const pptxgen = require("pptxgenjs");
const pres = new pptxgen();
pres.layout = "LAYOUT_16x9"; // 10" x 5.625"
pres.author = "Qian";
pres.title = "GRN-Guided Cascaded Flow Matching for Single-Cell Perturbation Prediction";

// ─── COLOR PALETTE ───
const NAVY    = "1B2A4A";
const BLUE    = "2E6B9E";
const LT_BLUE = "B8D4E8";
const BG_GRAY = "F0F2F5";
const CARD_BG = "F7F8FA";
const TXT     = "2C3E50";
const TXT_MID = "4A5568";
const TXT_LT  = "6B7B8D";
const WHITE   = "FFFFFF";
const RED     = "C0392B";
const GREEN   = "27AE60";
const ORANGE  = "D35400";

const HF = "Cambria";
const BF = "Calibri";
const CF = "Consolas";

// ─── HELPERS ───
function slideNum(slide, n) {
  slide.addText(String(n), {
    x: 9.2, y: 5.2, w: 0.5, h: 0.3,
    fontSize: 10, color: TXT_LT, fontFace: BF, align: "right"
  });
}

function headerBar(slide, title) {
  slide.addShape(pres.shapes.RECTANGLE, {
    x: 0, y: 0, w: 10, h: 0.85,
    fill: { color: NAVY }
  });
  slide.addText(title, {
    x: 0.6, y: 0.12, w: 8.8, h: 0.6,
    fontSize: 24, fontFace: HF, color: WHITE, bold: true, margin: 0
  });
}

function sectionLabel(slide, text, x, y, w) {
  slide.addText(text, {
    x: x || 0.6, y: y || 1.1, w: w || 8.8, h: 0.4,
    fontSize: 17, fontFace: HF, color: NAVY, bold: true, margin: 0
  });
}

function card(slide, x, y, w, h, accentColor) {
  slide.addShape(pres.shapes.RECTANGLE, {
    x, y, w, h, fill: { color: CARD_BG }
  });
  if (accentColor) {
    slide.addShape(pres.shapes.RECTANGLE, {
      x, y, w: 0.06, h, fill: { color: accentColor }
    });
  }
}

// ════════════════════════════════════════════════════════════
// SLIDE 1: TITLE
// ════════════════════════════════════════════════════════════
let s1 = pres.addSlide();
s1.background = { color: NAVY };
s1.addShape(pres.shapes.RECTANGLE, {
  x: 0, y: 0, w: 10, h: 0.06, fill: { color: BLUE }
});
s1.addText([
  { text: "GRN-Guided Cascaded Flow Matching", options: { breakLine: true, fontSize: 34 } },
  { text: "for Single-Cell Perturbation Prediction", options: { fontSize: 28 } }
], {
  x: 0.8, y: 1.0, w: 8.4, h: 2.2,
  fontFace: HF, color: WHITE, bold: true,
  align: "center", valign: "middle", paraSpaceAfter: 8
});
s1.addShape(pres.shapes.RECTANGLE, {
  x: 3.8, y: 3.4, w: 2.4, h: 0.035, fill: { color: BLUE }
});
s1.addText("Group Meeting Report", {
  x: 1, y: 3.65, w: 8, h: 0.45,
  fontSize: 18, fontFace: BF, color: LT_BLUE, align: "center"
});
s1.addText("March 2026", {
  x: 1, y: 4.2, w: 8, h: 0.35,
  fontSize: 14, fontFace: BF, color: TXT_LT, align: "center"
});

// ════════════════════════════════════════════════════════════
// SLIDE 2: TASK DEFINITION
// ════════════════════════════════════════════════════════════
let s2 = pres.addSlide();
headerBar(s2, "Task: Single-Cell Perturbation Prediction");
slideNum(s2, 2);

// Left column
sectionLabel(s2, "Virtual Cell Vision", 0.6, 1.05);
s2.addText([
  { text: "AI model simulating cell behavior", options: { bullet: true, breakLine: true } },
  { text: "Predict molecular state under perturbation", options: { bullet: true, breakLine: true } },
  { text: "Focus: CRISPR genetic perturbation", options: { bullet: true } }
], {
  x: 0.6, y: 1.5, w: 4.2, h: 1.2,
  fontSize: 13, fontFace: BF, color: TXT, paraSpaceAfter: 4
});

sectionLabel(s2, "Perturbation Types", 0.6, 2.8);
s2.addText([
  { text: "Drug (small molecule compounds)", options: { bullet: true, breakLine: true } },
  { text: "Cytokine (immune signaling)", options: { bullet: true, breakLine: true } },
  { text: "Genetic (CRISPR KO / OE / KD)", options: { bullet: true, bold: true } }
], {
  x: 0.6, y: 3.25, w: 4.2, h: 1.2,
  fontSize: 13, fontFace: BF, color: TXT, paraSpaceAfter: 4
});

