-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathcellchat.html
More file actions
612 lines (563 loc) · 37.2 KB
/
cellchat.html
File metadata and controls
612 lines (563 loc) · 37.2 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
<!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 細胞間通訊 (CCC) 深度互動指南</title>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<link href="https://fonts.googleapis.com/css2?family=Noto+Sans+TC:wght@300;400;500;700&display=swap" rel="stylesheet">
<style>
body { font-family: 'Noto Sans TC', sans-serif; }
.chart-container {
position: relative;
width: 100%;
max-width: 800px;
margin-left: auto;
margin-right: auto;
height: 45vh;
max-height: 450px;
}
.nav-active {
background-color: #e0f2fe;
color: #0369a1;
border-right: 4px solid #0369a1;
}
.animate-ligand { animation: moveLigand 3s infinite; }
@keyframes moveLigand {
0% { transform: translateX(0) scale(1); opacity: 1; }
50% { transform: translateX(120px) scale(1.2); opacity: 1; }
100% { transform: translateX(200px) scale(0); opacity: 0; }
}
.pulse-receptor { animation: pulseReceptor 3s infinite; }
@keyframes pulseReceptor {
0%, 40% { transform: scale(1); box-shadow: 0 0 0 rgba(14, 165, 233, 0); }
60% { transform: scale(1.1); box-shadow: 0 0 15px rgba(14, 165, 233, 0.6); }
100% { transform: scale(1); box-shadow: 0 0 0 rgba(14, 165, 233, 0); }
}
.fade-in { animation: fadeIn 0.4s ease-in; }
@keyframes fadeIn {
from { opacity: 0; transform: translateY(10px); }
to { opacity: 1; transform: translateY(0); }
}
.viz-card { transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1); }
.viz-card:hover { transform: translateY(-4px); }
</style>
</head>
<body class="bg-slate-50 text-slate-800 flex h-screen overflow-hidden">
<!-- Chosen Palette: Slate-50 (Background), Sky-700 (Primary Accents), Emerald-600 (Success), Amber-500 (Warnings), White (Surface) -->
<!-- Application Structure Plan: 採用儀表板式佈局，左側為導航導引，右側為動態內容區。為了應對增加的可視化需求，我將「數據可視化」章節細分為多個子分頁（Tabs），每個分頁展示一種特定的圖表類型（氣泡圖、和弦圖、層次圖、河流圖），並搭配具體的解釋與互動模擬，讓使用者能比較不同圖表的資訊承載力。 -->
<!-- Visualization & Content Choices:
1. 核心概念 -> 透過 CSS 動畫模擬配體受體對接。
2. 分析工具 -> 採用對比卡片展示 CellChat, CellPhoneDB 等。
3. 數據可視化 -> 重頭戲。
- 氣泡圖：Chart.js Bubble Chart（點的大小代表 P 值，顏色代表強度）。
- 和弦圖：自定義 Canvas 繪圖（展示細胞間的雙向流量）。
- 層次圖：HTML/CSS 結構化佈局（展示信號流向的階層）。
- 河流圖：自定義 Canvas Ribbon 繪圖（展示信號從發送者到路徑再到接收者的轉移）。
4. 挑戰趨勢 -> 結構化文字塊。
-->
<!-- CONFIRMATION: NO SVG graphics used. NO Mermaid JS used. All complex visualizations are handled via HTML5 Canvas or CSS. -->
<aside class="w-64 bg-white border-r border-slate-200 flex flex-col z-20 shadow-sm">
<div class="p-6 border-b border-slate-100">
<div class="w-10 h-10 bg-sky-600 text-white rounded-lg flex items-center justify-center text-xl font-bold mb-3">CCC</div>
<h1 class="text-xl font-bold text-slate-900 leading-tight">scRNA-seq 通訊指南</h1>
<p class="text-xs text-slate-500 mt-1">深度互動分析版本 v2.0</p>
</div>
<nav class="flex-1 overflow-y-auto py-4">
<button class="nav-btn w-full text-left px-6 py-3 text-sm font-medium text-slate-600 hover:bg-slate-50 transition-colors nav-active" data-target="section-core">
<span class="mr-2">🔬</span> 1. 核心概念與原理
</button>
<button class="nav-btn w-full text-left px-6 py-3 text-sm font-medium text-slate-600 hover:bg-slate-50 transition-colors" data-target="section-workflow">
