Autoencoder \u2192 limited reconstruction", options: { bullet: true, fontSize: 10, color: C.textMid } },
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    { text: "Flow Matching Solution", options: { bold: true, fontSize: 13, color: C.accent3, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "Learn probabilistic transport mapping\nbetween distributions (not individual cells)", options: { fontSize: 11, color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "Only needs population-level distributions", options: { bullet: true, fontSize: 10, color: C.textMid, breakLine: true } },
    { text: "Conditional OT for efficient pairing", options: { bullet: true, fontSize: 10, color: C.textMid, breakLine: true } },
    { text: "Generative output = uncertainty estimation", options: { bullet: true, fontSize: 10, color: C.textMid } },
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  // Flow diagram
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  s.addShape(pres.shapes.RECTANGLE, { x: 3.5, y: 3.95, w: 3.0, h: 0.04, fill: { color: C.teal } });
  s.addText("learned velocity field (ODE)", {
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    fontSize: 9, fontFace: "Calibri", color: C.textMid, align: "center", margin: 0,
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}

// ============================================================
// SLIDE 8: Motivation 2 — GRN Cascade
// ============================================================
slideNum++;
{
  const s = addContentSlide("Motivation 2: Perturbation Propagates via GRN", slideNum);

  // Cascade diagram (left)
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    { text: "CRISPR knock-out Gene A", color: C.coral, bold: true },
    { text: "Gene A expression --> 0", color: C.coral, bold: false },
    { text: "Direct targets B, C, D change (1st order)", color: C.accent2, bold: false },
    { text: "B->E,F   C->G,H   D->I  ... (cascade)", color: C.accent2, bold: false },
    { text: "Thousands of genes ultimately affected", color: C.teal, bold: true },
  ];
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  s.addText("This cascade path = Gene Regulatory Network (GRN)", {
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    { text: "Existing Methods", options: { bold: true, fontSize: 12, color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Pert -> [black box] -> Expr", options: { fontSize: 11, fontFace: "Consolas", color: C.coral, breakLine: true } },
    { text: "End-to-end, no GRN understanding", options: { fontSize: 10, color: C.textMid } },
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  s.addShape(pres.shapes.RECTANGLE, { x: 6.3, y: 2.3, w: 3.2, h: 1.4, fill: { color: "D1FAE5" }, shadow: cardShadow() });
  s.addText([
    { text: "Our Approach", options: { bold: true, fontSize: 12, color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Pert -> GRN change -> Expr", options: { fontSize: 11, fontFace: "Consolas", color: C.accent3, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Explicitly model how perturbation rewires the regulatory network, then predict expression", options: { fontSize: 10, color: C.textDark } },
  ], { x: 6.5, y: 2.35, w: 2.9, h: 1.3, fontFace: "Calibri", valign: "top", margin: 0 });

  // Bottom insight
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  s.addText("Understanding GRN changes is a prerequisite for accurate expression prediction", {
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    fontSize: 12, fontFace: "Calibri", color: C.gold, bold: true, margin: 0, valign: "middle",
  });
}

// ============================================================
// SLIDE 9: Motivation 3 — scGPT Attention = GRN
// ============================================================
slideNum++;
{
  const s = addContentSlide("Motivation 3: scGPT Attention = Data-Driven GRN", slideNum);