// Right column - task formulation card
card(s2, 5.3, 1.05, 4.2, 3.7, BLUE);
s2.addText("Task Formulation", {
  x: 5.6, y: 1.15, w: 3.7, h: 0.35,
  fontSize: 15, fontFace: HF, color: NAVY, bold: true, margin: 0
});
s2.addText([
  { text: "Input:", options: { bold: true, breakLine: true } },
  { text: "  x_ctrl (control expression, G dims)", options: { breakLine: true } },
  { text: "  p (perturbed gene ID)", options: { breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Output:", options: { bold: true, breakLine: true } },
  { text: "  x_pert (perturbed expression, G dims)", options: { breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "G = 5,000 highly variable genes", options: { italic: true, color: TXT_MID } }
], {
  x: 5.6, y: 1.6, w: 3.7, h: 2.8,
  fontSize: 12, fontFace: CF, color: TXT
});

// ════════════════════════════════════════════════════════════
// SLIDE 3: SIGNIFICANCE & DATASET
// ════════════════════════════════════════════════════════════
let s3 = pres.addSlide();
headerBar(s3, "Significance & Dataset");
slideNum(s3, 3);

// Three importance cards
const importCards = [
  { num: "$$$", title: "Drug Screening", body: "Wet-lab Perturb-seq is expensive;\nvirtual screening saves resources" },
  { num: "N\u00B2", title: "Combinatorial Explosion", body: "N genes \u2192 N(N-1)/2 combinations;\nimpossible to enumerate all" },
  { num: "DNA", title: "Disease Mechanism", body: "Predict which gene perturbation\ncauses disease phenotype" }
];
importCards.forEach((c, i) => {
  const cx = 0.6 + i * 3.1;
  card(s3, cx, 1.05, 2.8, 2.0, BLUE);
  s3.addText(c.num, {
    x: cx + 0.15, y: 1.15, w: 1.0, h: 0.45,
    fontSize: 20, fontFace: HF, color: BLUE, bold: true, margin: 0
  });
  s3.addText(c.title, {
    x: cx + 0.15, y: 1.6, w: 2.5, h: 0.3,
    fontSize: 14, fontFace: HF, color: NAVY, bold: true, margin: 0
  });
  s3.addText(c.body, {
    x: cx + 0.15, y: 1.95, w: 2.5, h: 0.9,
    fontSize: 11, fontFace: BF, color: TXT_MID
  });
});

// Dataset info
sectionLabel(s3, "Dataset: Norman et al.", 0.6, 3.3);
s3.addText([
  { text: "~9,000 cells \u00D7 5,000 HVG", options: { bullet: true, breakLine: true, bold: true } },
  { text: "105 single/double CRISPR perturbations (KO + OE)", options: { bullet: true, breakLine: true } },
  { text: "No cell-level pairing (destructive measurement)", options: { bullet: true, breakLine: true, bold: true, color: RED } },
  { text: "Metrics: DE gene overlap, direction, MSE, Pearson r", options: { bullet: true } }
], {
  x: 0.6, y: 3.75, w: 8.8, h: 1.3,
  fontSize: 13, fontFace: BF, color: TXT, paraSpaceAfter: 4
});

// ════════════════════════════════════════════════════════════
// SLIDE 4: EXISTING METHODS
// ════════════════════════════════════════════════════════════
let s4 = pres.addSlide();
headerBar(s4, "Existing Methods & Limitations");
slideNum(s4, 4);

const hdrOpts = (txt) => ({ text: txt, options: { bold: true, color: WHITE, fill: { color: NAVY }, fontSize: 12, fontFace: BF, align: "center" } });
const cellOpts = (txt, opts) => ({ text: txt, options: { fontSize: 11, fontFace: BF, ...opts } });
const altBg = { fill: { color: "F8F9FA" } };

s4.addTable([
  [ hdrOpts("Method"), hdrOpts("Type"), hdrOpts("Key Limitation") ],
  [ cellOpts("Additive Shift", altBg), cellOpts("Baseline", { ...altBg, align: "center" }), cellOpts("Ignores cell heterogeneity; constant shift assumption", altBg) ],
  [ cellOpts("scGPT"), cellOpts("Pretrained LM", { align: "center" }), cellOpts("Autoregressive completion; not designed for perturbation") ],
  [ cellOpts("Geneformer", altBg), cellOpts("Pretrained LM", { ...altBg, align: "center" }), cellOpts("Heuristic in-silico perturbation; loses expression info", altBg) ],
  [ cellOpts("CPA"), cellOpts("Specialized", { align: "center" }), cellOpts("Linear additivity assumption in latent space") ],
  [ cellOpts("GEARS", altBg), cellOpts("Specialized", { ...altBg, align: "center" }), cellOpts("Static GO graph prior; deterministic prediction only", altBg) ],
  [ cellOpts("scDFM", { bold: true }), cellOpts("Flow Matching", { align: "center" }), cellOpts("No GRN modeling; limited model capacity (d=128)") ]
], {
  x: 0.6, y: 1.1, w: 8.8,
  colW: [1.8, 1.5, 5.5],
  border: { pt: 0.5, color: "DDE1E6" },
  rowH: [0.45, 0.42, 0.42, 0.42, 0.42, 0.42, 0.42]
});