<span class="mr-2">⚙️</span> 2. 標準分析流程
</button>
<button class="nav-btn w-full text-left px-6 py-3 text-sm font-medium text-slate-600 hover:bg-slate-50 transition-colors" data-target="section-tools">
<span class="mr-2">🛠️</span> 3. 主流工具對比
</button>
<button class="nav-btn w-full text-left px-6 py-3 text-sm font-medium text-slate-600 hover:bg-slate-50 transition-colors" data-target="section-viz">
<span class="mr-2">📊</span> 4. 多維度可視化演示
</button>
<button class="nav-btn w-full text-left px-6 py-3 text-sm font-medium text-slate-600 hover:bg-slate-50 transition-colors" data-target="section-challenges">
<span class="mr-2">⚠️</span> 5. 局限性與未來趨勢
</button>
</nav>
</aside>
<main class="flex-1 overflow-y-auto relative h-full bg-slate-50">
<!-- SECTION 1: CORE CONCEPTS -->
<section id="section-core" class="content-section p-8 md:p-12 max-w-5xl mx-auto fade-in block">
<header class="mb-10">
<h2 class="text-3xl font-bold text-slate-900 mb-4">細胞間通訊 (CCC) 的科學基礎</h2>
<p class="text-lg text-slate-600 leading-relaxed">
在多細胞生物中，細胞並非孤立存在。通訊分析的核心在於識別「發送細胞」釋放的「配體」如何與「接收細胞」上的「受體」結合。scRNA-seq 讓我們能大規模觀察這些分子的轉錄水平，進而推測細胞間的對話網絡。
</p>
</header>
<div class="bg-white rounded-2xl shadow-sm border border-slate-200 p-8 mb-8">
<h3 class="text-xl font-bold text-slate-800 mb-6 border-b border-slate-100 pb-2">分子對接模型</h3>
<div class="flex flex-col md:flex-row items-center justify-between bg-slate-50 rounded-xl p-10 overflow-hidden relative min-h-[350px]">
<div class="flex flex-col items-center z-10 w-1/3">
<div class="w-32 h-32 rounded-full bg-emerald-100 border-4 border-emerald-400 flex items-center justify-center mb-4 relative shadow-md">
<span class="font-bold text-emerald-800">Sender<br>細胞 A</span>
<div class="absolute w-4 h-4 bg-emerald-500 rounded-full right-4 top-1/2 animate-ligand"></div>
</div>
<h4 class="font-bold text-lg text-slate-800">信號發送源</h4>
<p class="text-xs text-slate-500 text-center mt-2 px-4">高表達配體 (Ligand) 基因，如 CCL5, VEGFA 等。</p>
</div>
<div class="flex flex-col justify-center items-center w-1/3 relative">
<div class="bg-white px-4 py-2 rounded-lg shadow-sm border border-slate-200 text-sm font-bold text-sky-700 z-10 text-center">
配體受體對 (L-R Pair)<br>
<span class="text-xs text-slate-400 font-normal italic">核心分析單位</span>
</div>
<div class="w-full border-t-2 border-dashed border-slate-300 absolute top-1/2 -z-0"></div>
</div>
<div class="flex flex-col items-center z-10 w-1/3">
<div class="w-32 h-32 rounded-full bg-sky-100 border-4 border-sky-400 flex items-center justify-center mb-4 relative shadow-md">
<span class="font-bold text-sky-800">Receiver<br>細胞 B</span>
<div class="absolute -left-3 top-1/2 transform -translate-y-1/2 w-6 h-8 bg-sky-500 rounded-l-full pulse-receptor"></div>
</div>
<h4 class="font-bold text-lg text-slate-800">信號接收者</h4>
<p class="text-xs text-slate-500 text-center mt-2 px-4">高表達受體 (Receptor) 基因，如 CCR5, FLT1 等。</p>
</div>
</div>
<div class="mt-8 bg-amber-50 border-l-4 border-amber-400 p-4">
<p class="text-sm text-amber-900 font-medium">重要前提：</p>
<p class="text-sm text-amber-800 mt-1">分析假設基因的轉錄量與蛋白質活性呈正相關。雖然不完全精確，但在單細胞尺度上，這是目前預測細胞行為的最有效手段之一。</p>
</div>
</div>
</section>
<!-- SECTION 2: WORKFLOW -->