  // Left: explanation
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  s.addText([
    { text: "scGPT Transformer Attention", options: { bold: true, fontSize: 13, color: C.teal, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "attn[i][j] high -> gene j influences gene i", options: { fontSize: 11, fontFace: "Consolas", color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "= Context-dependent, data-driven GRN", options: { fontSize: 12, color: C.navy, bold: true, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "vs static GO graph:", options: { bold: true, fontSize: 10, color: C.textMid, breakLine: true } },
    { text: "Changes with cell state (context-aware)", options: { bullet: true, fontSize: 10, color: C.textMid, breakLine: true } },
    { text: "Learned from massive scRNA-seq data", options: { bullet: true, fontSize: 10, color: C.textMid, breakLine: true } },
    { text: "Captures non-linear regulatory logic", options: { bullet: true, fontSize: 10, color: C.textMid } },
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    { text: "Attention-Delta", options: { bold: true, fontSize: 13, color: C.accent2, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 5 } },
    { text: "Same frozen scGPT, two inputs:", options: { fontSize: 11, color: C.textDark, breakLine: true } },
    { text: "attn_ctrl = scGPT(x_ctrl)", options: { fontSize: 10.5, fontFace: "Consolas", color: C.accent1, breakLine: true } },
    { text: "attn_pert = scGPT(x_pert)", options: { fontSize: 10.5, fontFace: "Consolas", color: C.coral, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "delta_attn = attn_pert - attn_ctrl", options: { fontSize: 11, fontFace: "Consolas", color: C.navy, bold: true, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Directly captures how perturbation\nrewires gene regulatory relationships", options: { fontSize: 10, color: C.textDark } },
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  // Bottom: GRN features formula
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  s.addText([
    { text: "GRN Change Features:", options: { bold: true, fontSize: 14, color: C.gold, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "z = delta_attn  x  gene_embeddings", options: { fontSize: 17, fontFace: "Consolas", color: C.white, breakLine: true } },
    { text: "    (G x G)          (G x 512)    -->    (G x 512)", options: { fontSize: 11, fontFace: "Consolas", color: C.subtitleOnDark } },
  ], { x: 0.7, y: 3.35, w: 8.6, h: 1.15, fontFace: "Calibri", valign: "top", margin: 0 });
}

// ============================================================
// SLIDE 10: Section — Our Method
// ============================================================
slideNum++;
addDividerSlide("4. Our Method", "GRN-Guided Cascaded Flow Matching", slideNum);

// ============================================================
// SLIDE 11: Two-Stage Cascaded FM
// ============================================================
slideNum++;
{
  const s = addContentSlide("Two-Stage Cascaded Flow Matching", slideNum);

  // Stage 1 card
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    { text: "Stage 1: GRN Latent Flow", options: { bold: true, fontSize: 13, color: C.accent2, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 6 } },
    { text: "noise ==(ODE)==> GRN features", options: { fontSize: 12, fontFace: "Consolas", color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 6 } },
    { text: "\"Understand how gene regulation\n changes under perturbation\"", options: { fontSize: 11, color: C.accent2, italic: true, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 6 } },
    { text: "t_latent: 0 -> 1", options: { fontSize: 10, fontFace: "Consolas", color: C.textMid, breakLine: true } },
    { text: "t_expr = 0 (expression frozen)", options: { fontSize: 10, fontFace: "Consolas", color: C.textMid } },
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  // Stage 2 card
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  s.addShape(pres.shapes.RECTANGLE, { x: 5.5, y: 0.9, w: 0.07, h: 2.0, fill: { color: C.accent1 } });
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    { text: "Stage 2: Expression Flow", options: { bold: true, fontSize: 13, color: C.accent1, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 6 } },
    { text: "noise ==(ODE)==> expression", options: { fontSize: 12, fontFace: "Consolas", color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 6 } },
    { text: "\"Based on GRN understanding,\n predict gene expression changes\"", options: { fontSize: 11, color: C.accent1, italic: true, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 6 } },
    { text: "t_expr: 0 -> 1", options: { fontSize: 10, fontFace: "Consolas", color: C.textMid, breakLine: true } },
    { text: "t_latent = 1 (GRN complete)", options: { fontSize: 10, fontFace: "Consolas", color: C.textMid } },
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  s.addText("Bio intuition: First understand HOW regulation changes, THEN predict WHAT expression changes", {
    x: 0.7, y: 3.18, w: 8.6, h: 0.5,
    fontSize: 12, fontFace: "Calibri", color: C.gold, bold: true, margin: 0, valign: "middle",
  });

  // Training note card
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  s.addText([
    { text: "Cascaded Training:  ", options: { bold: true, fontSize: 12, color: C.teal } },
    { text: "Probabilistic switching (not simultaneous)", options: { fontSize: 12, color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "40%  Train Latent Flow:  t_latent random, t_expr=0,   loss_latent only", options: { fontSize: 10, fontFace: "Consolas", color: C.accent2, breakLine: true } },
    { text: "60%  Train Expr Flow:    t_expr random,   t_latent~1, loss_expr only", options: { fontSize: 10, fontFace: "Consolas", color: C.accent1 } },
  ], { x: 0.7, y: 4.0, w: 8.6, h: 0.75, fontFace: "Calibri", valign: "top", margin: 0 });
}