s4.addText("scDFM (ICLR 2026) is closest to our work \u2014 we build upon its flow matching framework.", {
  x: 0.6, y: 4.5, w: 8.8, h: 0.3,
  fontSize: 11, fontFace: BF, color: TXT_MID, italic: true
});

// ════════════════════════════════════════════════════════════
// SLIDE 5: THE MISSING PIECE
// ════════════════════════════════════════════════════════════
let s5 = pres.addSlide();
headerBar(s5, "The Common Blind Spot");
slideNum(s5, 5);

sectionLabel(s5, "All existing methods share the same gap:");

// Existing approach block
card(s5, 0.6, 1.8, 8.8, 1.3, RED);
s5.addText("Existing Approach", {
  x: 0.85, y: 1.9, w: 3.0, h: 0.3,
  fontSize: 14, fontFace: HF, color: RED, bold: true, margin: 0
});
s5.addText("Perturbation   \u2192   [ Black-Box Model ]   \u2192   Expression Change", {
  x: 0.85, y: 2.3, w: 8.2, h: 0.4,
  fontSize: 16, fontFace: CF, color: TXT, margin: 0
});
s5.addText("No explicit modeling of gene regulatory network changes", {
  x: 0.85, y: 2.7, w: 8.0, h: 0.3,
  fontSize: 12, fontFace: BF, color: TXT_MID, italic: true, margin: 0
});

// Our approach block
card(s5, 0.6, 3.5, 8.8, 1.3, GREEN);
s5.addText("Our Approach", {
  x: 0.85, y: 3.6, w: 3.0, h: 0.3,
  fontSize: 14, fontFace: HF, color: GREEN, bold: true, margin: 0
});
s5.addText("Perturbation   \u2192   GRN Rewiring   \u2192   Expression Change", {
  x: 0.85, y: 4.0, w: 8.2, h: 0.4,
  fontSize: 16, fontFace: CF, color: TXT, margin: 0
});
s5.addText("Explicitly model how perturbation alters the gene regulatory network", {
  x: 0.85, y: 4.4, w: 8.0, h: 0.3,
  fontSize: 12, fontFace: BF, color: TXT_MID, italic: true, margin: 0
});

// ════════════════════════════════════════════════════════════
// SLIDE 6: THREE KEY MOTIVATIONS
// ════════════════════════════════════════════════════════════
let s6 = pres.addSlide();
headerBar(s6, "Three Key Motivations");
slideNum(s6, 6);

const motivations = [
  {
    num: "1", title: "Flow Matching for Unpaired Data",
    bullets: [
      "Learns probability transport: p(ctrl) \u2192 p(pert)",
      "No cell-level pairing required",
      "Generative output with uncertainty estimation"
    ]
  },
  {
    num: "2", title: "GRN Cascade Drives Expression Change",
    bullets: [
      "KO gene A \u2192 direct targets B,C,D change",
      "Cascade propagates through regulatory network",
      "Understanding GRN change = better prediction"
    ]
  },
  {
    num: "3", title: "scGPT Attention \u2248 Data-Driven GRN",
    bullets: [
      "Pretrained attention encodes gene-gene regulation",
      "Context-dependent: varies with cell state",
      "\u0394_attn captures GRN change from perturbation"
    ]
  }
];

motivations.forEach((m, i) => {
  const cy = 1.05 + i * 1.4;
  card(s6, 0.6, cy, 8.8, 1.2, BLUE);
  s6.addShape(pres.shapes.OVAL, {
    x: 0.85, y: cy + 0.15, w: 0.5, h: 0.5,
    fill: { color: NAVY }
  });
  s6.addText(m.num, {
    x: 0.85, y: cy + 0.15, w: 0.5, h: 0.5,
    fontSize: 18, fontFace: HF, color: WHITE, bold: true,
    align: "center", valign: "middle", margin: 0
  });
  s6.addText(m.title, {
    x: 1.55, y: cy + 0.1, w: 7.5, h: 0.35,
    fontSize: 15, fontFace: HF, color: NAVY, bold: true, margin: 0
  });
  s6.addText(m.bullets.map((b, bi) => ({
    text: b,
    options: { bullet: true, breakLine: bi < m.bullets.length - 1 }
  })), {
    x: 1.55, y: cy + 0.5, w: 7.5, h: 0.65,
    fontSize: 12, fontFace: BF, color: TXT_MID, paraSpaceAfter: 2
  });
});