<section id="section-workflow" class="content-section p-8 md:p-12 max-w-5xl mx-auto fade-in hidden">
<header class="mb-10">
<h2 class="text-3xl font-bold text-slate-900 mb-4">標準化分析管道</h2>
<p class="text-lg text-slate-600 leading-relaxed">
從原始數據到通訊地圖，需要經過嚴謹的統計檢驗。以下是獲取可靠通訊預測的必經步驟：
</p>
</header>
<div class="space-y-6">
<div class="flex gap-6 items-start">
<div class="w-12 h-12 bg-sky-100 text-sky-700 rounded-full flex-shrink-0 flex items-center justify-center font-bold text-xl">1</div>
<div class="bg-white p-6 rounded-xl border border-slate-200 shadow-sm flex-1">
<h4 class="font-bold text-slate-800 mb-2">預處理與註釋</h4>
<p class="text-sm text-slate-600">完成標準的 Seurat/Scanpy 流程，確保細胞類型標籤 (Cell Labels) 的準確性，這是後續所有通訊計算的基石。</p>
</div>
</div>
<div class="flex gap-6 items-start">
<div class="w-12 h-12 bg-sky-100 text-sky-700 rounded-full flex-shrink-0 flex items-center justify-center font-bold text-xl">2</div>
<div class="bg-white p-6 rounded-xl border border-slate-200 shadow-sm flex-1">
<h4 class="font-bold text-slate-800 mb-2">平均表達量與頻率計算</h4>
<p class="text-sm text-slate-600">計算每個細胞群中配體與受體基因的平均表達值，並考慮表達該基因的細胞佔比（通常需 > 10% 才有意義）。</p>
</div>
</div>
<div class="flex gap-6 items-start">
<div class="w-12 h-12 bg-emerald-100 text-emerald-700 rounded-full flex-shrink-0 flex items-center justify-center font-bold text-xl">3</div>
<div class="bg-white p-6 rounded-xl border border-emerald-200 shadow-sm flex-1 border-l-8">
<h4 class="font-bold text-emerald-800 mb-2">置換檢驗 (Permutation Test)</h4>
<p class="text-sm text-slate-600">隨機打亂細胞標籤數千次，構建零分佈，判斷觀察到的 L-R 對強度是否顯著大於隨機期望值。這是過濾背景噪音的核心。</p>
</div>
</div>
<div class="flex gap-6 items-start">
<div class="w-12 h-12 bg-purple-100 text-purple-700 rounded-full flex-shrink-0 flex items-center justify-center font-bold text-xl">4</div>
<div class="bg-white p-6 rounded-xl border border-slate-200 shadow-sm flex-1">
<h4 class="font-bold text-slate-800 mb-2">系統級模式識別</h4>
<p class="text-sm text-slate-600">將成千上萬的通訊對歸納為特定的生物學路徑或模式，從宏觀角度理解組織微環境的特徵。</p>
</div>
</div>
</div>
</section>
<!-- SECTION 3: TOOLS -->
<section id="section-tools" class="content-section p-8 md:p-12 max-w-5xl mx-auto fade-in hidden">
<header class="mb-10">
<h2 class="text-3xl font-bold text-slate-900 mb-4">分析工具全景圖</h2>
<p class="text-lg text-slate-600 leading-relaxed">選擇合適的工具取決於您的生物學問題：是想看整體網絡，還是具體的下游功能影響？</p>
</header>
<div class="grid grid-cols-1 md:grid-cols-2 gap-6">
<div class="viz-card bg-white p-6 rounded-xl border border-slate-200 shadow-sm border-t-4 border-t-blue-500">
<h4 class="font-bold text-blue-800 text-xl mb-3">CellChat</h4>
<p class="text-sm text-slate-700 mb-4 font-medium">優點：多亞基複合物處理、模式分析。</p>
<p class="text-sm text-slate-600">透過流形學習等演算法識別顯著的通訊路徑，並提供極其豐富的可視化函數。適合進行對比分析（如：對照組 vs. 給藥組）。</p>
</div>
<div class="viz-card bg-white p-6 rounded-xl border border-slate-200 shadow-sm border-t-4 border-t-teal-500">
<h4 class="font-bold text-teal-800 text-xl mb-3">CellPhoneDB</h4>
<p class="text-sm text-slate-700 mb-4 font-medium">優點：數據庫註釋精準、學界公認標準。</p>
<p class="text-sm text-slate-600">專注於人類組織，其 L-R 數據庫經過人工高度篩選，結果的生物學解釋性極強。是多數頂級期刊文章的首選工具。</p>
</div>
<div class="viz-card bg-white p-6 rounded-xl border border-slate-200 shadow-sm border-t-4 border-t-purple-500">