// ============================================================
// SLIDE 12: Model Architecture
// ============================================================
slideNum++;
{
  const s = addContentSlide("Model Architecture", slideNum);

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  // --- Top: Two input streams ---
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  s.addText([
    { text: "Expression Stream", options: { bold: true, fontSize: 11, color: C.accent1, breakLine: true } },
    { text: "GeneEnc(id) + ValueEnc(x_t, x_ctrl) -> tokens", options: { fontSize: 8.5, fontFace: "Consolas", color: C.textMid } },
  ], { x: 0.9, y: 1.03, w: 3.3, h: 0.55, fontFace: "Calibri", valign: "middle", margin: 0 });

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    { text: "LatentEmbedder(z_t) -> tokens", options: { fontSize: 8.5, fontFace: "Consolas", color: C.textMid } },
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  // Plus
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  // Down arrow
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  s.addText("V", { x: 4.5, y: 1.8, w: 0.5, h: 0.2, fontSize: 10, color: C.teal, align: "center", valign: "middle", margin: 0 });

  // --- Shared Backbone ---
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  s.addText([
    { text: "Shared Backbone", options: { bold: true, fontSize: 13, color: C.white, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 3 } },
    { text: "DiffPerceiverBlock x 4", options: { fontSize: 11, color: C.mint, breakLine: true } },
    { text: "(GeneadaLN + Adapter + DiffAttn)", options: { fontSize: 9, color: C.mint, breakLine: true } },
    { text: "d_model = 512", options: { fontSize: 10, fontFace: "Consolas", color: C.white } },
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  // --- Conditioning box ---
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    x: 6.05, y: 2.1, w: 3.1, h: 0.55,
    fontSize: 9, fontFace: "Consolas", color: C.accent1, valign: "middle", align: "center", margin: 0,
  });
  s.addText("Cond.", {
    x: 5.35, y: 2.15, w: 0.6, h: 0.45,
    fontSize: 8, fontFace: "Calibri", color: C.textMid, valign: "middle", align: "center", margin: 0,
  });

  // --- Frozen scGPT box ---
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    { text: "Frozen scGPT", options: { bold: true, fontSize: 11, color: C.darkGray, breakLine: true } },
    { text: "(no gradient)", options: { fontSize: 8, color: C.midGray, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 3 } },
    { text: "x_ctrl, x_pert", options: { fontSize: 9, fontFace: "Consolas", color: C.accent1, breakLine: true } },
    { text: "  -> attention layer 11", options: { fontSize: 9, fontFace: "Consolas", color: C.midGray, breakLine: true } },
    { text: "delta_attn x gene_emb", options: { fontSize: 9, fontFace: "Consolas", color: C.accent2, breakLine: true } },
    { text: "  -> z_target (B,G,512)", options: { fontSize: 9, fontFace: "Consolas", color: C.accent2 } },
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  // --- Two decoder heads ---
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    { text: "v_expr (B,G)", options: { fontSize: 9, fontFace: "Consolas", color: C.textMid } },
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}

// ============================================================
// SLIDE 13: Section — Challenges
// ============================================================
slideNum++;
addDividerSlide("5. Current Challenges", "And proposed solutions", slideNum);

// ============================================================
// SLIDE 14: Challenges + Solutions
// ============================================================
slideNum++;
{
  const s = addContentSlide("Challenges & Solutions", slideNum);