// ════════════════════════════════════════════════════════════
// SLIDE 7: METHOD OVERVIEW
// ════════════════════════════════════════════════════════════
let s7 = pres.addSlide();
headerBar(s7, "Method: Cascaded Flow Matching");
slideNum(s7, 7);

sectionLabel(s7, "Two-Stage Generation: \"Think First, Then Predict\"");

// Stage 1 box
card(s7, 0.6, 1.7, 4.1, 2.8, ORANGE);
s7.addText("Stage 1: GRN Latent Flow", {
  x: 0.85, y: 1.8, w: 3.6, h: 0.35,
  fontSize: 15, fontFace: HF, color: ORANGE, bold: true, margin: 0
});
s7.addText([
  { text: "noise \u2192 GRN change features", options: { breakLine: true, bold: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Understand how perturbation", options: { breakLine: true } },
  { text: "rewires the regulatory network", options: { breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Conditioned on:", options: { bold: true, breakLine: true } },
  { text: "  \u2022 control expression", options: { breakLine: true } },
  { text: "  \u2022 perturbed gene ID", options: {} }
], {
  x: 0.85, y: 2.25, w: 3.6, h: 2.0,
  fontSize: 12, fontFace: BF, color: TXT
});

// Arrow
s7.addText("\u2192", {
  x: 4.7, y: 2.7, w: 0.6, h: 0.5,
  fontSize: 30, fontFace: BF, color: NAVY, align: "center", valign: "middle", bold: true
});

// Stage 2 box
card(s7, 5.3, 1.7, 4.1, 2.8, GREEN);
s7.addText("Stage 2: Expression Flow", {
  x: 5.55, y: 1.8, w: 3.6, h: 0.35,
  fontSize: 15, fontFace: HF, color: GREEN, bold: true, margin: 0
});
s7.addText([
  { text: "noise \u2192 gene expression", options: { breakLine: true, bold: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Predict expression changes", options: { breakLine: true } },
  { text: "based on GRN understanding", options: { breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Conditioned on:", options: { bold: true, breakLine: true } },
  { text: "  \u2022 Stage 1 GRN features", options: { breakLine: true } },
  { text: "  \u2022 control expression + pert ID", options: {} }
], {
  x: 5.55, y: 2.25, w: 3.6, h: 2.0,
  fontSize: 12, fontFace: BF, color: TXT
});

// Bottom insight bar
s7.addShape(pres.shapes.RECTANGLE, {
  x: 0.6, y: 4.7, w: 8.8, h: 0.5,
  fill: { color: LT_BLUE }
});
s7.addText("Biological intuition: first understand GRN rewiring, then predict expression change", {
  x: 0.8, y: 4.7, w: 8.4, h: 0.5,
  fontSize: 13, fontFace: BF, color: NAVY, italic: true, valign: "middle"
});

// ════════════════════════════════════════════════════════════
// SLIDE 8: GRN FEATURE EXTRACTION
// ════════════════════════════════════════════════════════════
let s8 = pres.addSlide();
headerBar(s8, "GRN Feature: Attention-Delta Extraction");
slideNum(s8, 8);

sectionLabel(s8, "Using Frozen scGPT to Extract GRN Change Signal");

// Steps
const steps = [
  "Feed control & perturbed expression\ninto frozen scGPT separately",
  "Extract attention matrices:\nAttn(ctrl) and Attn(pert)",
  "Compute delta:\n\u0394_attn = Attn(pert) \u2212 Attn(ctrl)",
  "Project to features:\nz = \u0394_attn \u00D7 gene_embeddings"
];
steps.forEach((desc, i) => {
  const sy = 1.65 + i * 0.9;
  s8.addShape(pres.shapes.OVAL, {
    x: 0.7, y: sy + 0.05, w: 0.45, h: 0.45,
    fill: { color: BLUE }
  });
  s8.addText(String(i + 1), {
    x: 0.7, y: sy + 0.05, w: 0.45, h: 0.45,
    fontSize: 16, fontFace: HF, color: WHITE, bold: true,
    align: "center", valign: "middle", margin: 0
  });
  s8.addText(desc, {
    x: 1.4, y: sy, w: 3.8, h: 0.6,
    fontSize: 12, fontFace: BF, color: TXT, valign: "middle", margin: 0
  });
  if (i < steps.length - 1) {
    s8.addShape(pres.shapes.LINE, {
      x: 0.92, y: sy + 0.52, w: 0, h: 0.35,
      line: { color: BLUE, width: 1.5, dashType: "dash" }
    });
  }
});