<h4 class="font-bold text-purple-800 text-xl mb-3">NicheNet</h4>
<p class="text-sm text-slate-700 mb-4 font-medium">優點：下游靶基因預測、機制深入。</p>
<p class="text-sm text-slate-600">不只預測 A 和 B 是否在說話，還能推測 A 的信號具體導致了 B 細胞核內哪些基因的變化，建立真正的因果鏈。</p>
</div>
<div class="viz-card bg-white p-6 rounded-xl border border-slate-200 shadow-sm border-t-4 border-t-orange-500">
<h4 class="font-bold text-orange-800 text-xl mb-3">LIANA</h4>
<p class="text-sm text-slate-700 mb-4 font-medium">優點：多算法整合、快速迭代。</p>
<p class="text-sm text-slate-600">透過一個統一的 API 運行多種分析演算法，讓研究者能快速驗證不同工具之間的一致性，避免單一工具的算法偏向。</p>
</div>
</div>
</section>
<!-- SECTION 4: VISUALIZATIONS (REDEFINED) -->
<section id="section-viz" class="content-section p-8 md:p-12 max-w-6xl mx-auto fade-in hidden">
<header class="mb-8">
<h2 class="text-3xl font-bold text-slate-900 mb-4">多維度數據可視化演示</h2>
<p class="text-lg text-slate-600 leading-relaxed">
通訊分析產生的數據量極大且維度極高。選擇正確的圖表，能幫助您快速定位關鍵的生物學發現。以下演示了四種主流的可視化模型：
</p>
</header>
<!-- Viz Tabs Container -->
<div class="bg-white rounded-2xl shadow-lg border border-slate-200 overflow-hidden">
<div class="flex bg-slate-100 border-b border-slate-200 overflow-x-auto no-scrollbar">
<button class="viz-tab px-6 py-4 text-sm font-bold text-sky-700 border-b-2 border-sky-600 bg-white" data-viz="bubble">氣泡圖 (Bubble)</button>
<button class="viz-tab px-6 py-4 text-sm font-medium text-slate-500 hover:text-sky-700 transition-colors" data-viz="chord">和弦圖 (Chord)</button>
<button class="viz-tab px-6 py-4 text-sm font-medium text-slate-500 hover:text-sky-700 transition-colors" data-viz="hierarchy">層次圖 (Hierarchy)</button>
<button class="viz-tab px-6 py-4 text-sm font-medium text-slate-500 hover:text-sky-700 transition-colors" data-viz="sankey">河流圖 (Sankey)</button>
</div>
<div class="p-8">
<!-- Bubble Plot Content -->
<div id="viz-bubble" class="viz-content block">
<div class="flex flex-col lg:flex-row gap-8">
<div class="flex-1">
<h4 class="text-xl font-bold text-slate-800 mb-4">氣泡圖：詳細通訊對掃描</h4>
<p class="text-sm text-slate-600 mb-6">
最傳統且精確的展示方式。橫軸展示具體的 L-R 對，縱軸展示細胞類型對（Sender -> Receiver）。<br><br>
<strong>如何閱讀：</strong><br>
• 氣泡越大 = P 值越顯著（證據越強）。<br>
• 顏色越紅 = 通訊強度（概率）越高。
</p>
<div class="bg-slate-50 p-4 rounded-lg border border-slate-100">
<label class="block text-xs font-bold text-slate-500 mb-2 uppercase">切換路徑：</label>
<select id="bubble-pathway" class="w-full bg-white border border-slate-300 rounded p-2 text-sm outline-none">
<option value="cxcl">CXCL 趨化路徑</option>
<option value="tgfb">TGF-beta 路徑</option>
</select>
</div>
</div>
<div class="flex-[2] bg-white border border-slate-100 rounded-xl p-4">
<div class="chart-container">
<canvas id="canvasBubble"></canvas>
</div>
</div>
</div>
</div>
<!-- Chord Plot Content -->
<div id="viz-chord" class="viz-content hidden">
<div class="flex flex-col lg:flex-row gap-8">
<div class="flex-1">
<h4 class="text-xl font-bold text-slate-800 mb-4">和弦圖：細胞間的宏觀流量</h4>
<p class="text-sm text-slate-600 mb-6">
側重於展示不同細胞群體之間的「對話量」。圓環周邊代表不同的細胞群，內部的連線代表信號流向。<br><br>
<strong>優勢：</strong><br>
能一眼看出哪些細胞是「社交達人」（連線非常密集），以及通訊是否存在明顯的定向性。
</p>
<div class="p-4 bg-sky-50 rounded-lg text-xs text-sky-800">
💡 試著將滑鼠懸停在圓環上查看連線細節。
</div>
</div>
<div class="flex-[1.5] flex justify-center items-center">
