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<!DOCTYPE html>
<html lang="zh-TW">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>scRNA-seq 細胞類型註解互動指南</title>
<!-- Tailwind CSS -->
<script src="https://cdn.tailwindcss.com"></script>
<!-- Chart.js -->
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<!-- Tailwind Config for Custom Palette -->
<script>
tailwind.config = {
theme: {
extend: {
colors: {
stone: {
50: '#fafaf9',
100: '#f5f5f4',
200: '#e7e5e4',
800: '#292524',
900: '#1c1917',
},
teal: {
600: '#0d9488',
700: '#0f766e',
},
indigo: {
600: '#4f46e5',
}
},
fontFamily: {
sans: ['Inter', 'system-ui', 'sans-serif'],
}
}
}
}
</script>
<style>
/* Embedded CSS for strict chart container constraints & custom scrollbar */
.chart-container {
position: relative;
width: 100%;
max-width: 600px;
margin-left: auto;
margin-right: auto;
height: 350px;
max-height: 400px;
}
@media (max-width: 640px) {
.chart-container {
height: 280px;
}
}
.tab-active {
border-bottom: 2px solid #0d9488;
color: #0d9488;
font-weight: 600;
}
.glass-panel {
background: rgba(255, 255, 255, 0.9);
backdrop-filter: blur(10px);
border: 1px solid rgba(231, 229, 228, 0.5);
}
.fade-in {
animation: fadeIn 0.4s ease-in-out;
}
@keyframes fadeIn {
from { opacity: 0; transform: translateY(10px); }
to { opacity: 1; transform: translateY(0); }
}
</style>
</head>
<body class="bg-stone-50 text-stone-800 antialiased min-h-screen">
<!-- Chosen Palette: Warm Neutrals (Stone) with Teal and Indigo accents -->
<!-- Application Structure Plan: The SPA uses a tabbed dashboard architecture to break down the dense technical guide into explorable modules. 1. '核心概念' (Core Concepts) establishes the 'Why' and 'How' with an interactive workflow. 2. '方法論解析' (Methodology) compares the three main annotation strategies using an interactive Radar chart to highlight trade-offs. 3. '生態系與趨勢' (Ecosystem & Trends) organizes tools and AI advancements in a clear grid. 4. '實戰挑戰' (Challenges) uses an accordion layout for problem-solution mapping. This non-linear structure allows users to jump directly to their area of interest (e.g., tools vs. theory) while maintaining context. -->
<!-- Visualization & Content Choices: 1. Goal: Inform about the 'Why' -> Viz: Doughnut Chart (Chart.js) -> Interaction: Hover tooltips -> Justification: Shows the proportional importance of biological, functional, and QC reasons. 2. Goal: Explain Workflow -> Viz: Interactive HTML/Tailwind step boxes -> Interaction: Click to reveal details -> Justification: Breaks down linear processes better than static text. 3. Goal: Compare Methods -> Viz: Radar Chart (Chart.js) -> Interaction: Hover to see exact scores across 5 dimensions -> Justification: Perfect for multivariate comparison of pros/cons (Speed, Accuracy, etc.). 4. Goal: Organize Challenges -> Viz: Interactive Accordion list -> Interaction: Click to expand -> Justification: Keeps the UI clean while hiding dense troubleshooting text until needed. Library: Chart.js for canvas charts, Vanilla JS for interactivity. -->
<!-- CONFIRMATION: NO SVG graphics used. NO Mermaid JS used. -->
<!-- Header Section -->
<header class="bg-white shadow-sm sticky top-0 z-50">
<div class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8">
<div class="flex flex-col md:flex-row justify-between items-center py-4">