// Output card on right
card(s8, 5.6, 1.65, 3.8, 3.2, NAVY);
s8.addText("Output", {
  x: 5.85, y: 1.75, w: 3.3, h: 0.3,
  fontSize: 15, fontFace: HF, color: NAVY, bold: true, margin: 0
});
s8.addText([
  { text: "Per-gene GRN change vector", options: { breakLine: true, bold: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Shape: (B, G, 512)", options: { breakLine: true, fontFace: CF } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Each gene gets a 512-d vector\nencoding: \"how did upstream\nregulatory relationships change\nfor this gene?\"", options: { color: TXT_MID } }
], {
  x: 5.85, y: 2.15, w: 3.3, h: 2.2,
  fontSize: 12, fontFace: BF, color: TXT
});

// ════════════════════════════════════════════════════════════
// SLIDE 9: MODEL ARCHITECTURE
// ════════════════════════════════════════════════════════════
let s9 = pres.addSlide();
headerBar(s9, "Model Architecture");
slideNum(s9, 9);

// Expression Stream
card(s9, 0.6, 1.1, 4.1, 1.1, BLUE);
s9.addText("Expression Stream", {
  x: 0.85, y: 1.15, w: 3.6, h: 0.3,
  fontSize: 14, fontFace: HF, color: BLUE, bold: true, margin: 0
});
s9.addText("GeneEncoder + ValueEnc \u2192 expr_tokens (B,G,d)", {
  x: 0.85, y: 1.5, w: 3.6, h: 0.4,
  fontSize: 11, fontFace: CF, color: TXT, margin: 0
});

// Latent Stream
card(s9, 5.3, 1.1, 4.1, 1.1, ORANGE);
s9.addText("Latent Stream", {
  x: 5.55, y: 1.15, w: 3.6, h: 0.3,
  fontSize: 14, fontFace: HF, color: ORANGE, bold: true, margin: 0
});
s9.addText("LatentEmbedder \u2192 lat_tokens (B,G,d)", {
  x: 5.55, y: 1.5, w: 3.6, h: 0.4,
  fontSize: 11, fontFace: CF, color: TXT, margin: 0
});

// Merge
s9.addText("\u2295  Additive Fusion", {
  x: 3.0, y: 2.35, w: 4.0, h: 0.35,
  fontSize: 13, fontFace: BF, color: NAVY, bold: true, align: "center", margin: 0
});

// Down arrows
s9.addShape(pres.shapes.LINE, {
  x: 2.5, y: 2.2, w: 0, h: 0.15,
  line: { color: NAVY, width: 1.5 }
});
s9.addShape(pres.shapes.LINE, {
  x: 7.5, y: 2.2, w: 0, h: 0.15,
  line: { color: NAVY, width: 1.5 }
});

// Conditioning
card(s9, 2.0, 2.85, 6.0, 0.5, NAVY);
s9.addText("Conditioning:  c = t_expr + t_latent + pert_embedding", {
  x: 2.25, y: 2.9, w: 5.5, h: 0.4,
  fontSize: 11, fontFace: CF, color: TXT, valign: "middle", margin: 0
});

// Down arrow to backbone
s9.addShape(pres.shapes.LINE, {
  x: 5.0, y: 3.35, w: 0, h: 0.2,
  line: { color: NAVY, width: 1.5 }
});

// Shared Backbone
s9.addShape(pres.shapes.RECTANGLE, {
  x: 2.0, y: 3.6, w: 6.0, h: 0.65,
  fill: { color: NAVY }
});
s9.addText("Shared Backbone:  DiffPerceiverBlock \u00D7 4  (with Gene-AdaLN)", {
  x: 2.0, y: 3.6, w: 6.0, h: 0.65,
  fontSize: 13, fontFace: BF, color: WHITE, bold: true,
  align: "center", valign: "middle"
});

// Down arrows to heads
s9.addShape(pres.shapes.LINE, {
  x: 3.4, y: 4.25, w: 0, h: 0.2,
  line: { color: NAVY, width: 1.5 }
});
s9.addShape(pres.shapes.LINE, {
  x: 6.6, y: 4.25, w: 0, h: 0.2,
  line: { color: NAVY, width: 1.5 }
});

// Dual heads
card(s9, 2.0, 4.5, 2.8, 0.65, BLUE);
s9.addText("Expression Head \u2192 v_expr (B,G)", {
  x: 2.2, y: 4.55, w: 2.4, h: 0.45,
  fontSize: 11, fontFace: CF, color: TXT, valign: "middle", margin: 0
});
card(s9, 5.2, 4.5, 2.8, 0.65, ORANGE);
s9.addText("Latent Head \u2192 v_latent (B,G,512)", {
  x: 5.4, y: 4.55, w: 2.4, h: 0.45,
  fontSize: 11, fontFace: CF, color: TXT, valign: "middle", margin: 0
});