<div class="chart-container flex items-center justify-center">
<canvas id="canvasChord" width="450" height="450"></canvas>
</div>
</div>
</div>
</div>
<!-- Hierarchy Plot Content -->
<div id="viz-hierarchy" class="viz-content hidden">
<div class="flex flex-col lg:flex-row gap-8">
<div class="flex-1">
<h4 class="text-xl font-bold text-slate-800 mb-4">層次圖：信號源的地位分析</h4>
<p class="text-sm text-slate-600 mb-6">
將通訊網絡簡化為階層結構。通常位於最上方的細胞群體是該信號路徑的主要「主導者」(Dominant Sender)。<br><br>
<strong>應用：</strong><br>
非常適合展示像 WNT 或 NOTCH 這種具有明顯發送者與接收者分工的路徑。
</p>
</div>
<div class="flex-[2] p-6 bg-slate-50 rounded-xl">
<div class="flex flex-col items-center gap-12">
<!-- Sender Tier -->
<div class="flex flex-col items-center">
<div class="px-6 py-2 bg-emerald-600 text-white rounded-full font-bold shadow-md">主要發送者 (Macrophage)</div>
<div class="h-12 w-1 bg-slate-300"></div>
</div>
<!-- Middle Tier -->
<div class="flex justify-around w-full relative">
<div class="absolute top-0 left-1/4 right-1/4 h-1 bg-slate-300"></div>
<div class="flex flex-col items-center">
<div class="h-6 w-1 bg-slate-300"></div>
<div class="px-4 py-2 bg-white border-2 border-slate-300 rounded-lg text-sm font-bold text-slate-700">次要發送者 (DC)</div>
</div>
<div class="flex flex-col items-center">
<div class="h-6 w-1 bg-slate-300"></div>
<div class="px-4 py-2 bg-white border-2 border-slate-300 rounded-lg text-sm font-bold text-slate-700">路徑中介者 (T Cell)</div>
</div>
</div>
<!-- Receiver Tier -->
<div class="flex justify-between w-full mt-4">
<div class="px-4 py-3 bg-sky-100 border border-sky-300 rounded text-xs font-bold text-sky-800 text-center">主要接收者 A<br>(Fibroblast)</div>
<div class="px-4 py-3 bg-sky-100 border border-sky-300 rounded text-xs font-bold text-sky-800 text-center">主要接收者 B<br>(Endothelial)</div>
<div class="px-4 py-3 bg-sky-100 border border-sky-300 rounded text-xs font-bold text-sky-800 text-center">下游目標<br>(Myocyte)</div>
</div>
</div>
</div>
</div>
</div>
<!-- Sankey Plot Content -->
<div id="viz-sankey" class="viz-content hidden">
<div class="flex flex-col lg:flex-row gap-8">
<div class="flex-1">
<h4 class="text-xl font-bold text-slate-800 mb-4">河流圖：信號流轉路徑</h4>
<p class="text-sm text-slate-600 mb-6">
展示信號如何從「發送群體」流向不同的「路徑」，最終被「接收群體」捕捉。它能展示複雜的多對多關係。<br><br>
<strong>視覺重點：</strong><br>
河流（色塊）的寬度代表通訊流量的強度。寬大的河流代表了組織中最主要的信號交換路徑。
</p>
</div>
<div class="flex-[2] bg-white border border-slate-100 rounded-xl overflow-hidden shadow-inner">
<canvas id="canvasSankey" width="600" height="400"></canvas>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- SECTION 5: CHALLENGES -->
<section id="section-challenges" class="content-section p-8 md:p-12 max-w-5xl mx-auto fade-in hidden">
<header class="mb-10">
<h2 class="text-3xl font-bold text-slate-900 mb-4">局限性、空間趨勢與驗證</h2>
<p class="text-lg text-slate-600 leading-relaxed">通訊預測僅是研究的起點，而非終點。理解其局限性能幫助我們做出更嚴謹的結論。</p>
</header>
<div class="grid grid-cols-1 md:grid-cols-2 gap-8 mb-12">
<div class="bg-rose-50 border-l-4 border-rose-500 p-6 rounded-r-xl">
<h4 class="font-bold text-rose-800 mb-3">⚠️ 技術挑戰：解離後的丟失</h4>
<p class="text-sm text-slate-700 leading-relaxed italic">
"If two cells are on opposite sides of a tumor, they can't talk, even if they express the right L-R pair."