<div class="flex items-center space-x-3 mb-4 md:mb-0">
<span class="text-3xl">🧬</span>
<h1 class="text-2xl font-bold text-stone-900 tracking-tight">scRNA-seq 細胞註解互動指南</h1>
</div>
<p class="text-sm text-stone-500 max-w-md text-center md:text-right">將運算數據轉化為生物學意義的核心步驟。探索 2025 最新分析流程與 AI 趨勢。</p>
</div>
<!-- Navigation Tabs -->
<nav class="flex overflow-x-auto space-x-6 border-b border-stone-200" id="main-nav">
<button onclick="switchTab('tab-core')" class="tab-btn tab-active pb-3 text-sm font-medium transition-colors hover:text-teal-600 whitespace-nowrap" data-target="tab-core">
🧠 核心概念與流程
</button>
<button onclick="switchTab('tab-methods')" class="tab-btn pb-3 text-sm font-medium text-stone-500 transition-colors hover:text-teal-600 whitespace-nowrap" data-target="tab-methods">
📊 主流方法比較
</button>
<button onclick="switchTab('tab-tools')" class="tab-btn pb-3 text-sm font-medium text-stone-500 transition-colors hover:text-teal-600 whitespace-nowrap" data-target="tab-tools">
💻 資源與 AI 趨勢
</button>
<button onclick="switchTab('tab-challenges')" class="tab-btn pb-3 text-sm font-medium text-stone-500 transition-colors hover:text-teal-600 whitespace-nowrap" data-target="tab-challenges">
⚠️ 實戰挑戰與解法
</button>
</nav>
</div>
</header>
<!-- Main Content Area -->
<main class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8 py-8">
<!-- TAB 1: Core Concepts & Workflow -->
<section id="tab-core" class="tab-content fade-in block">
<div class="mb-8">
<h2 class="text-xl font-bold text-stone-900 mb-2">為什麼需要細胞類型註解？</h2>
<p class="text-stone-600 leading-relaxed">
在單細胞轉錄組分析中，電腦僅能依據基因表達相似度將細胞分群（Clusters），但無法得知其生物學身份。本區塊將幫助您理解註解的三大核心目的，並透過互動式流程圖掌握標準的分析步驟。
</p>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 gap-8 items-start">
<!-- Doughnut Chart for 'Why' -->
<div class="glass-panel p-6 rounded-2xl shadow-sm">
<h3 class="text-lg font-semibold text-stone-800 mb-4 text-center">註解的核心驅動力</h3>
<div class="chart-container">
<canvas id="whyChart"></canvas>
</div>
<div class="mt-6 space-y-3">
<div class="flex items-start">
<span class="text-teal-600 mr-2 mt-0.5">🔹</span>
<p class="text-sm text-stone-700"><strong>生物學解釋：</strong> 確定樣本細胞組成比例，還原組織真實面貌。</p>
</div>
<div class="flex items-start">
<span class="text-indigo-600 mr-2 mt-0.5">🔹</span>
<p class="text-sm text-stone-700"><strong>功能挖掘：</strong> 尋找疾病或特定處理下，發生變化的特定細胞群。</p>
</div>
<div class="flex items-start">
<span class="text-stone-400 mr-2 mt-0.5">🔹</span>
<p class="text-sm text-stone-700"><strong>質量控制：</strong> 剔除雙細胞 (Doublets) 或低質量污染數據。</p>
</div>
</div>
</div>
<!-- Interactive Workflow -->
<div class="glass-panel p-6 rounded-2xl shadow-sm">
<h3 class="text-lg font-semibold text-stone-800 mb-4">標準註解四步曲 (點擊展開)</h3>
<div class="space-y-4 relative before:absolute before:inset-0 before:ml-5 before:-translate-x-px md:before:mx-auto md:before:translate-x-0 before:h-full before:w-0.5 before:bg-gradient-to-b before:from-transparent before:via-stone-300 before:to-transparent">
<!-- Step 1 -->
<div class="relative flex items-center justify-between md:justify-normal md:odd:flex-row-reverse group cursor-pointer" onclick="toggleStep('step1')">
<div class="flex items-center justify-center w-10 h-10 rounded-full border-4 border-white bg-teal-100 text-teal-600 font-bold shadow shrink-0 md:order-1 md:group-odd:-translate-x-1/2 md:group-even:translate-x-1/2 z-10 transition-transform group-hover:scale-110">1</div>