// ════════════════════════════════════════════════════════════
// SLIDE 10: TRAINING & INFERENCE
// ════════════════════════════════════════════════════════════
let s10 = pres.addSlide();
headerBar(s10, "Cascaded Training & Inference");
slideNum(s10, 10);

// Left: Training
sectionLabel(s10, "Training: Probabilistic Switching", 0.6, 1.1, 4.2);
card(s10, 0.6, 1.55, 4.2, 1.4, BLUE);
s10.addText([
  { text: "40%", options: { bold: true, fontSize: 20, color: ORANGE } },
  { text: "  Train Latent Flow only", options: { fontSize: 13 } }
], {
  x: 0.85, y: 1.65, w: 3.7, h: 0.45, fontFace: BF, color: TXT, valign: "middle", margin: 0
});
s10.addText("t\u2082 random, t\u2081 = 0, only loss_latent", {
  x: 0.85, y: 2.05, w: 3.7, h: 0.25,
  fontSize: 10, fontFace: CF, color: TXT_MID, margin: 0
});
s10.addText([
  { text: "60%", options: { bold: true, fontSize: 20, color: GREEN } },
  { text: "  Train Expression Flow only", options: { fontSize: 13 } }
], {
  x: 0.85, y: 2.4, w: 3.7, h: 0.45, fontFace: BF, color: TXT, valign: "middle", margin: 0
});
s10.addText("t\u2081 random, t\u2082 \u2248 1, only loss_expr", {
  x: 0.85, y: 2.7, w: 3.7, h: 0.25,
  fontSize: 10, fontFace: CF, color: TXT_MID, margin: 0
});

// Right: Inference
sectionLabel(s10, "Inference: Sequential Two-Stage", 5.3, 1.1, 4.2);
card(s10, 5.3, 1.55, 4.2, 1.4, NAVY);
s10.addText([
  { text: "Stage 1:", options: { bold: true, color: ORANGE, breakLine: true } },
  { text: "z_noise \u2550\u2550(ODE)\u2550\u2550> z_clean  (t\u2082: 0\u21921)", options: { fontFace: CF, fontSize: 11, breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 4 } },
  { text: "Stage 2:", options: { bold: true, color: GREEN, breakLine: true } },
  { text: "x_noise \u2550\u2550(ODE)\u2550\u2550> x_pred   (t\u2081: 0\u21921)", options: { fontFace: CF, fontSize: 11 } }
], {
  x: 5.55, y: 1.65, w: 3.7, h: 1.2,
  fontSize: 12, fontFace: BF, color: TXT, margin: 0
});

// Biological analogy
sectionLabel(s10, "Biological Cascade Analogy", 0.6, 3.2, 8.8);
s10.addShape(pres.shapes.RECTANGLE, {
  x: 0.6, y: 3.6, w: 8.8, h: 1.6,
  fill: { color: LT_BLUE }
});
s10.addText([
  { text: "CRISPR knock-out gene A", options: { bold: true, breakLine: true } },
  { text: "  \u2193  Gene A expression \u2192 0", options: { breakLine: true } },
  { text: "  \u2193  Direct targets B, C, D change (1st-order)", options: { breakLine: true } },
  { text: "  \u2193  B\u2019s targets E, F and C\u2019s targets G, H change (cascade)", options: { breakLine: true } },
  { text: "  \u2193  Thousands of genes altered across the transcriptome", options: {} }
], {
  x: 0.8, y: 3.65, w: 8.4, h: 1.5,
  fontSize: 12, fontFace: CF, color: TXT
});

// ════════════════════════════════════════════════════════════
// SLIDE 11: CHALLENGE 1 - NOISY GRN SIGNAL
// ════════════════════════════════════════════════════════════
let s11 = pres.addSlide();
headerBar(s11, "Challenge 1: Noisy GRN Signal");
slideNum(s11, 11);

// Problem
sectionLabel(s11, "Problem: Noise Drowns True Signal", 0.6, 1.1, 4.2);
card(s11, 0.6, 1.55, 4.2, 1.8, RED);
s11.addText([
  { text: "Attention matrix: 5000\u00D75000", options: { bold: true, breakLine: true } },
  { text: "= 25,000,000 non-zero values", options: { breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Real GRN: ~20\u201350 targets per gene", options: { breakLine: true } },
  { text: "\u2192 99%+ attention values are noise", options: { bold: true, color: RED } }
], {
  x: 0.85, y: 1.65, w: 3.7, h: 1.4,
  fontSize: 12, fontFace: BF, color: TXT
});
s11.addText("Evidence: latent loss \u2248 1.12 >> expr loss \u2248 0.019", {
  x: 0.6, y: 3.5, w: 4.2, h: 0.25,
  fontSize: 11, fontFace: BF, color: TXT_MID, italic: true
});