  // Challenge 1 (top-left)
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  s.addText([
    { text: "Challenge 1: Noise in Attention", options: { bold: true, fontSize: 12, color: C.coral, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Attention: 5000x5000 = 25M non-zero values", options: { fontSize: 10, color: C.textDark, breakLine: true } },
    { text: "Real GRN: ~20-50 regulators per gene", options: { fontSize: 10, color: C.textDark, breakLine: true } },
    { text: "99%+ values are noise!", options: { fontSize: 11, color: C.coral, bold: true, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Evidence: latent loss ~ 1.12", options: { fontSize: 10, color: C.textMid, breakLine: true } },
    { text: "          >> expr loss ~ 0.019", options: { fontSize: 10, color: C.textMid } },
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  // Solution 1 (top-right)
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    { text: "Solution: Sparse Top-K", options: { bold: true, fontSize: 12, color: C.accent3, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Per gene: keep only K=30 largest |delta|", options: { fontSize: 10, color: C.textDark, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "delta_attn (GxG)    25M values", options: { fontSize: 9.5, fontFace: "Consolas", color: C.coral, breakLine: true } },
    { text: "  -> top-K sparsification", options: { fontSize: 9.5, fontFace: "Consolas", color: C.textMid, breakLine: true } },
    { text: "sparse_delta (Gx30) filter 99.4%", options: { fontSize: 9.5, fontFace: "Consolas", color: C.accent3, breakLine: true } },
    { text: "  -> x gene_emb = (G,512)", options: { fontSize: 9.5, fontFace: "Consolas", color: C.accent3 } },
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  // Challenge 2 (bottom-left)
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  s.addText([
    { text: "Challenge 2: 512-d Latent Too Hard", options: { bold: true, fontSize: 12, color: C.coral, breakLine: true } },
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    { text: "(G,512) = 2.5M-dim velocity field per step", options: { fontSize: 10, color: C.textDark, breakLine: true } },
    { text: "Ablation: dim 512->1: loss 1.1 -> 0.5-0.7", options: { fontSize: 10, fontFace: "Consolas", color: C.textDark } },
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  s.addText([
    { text: "Solution: PCA Reduction", options: { bold: true, fontSize: 12, color: C.accent3, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "sparse_delta x pca_basis -> (G, 64)", options: { fontSize: 10, fontFace: "Consolas", color: C.textDark, breakLine: true } },
    { text: "Keep principal directions, 8x reduction", options: { fontSize: 10, color: C.textMid } },
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}

// ============================================================
// SLIDE 15: Section — Summary
// ============================================================
slideNum++;
addDividerSlide("6. Summary & Future Work", "Validating the biological hypothesis", slideNum);

// ============================================================
// SLIDE 16: Summary + Future Experiment
// ============================================================
slideNum++;
{
  const s = addContentSlide("Summary & Key Future Experiment", slideNum);

  // Core contribution
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  s.addText([
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    { text: "Not architectural improvement -- biological mechanism-driven modeling", options: { color: C.white, fontSize: 12, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 3 } },
    { text: "Existing:  Pert -> [black box] -> Expr        ", options: { fontSize: 10, fontFace: "Consolas", color: C.midGray } },
    { text: "Ours:  Pert -> GRN rewiring -> Expr", options: { fontSize: 10, fontFace: "Consolas", color: C.subtitleOnDark } },
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  // Future experiment
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    { text: "Key Future Experiment: Does inference order matter?", options: { bold: true, fontSize: 14, color: C.teal, breakLine: true } },
    { text: "", options: { breakLine: true, fontSize: 4 } },
    { text: "Train with random t1, t2 (no cascade). Compare inference orders:", options: { fontSize: 11, color: C.textDark } },
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    { order: "Expr first -> GRN", meaning: "Predict first, understand after", expected: "Suboptimal", bg: "FEF3C7", color: C.accent2 },
    { order: "Simultaneous", meaning: "No explicit order", expected: "Worst", bg: "FEE2E2", color: C.coral },
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      fontSize: 11, fontFace: "Consolas", color: C.textDark, bold: true, valign: "middle", margin: 0,
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    s.addText(r.expected, {
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  // Hypothesis
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    { text: "Hypothesis:  ", options: { bold: true, color: C.gold, fontSize: 12 } },
    { text: "Understanding GRN changes is a prerequisite for expression prediction, not a byproduct.", options: { color: C.white, fontSize: 12 } },
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}

// ============================================================
// SLIDE 17: Closing
// ============================================================
slideNum++;
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  s.addText("Use scGPT attention-delta to explicitly extract perturbation-induced GRN changes, and through cascaded flow matching, force the model to \"first understand how GRN changes, then predict how expression changes\" -- embedding the biological prior that perturbation propagates through GRN into the generative model's inference process.", {
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  s.addText("Thank you!", {
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    fontSize: 16, fontFace: "Georgia", color: C.white, bold: true, margin: 0,
  });
  addSlideNum(s, slideNum);
}

// === Save ===
const outPath = "/home/hp250092/ku50001222/qian/aivc/lfj/Report/GRN_CCFM_group_meeting.pptx";
pres.writeFile({ fileName: outPath }).then(() => {
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}).catch(err => {
  console.error("Error:", err);
});