<div class="w-[calc(100%-4rem)] md:w-[calc(50%-2.5rem)] bg-white p-4 rounded-xl border border-stone-200 shadow-sm transition-all hover:shadow-md">
<h4 class="font-bold text-stone-800">降維與聚類</h4>
<p id="step1-desc" class="text-sm text-stone-500 mt-2 hidden fade-in">使用 PCA、UMAP 或 t-SNE 降低數據維度，並透過 Louvain 或 Leiden 算法將相似細胞分群。</p>
</div>
</div>
<!-- Step 2 -->
<div class="relative flex items-center justify-between md:justify-normal md:odd:flex-row-reverse group cursor-pointer" onclick="toggleStep('step2')">
<div class="flex items-center justify-center w-10 h-10 rounded-full border-4 border-white bg-teal-100 text-teal-600 font-bold shadow shrink-0 md:order-1 md:group-odd:-translate-x-1/2 md:group-even:translate-x-1/2 z-10 transition-transform group-hover:scale-110">2</div>
<div class="w-[calc(100%-4rem)] md:w-[calc(50%-2.5rem)] bg-white p-4 rounded-xl border border-stone-200 shadow-sm transition-all hover:shadow-md">
<h4 class="font-bold text-stone-800">尋找標誌基因 (DEGs)</h4>
<p id="step2-desc" class="text-sm text-stone-500 mt-2 hidden fade-in">對每個 Cluster 進行差異分析，找出表達量顯著升高的 Marker Genes。</p>
</div>
</div>
<!-- Step 3 -->
<div class="relative flex items-center justify-between md:justify-normal md:odd:flex-row-reverse group cursor-pointer" onclick="toggleStep('step3')">
<div class="flex items-center justify-center w-10 h-10 rounded-full border-4 border-white bg-teal-100 text-teal-600 font-bold shadow shrink-0 md:order-1 md:group-odd:-translate-x-1/2 md:group-even:translate-x-1/2 z-10 transition-transform group-hover:scale-110">3</div>
<div class="w-[calc(100%-4rem)] md:w-[calc(50%-2.5rem)] bg-white p-4 rounded-xl border border-stone-200 shadow-sm transition-all hover:shadow-md">
<h4 class="font-bold text-stone-800">身份分配</h4>
<p id="step3-desc" class="text-sm text-stone-500 mt-2 hidden fade-in">根據標誌基因，對照文獻或權威數據庫，為每個 Cluster 命名（如：T 細胞）。</p>
</div>
</div>
<!-- Step 4 -->
<div class="relative flex items-center justify-between md:justify-normal md:odd:flex-row-reverse group cursor-pointer" onclick="toggleStep('step4')">
<div class="flex items-center justify-center w-10 h-10 rounded-full border-4 border-white bg-teal-100 text-teal-600 font-bold shadow shrink-0 md:order-1 md:group-odd:-translate-x-1/2 md:group-even:translate-x-1/2 z-10 transition-transform group-hover:scale-110">4</div>
<div class="w-[calc(100%-4rem)] md:w-[calc(50%-2.5rem)] bg-white p-4 rounded-xl border border-stone-200 shadow-sm transition-all hover:shadow-md">
<h4 class="font-bold text-stone-800">可視化驗證</h4>
<p id="step4-desc" class="text-sm text-stone-500 mt-2 hidden fade-in">透過小提琴圖 (Violin Plot)、點圖 (Dot Plot) 等檢查 Marker 基因在群體間的特異性。</p>
</div>
</div>
</div>
<div class="mt-6 bg-teal-50 border-l-4 border-teal-500 p-4 rounded text-sm text-teal-900">
💡 <strong>專家建議：</strong> 大多數研究最穩健的策略是「自動預測 + 人工校對」。
</div>
</div>
</div>
</section>
<!-- TAB 2: Methodology Comparison -->
<section id="tab-methods" class="tab-content hidden">
<div class="mb-8">
<h2 class="text-xl font-bold text-stone-900 mb-2">三大主流註解方法解析</h2>
<p class="text-stone-600 leading-relaxed">
選擇正確的註解方法取決於您的數據特點與研究目的。此區塊透過多維度雷達圖，直觀比較「人工註解」、「基於參考數據」與「基於標誌基因」三種策略的優劣勢。
</p>
</div>