// Solution
sectionLabel(s11, "Solution: Sparse Top-K Filtering", 5.3, 1.1, 4.2);
card(s11, 5.3, 1.55, 4.2, 1.8, GREEN);
s11.addText([
  { text: "Keep only top K=30 per gene", options: { bold: true, breakLine: true } },
  { text: "(ranked by |\u0394_attn| magnitude)", options: { breakLine: true, color: TXT_MID } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "\u2192 Filters 99.4% noise", options: { bold: true, color: GREEN, breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "features = sparse_\u0394_topk \u00D7 gene_emb", options: { fontFace: CF, fontSize: 11 } }
], {
  x: 5.55, y: 1.65, w: 3.7, h: 1.4,
  fontSize: 12, fontFace: BF, color: TXT
});
s11.addText("Status: implemented (sparse_topk_emb mode)", {
  x: 5.3, y: 3.5, w: 4.2, h: 0.25,
  fontSize: 11, fontFace: BF, color: GREEN, italic: true
});

// Before/after comparison bar
s11.addShape(pres.shapes.RECTANGLE, {
  x: 0.6, y: 4.0, w: 8.8, h: 1.2,
  fill: { color: BG_GRAY }
});
s11.addText([
  { text: "Before: ", options: { bold: true } },
  { text: "\u0394_attn (G\u00D7G) \u2192 25M values \u2192 noise dominates \u2192 loss ~1.12", options: { color: RED } }
], {
  x: 0.8, y: 4.1, w: 8.4, h: 0.35,
  fontSize: 12, fontFace: BF, color: TXT
});
s11.addText([
  { text: "After:  ", options: { bold: true } },
  { text: "sparse_\u0394_topk (G\u00D7K) \u2192 150K values \u2192 signal preserved \u2192 loss expected \u2193", options: { color: GREEN } }
], {
  x: 0.8, y: 4.55, w: 8.4, h: 0.35,
  fontSize: 12, fontFace: BF, color: TXT
});

// ════════════════════════════════════════════════════════════
// SLIDE 12: CHALLENGE 2 - HIGH-DIM LATENT
// ════════════════════════════════════════════════════════════
let s12 = pres.addSlide();
headerBar(s12, "Challenge 2: High-Dimensional Latent");
slideNum(s12, 12);

// Problem
sectionLabel(s12, "Problem: High-Dim Latent Prediction", 0.6, 1.1, 4.2);
card(s12, 0.6, 1.55, 4.2, 1.8, RED);
s12.addText([
  { text: "Each gene: 512-d GRN feature vector", options: { breakLine: true, bold: true } },
  { text: "Total: G\u00D7512 = 2.5M-dim velocity field", options: { breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "Ablation experiment:", options: { bold: true, breakLine: true } },
  { text: "512-d \u2192 1-d: loss drops 1.1 \u2192 0.5", options: { bold: true, color: RED } }
], {
  x: 0.85, y: 1.65, w: 3.7, h: 1.5,
  fontSize: 12, fontFace: BF, color: TXT
});
s12.addText("Dimensionality is a major difficulty source", {
  x: 0.6, y: 3.5, w: 4.2, h: 0.25,
  fontSize: 11, fontFace: BF, color: TXT_MID, italic: true
});

// Solution
sectionLabel(s12, "Solution: PCA Compression", 5.3, 1.1, 4.2);
card(s12, 5.3, 1.55, 4.2, 1.8, GREEN);
s12.addText([
  { text: "PCA on gene embeddings:", options: { bold: true, breakLine: true } },
  { text: "512-d \u2192 64-d principal components", options: { breakLine: true } },
  { text: "", options: { breakLine: true, fontSize: 6 } },
  { text: "features = sparse_\u0394 \u00D7 pca_basis", options: { fontFace: CF, fontSize: 11, breakLine: true } },
  { text: "Output: (B, G, 64)", options: { fontFace: CF, fontSize: 11 } }
], {
  x: 5.55, y: 1.65, w: 3.7, h: 1.5,
  fontSize: 12, fontFace: BF, color: TXT
});
s12.addText("Status: implemented (sparse_pca mode)", {
  x: 5.3, y: 3.5, w: 4.2, h: 0.25,
  fontSize: 11, fontFace: BF, color: GREEN, italic: true
});

// Combined pipeline
s12.addShape(pres.shapes.RECTANGLE, {
  x: 0.6, y: 4.0, w: 8.8, h: 1.2,
  fill: { color: LT_BLUE }
});
s12.addText("Combined Pipeline", {
  x: 0.8, y: 4.05, w: 8.4, h: 0.3,
  fontSize: 14, fontFace: HF, color: NAVY, bold: true, margin: 0
});
s12.addText("\u0394_attn  \u2192  Sparse Top-K (noise filter)  \u2192  PCA 512\u219264 (dim reduction)  \u2192  GRN features (B, G, 64)", {
  x: 0.8, y: 4.45, w: 8.4, h: 0.5,
  fontSize: 13, fontFace: CF, color: TXT, valign: "middle"
});