<div class="grid grid-cols-1 lg:grid-cols-2 gap-8 items-center">
<!-- Radar Chart -->
<div class="glass-panel p-6 rounded-2xl shadow-sm flex flex-col items-center">
<h3 class="text-lg font-semibold text-stone-800 mb-4 w-full text-center">方法優勢多維度比較</h3>
<div class="chart-container w-full">
<canvas id="methodsRadarChart"></canvas>
</div>
<p class="text-xs text-stone-400 mt-4 text-center">分數越高代表在該維度表現越佳（例如：自動化程度越高，分數越高）。</p>
</div>
<!-- Method Details -->
<div class="space-y-4">
<!-- Method A -->
<div class="bg-white p-5 rounded-xl border border-stone-200 shadow-sm hover:border-teal-300 transition-colors">
<div class="flex items-center justify-between mb-2">
<h4 class="text-lg font-bold text-teal-700">A. 人工註解 (Manual)</h4>
<span class="px-2 py-1 bg-yellow-100 text-yellow-800 text-xs font-bold rounded">黃金標準</span>
</div>
<p class="text-sm text-stone-600 mb-3">分析者手動查看高表達基因 (P值與 logFC)，並與文獻比對。</p>
<ul class="text-sm text-stone-500 list-disc pl-5 space-y-1">
<li><strong>優點：</strong> 準確度極高，能發現稀有亞型 (Subtypes) 或全新細胞。</li>
<li><strong>缺點：</strong> 極度耗時，易受主觀經驗影響。</li>
</ul>
</div>
<!-- Method B -->
<div class="bg-white p-5 rounded-xl border border-stone-200 shadow-sm hover:border-indigo-300 transition-colors">
<div class="flex items-center justify-between mb-2">
<h4 class="text-lg font-bold text-indigo-700">B. 基於參考數據 (Reference-based)</h4>
<span class="px-2 py-1 bg-indigo-50 text-indigo-800 text-xs font-bold rounded">快速映射</span>
</div>
<p class="text-sm text-stone-600 mb-3">將數據與已標註圖譜 (如 Human Cell Atlas) 計算相關性。</p>
<ul class="text-sm text-stone-500 list-disc pl-5 space-y-1">
<li><strong>優點：</strong> 快速、可重複性強。</li>
<li><strong>缺點：</strong> 依賴參考集質量，無法識別參考集中不存在的新細胞。</li>
<li><strong>代表工具：</strong> SingleR, Azimuth, Scmap</li>
</ul>
</div>
<!-- Method C -->
<div class="bg-white p-5 rounded-xl border border-stone-200 shadow-sm hover:border-stone-400 transition-colors">
<div class="flex items-center justify-between mb-2">
<h4 class="text-lg font-bold text-stone-700">C. 基於標誌基因組 (Marker-based)</h4>
<span class="px-2 py-1 bg-stone-100 text-stone-800 text-xs font-bold rounded">富集評分</span>
</div>
<p class="text-sm text-stone-600 mb-3">提供預定義細胞標誌清單，算法評估 Cluster 富集程度。</p>
<ul class="text-sm text-stone-500 list-disc pl-5 space-y-1">
<li><strong>優點：</strong> 不需完整轉錄組參考，只需精確的 Marker 列表。</li>
<li><strong>代表工具：</strong> SCINA, CellAssign, AUCell</li>
</ul>
</div>
</div>
</div>
</section>
<!-- TAB 3: Tools & Trends -->
<section id="tab-tools" class="tab-content hidden">
<div class="mb-8">
<h2 class="text-xl font-bold text-stone-900 mb-2">資源庫與 2025 AI 新趨勢</h2>
<p class="text-stone-600 leading-relaxed">
強大的數據庫是準確註解的基石，而人工智慧模型正徹底改變我們處理單細胞數據的方式。探索必備辭典與前沿技術。
</p>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 gap-8">
<!-- Databases -->
<div>
<h3 class="text-lg font-bold text-stone-800 mb-4 flex items-center">
<span class="text-2xl mr-2">📚</span> 必備資料庫辭典
</h3>
<div class="space-y-4">
<div class="bg-white p-4 rounded-lg border-l-4 border-teal-500 shadow-sm">
<h4 class="font-bold text-stone-800">CellMarker 2.0</h4>
<p class="text-sm text-stone-600 mt-1">目前最全面的細胞標誌基因數據庫，涵蓋大量組織與疾病狀態，是人工註解的首選查詢工具。</p>
</div>
<div class="bg-white p-4 rounded-lg border-l-4 border-teal-400 shadow-sm">
<h4 class="font-bold text-stone-800">PanglaoDB</h4>
<p class="text-sm text-stone-600 mt-1">深度整合了人類與小鼠單細胞數據的標誌基因，特別適合跨物種對比分析。</p>
</div>
<div class="bg-white p-4 rounded-lg border-l-4 border-teal-300 shadow-sm">
<h4 class="font-bold text-stone-800">CZ CELLxGENE</h4>
<p class="text-sm text-stone-600 mt-1">2025 年主流的圖譜瀏覽器，擁有海量標準化數據，非常適合用於結果的交叉驗證。</p>
</div>
</div>
</div>
<!-- AI Trends -->
<div>
<h3 class="text-lg font-bold text-stone-800 mb-4 flex items-center">
<span class="text-2xl mr-2">🤖</span> LLM 與基礎模型發展
</h3>
<div class="bg-gradient-to-br from-indigo-900 to-indigo-700 p-6 rounded-2xl text-white shadow-lg h-full">
<p class="text-indigo-100 mb-6 text-sm">
大型語言模型 (LLM) 與預訓練基礎模型正在取代傳統的純統計學分析，提供更具生物學直覺的註解結果。
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<h4 class="font-bold text-teal-300 flex items-center">
<span class="mr-2">💬</span> GPT-based Annotation
</h4>
<p class="text-sm text-indigo-50 mt-2">
工具如 <code>mLLMCelltype</code> 透過精心設計的 Prompt，將 Top DEGs 餵給 GPT-4 等模型，讓 AI 總結並推理細胞身份，極大化減少文獻檢索時間。
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<span class="mr-2">🧠</span> Foundation Models (基礎模型)
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如 <strong>scGPT</strong> 或 <strong>Geneformer</strong>。這些模型在千萬級單細胞數據上進行預訓練，理解基因間的語境關係，能「直接預測」細胞類別及狀態。
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<h2 class="text-xl font-bold text-stone-900 mb-2">實戰挑戰與解決策略</h2>
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真實世界的數據充滿雜訊。以下收錄了 scRNA-seq 註解過程中最常見的四大難題，點擊即可查看對應的排雷策略。
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<span class="font-bold text-stone-800">批次效應 (Batch Effect)</span>
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<p class="text-sm text-stone-600 mt-3 mb-2"><strong>情境：</strong> 不同實驗室、不同天做的實驗數據混合時，相同細胞因技術誤差被分到不同 Cluster。</p>
<p class="text-sm text-teal-700 bg-teal-50 p-2 rounded"><strong>✅ 解決建議：</strong> 在聚類前，使用 <code>Harmony</code> 或 <code>Seurat v5</code> 的整合功能 (Integration) 消除技術誤差。</p>
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<span class="text-red-500 font-bold mr-3 text-lg">⚠️</span>
<span class="font-bold text-stone-800">細胞狀態 vs 類型界限模糊</span>
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<p class="text-sm text-teal-700 bg-teal-50 p-2 rounded"><strong>✅ 解決建議：</strong> 單純看 Marker 不夠，應結合基因集富集分析 (GSEA) 或路徑分析來區分功能狀態。</p>
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<!-- Challenge 3 -->
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<span class="font-bold text-stone-800">雙細胞 (Doublets) 污染</span>
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<p class="text-sm text-stone-600 mt-3 mb-2"><strong>情境：</strong> 機器將兩個黏在一起的不同細胞（如 B細胞與 T細胞）當成一個，導致該 Cluster 同時表達兩類 Marker。</p>
<p class="text-sm text-teal-700 bg-teal-50 p-2 rounded"><strong>✅ 解決建議：</strong> 在分析極早期階段，使用 <code>Scrublet</code> 或 <code>DoubletFinder</code> 工具進行預先過濾剔除。</p>
</div>
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<!-- Challenge 4 -->
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<span class="font-bold text-stone-800">解析度 (Resolution) 難以抉擇</span>
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<p class="text-sm text-stone-600 mt-3 mb-2"><strong>情境：</strong> 聚類參數太粗看不到亞型，太細又會導致細胞群體過度碎片化。</p>
<p class="text-sm text-teal-700 bg-teal-50 p-2 rounded"><strong>✅ 解決建議：</strong> 使用 clustree 工具繪製不同 Resolution 下的聚類樹，觀察分群的穩定性；結合生物學背景知識選擇最合理的層級。</p>
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