// ════════════════════════════════════════════════════════════
// SLIDE 13: SUMMARY & FUTURE WORK
// ════════════════════════════════════════════════════════════
let s13 = pres.addSlide();
headerBar(s13, "Summary & Future Work");
slideNum(s13, 13);

// Core Contribution
sectionLabel(s13, "Core Contribution", 0.6, 1.1);
s13.addText([
  { text: "First explicit GRN modeling in perturbation prediction", options: { bullet: true, breakLine: true, bold: true } },
  { text: "Cascaded flow matching: GRN first, expression second", options: { bullet: true, breakLine: true } },
  { text: "Biologically grounded: perturbation cascades through GRN", options: { bullet: true } }
], {
  x: 0.6, y: 1.5, w: 8.8, h: 1.0,
  fontSize: 13, fontFace: BF, color: TXT, paraSpaceAfter: 4
});

// Future experiment
sectionLabel(s13, "Key Future Experiment: Validate Causal Hypothesis", 0.6, 2.7);

const fHdr = (t) => ({ text: t, options: { bold: true, color: WHITE, fill: { color: NAVY }, fontSize: 11, fontFace: BF, align: "center" } });
const fCell = (t, opts) => ({ text: t, options: { fontSize: 11, fontFace: BF, ...opts } });
const fAlt = { fill: { color: "F8F9FA" } };

s13.addTable([
  [ fHdr("Inference Order"), fHdr("Meaning"), fHdr("Expected") ],
  [ fCell("GRN \u2192 Expression", { ...fAlt, bold: true }), fCell("Understand first, then predict", fAlt), fCell("Best", { ...fAlt, bold: true, color: GREEN, align: "center" }) ],
  [ fCell("Expression \u2192 GRN"), fCell("Predict first, understand later"), fCell("Suboptimal", { color: ORANGE, align: "center" }) ],
  [ fCell("Simultaneous", fAlt), fCell("No explicit order", fAlt), fCell("Worst", { ...fAlt, color: RED, align: "center" }) ]
], {
  x: 0.6, y: 3.15, w: 8.8,
  colW: [2.5, 3.8, 2.5],
  border: { pt: 0.5, color: "DDE1E6" },
  rowH: [0.4, 0.4, 0.4, 0.4]
});

s13.addText("If \"GRN \u2192 Expression\" wins: GRN understanding is a prerequisite, not a byproduct.", {
  x: 0.6, y: 4.8, w: 8.8, h: 0.4,
  fontSize: 12, fontFace: BF, color: NAVY, bold: true, italic: true
});

// ════════════════════════════════════════════════════════════
// SLIDE 14: TAKE-HOME MESSAGE
// ════════════════════════════════════════════════════════════
let s14 = pres.addSlide();
s14.background = { color: NAVY };
s14.addShape(pres.shapes.RECTANGLE, {
  x: 0, y: 0, w: 10, h: 0.06, fill: { color: BLUE }
});
s14.addText("Take-Home Message", {
  x: 1, y: 1.0, w: 8, h: 0.6,
  fontSize: 24, fontFace: HF, color: LT_BLUE, align: "center"
});
s14.addText([
  { text: "We embed biological prior \u2014 perturbation cascades through GRN \u2014", options: { breakLine: true } },
  { text: "into generative modeling via cascaded flow matching,", options: { breakLine: true } },
  { text: "forcing the model to ", options: {} },
  { text: "\"understand regulatory rewiring", options: { bold: true } },
  { text: "", options: { breakLine: true } },
  { text: "before predicting expression changes.\"", options: { bold: true } }
], {
  x: 1.0, y: 2.0, w: 8.0, h: 2.0,
  fontSize: 18, fontFace: BF, color: WHITE, align: "center", valign: "middle",
  paraSpaceAfter: 6
});
s14.addShape(pres.shapes.RECTANGLE, {
  x: 3.8, y: 4.3, w: 2.4, h: 0.035, fill: { color: BLUE }
});
s14.addText("Thank You", {
  x: 1, y: 4.5, w: 8, h: 0.5,
  fontSize: 20, fontFace: HF, color: TXT_LT, align: "center"
});

// ─── WRITE ───
pres.writeFile({ fileName: "/home/hp250092/ku50001222/qian/aivc/lfj/Report/PPT2/GRN_CCFM_presentation.pptx" })
  .then(() => console.log("SUCCESS: Presentation saved."))
  .catch(err => console.error("ERROR:", err));