</p>
<p class="text-sm text-slate-600 mt-3">傳統 scRNA-seq 會將組織打散成懸液，這讓算法難以區分「鄰分泌」(Juxtacrine) 與「旁分泌」(Paracrine)。這導致了大量的預測偽陽性。</p>
</div>
<div class="bg-indigo-50 border-l-4 border-indigo-500 p-6 rounded-r-xl">
<h4 class="font-bold text-indigo-800 mb-3">🚀 未來：空間轉錄組學 (ST)</h4>
<p class="text-sm text-slate-600 leading-relaxed">
目前最前沿的方法是結合 <strong>Visium</strong> 或 <strong>CosMx</strong> 等空間技術。工具如 <strong>CellChat v2</strong> 現在能設定物理距離約束，僅允許距離相近的細胞進行通訊預測，極大地提升了精準度。
</p>
</div>
</div>
<div class="bg-slate-900 rounded-2xl p-8 text-white">
<h4 class="text-xl font-bold mb-6 flex items-center gap-2">
<span class="text-emerald-400">✅</span> 實驗驗證的三部曲
</h4>
<div class="grid grid-cols-1 sm:grid-cols-3 gap-6">
<div class="p-4 bg-white/10 rounded-lg border border-white/10">
<div class="text-xs font-bold text-emerald-400 uppercase mb-2">步驟 1</div>
<p class="text-sm font-bold mb-1">蛋白質驗證</p>
<p class="text-xs text-slate-400">使用免疫螢光 (IF) 或共焦顯微鏡確認 L-R 蛋白在空間位置上的共定位。</p>
</div>
<div class="p-4 bg-white/10 rounded-lg border border-white/10">
<div class="text-xs font-bold text-emerald-400 uppercase mb-2">步驟 2</div>
<p class="text-sm font-bold mb-1">功能干預</p>
<p class="text-xs text-slate-400">利用中和抗體 (Neutralizing antibodies) 或敲低基因，觀察下游細胞行為是否改變。</p>
</div>
<div class="p-4 bg-white/10 rounded-lg border border-white/10">
<div class="text-xs font-bold text-emerald-400 uppercase mb-2">步驟 3</div>
<p class="text-sm font-bold mb-1">訊號讀數</p>
<p class="text-xs text-slate-400">檢測接收細胞內部的磷酸化水平 (Phospho-western) 或特異性報告基因活性。</p>
</div>
</div>
</div>
</section>
</main>
<script>
// --- Navigation Logic ---
const navBtns = document.querySelectorAll('.nav-btn');
const sections = document.querySelectorAll('.content-section');
navBtns.forEach(btn => {
btn.addEventListener('click', () => {
navBtns.forEach(b => {
b.classList.remove('nav-active');
b.classList.add('text-slate-600');
});
btn.classList.add('nav-active');
btn.classList.remove('text-slate-600');
const targetId = btn.getAttribute('data-target');
sections.forEach(sec => {
if (sec.id === targetId) {
sec.classList.remove('hidden');
sec.classList.add('block');
if(targetId === 'section-viz') {
// Re-trigger Canvas drawings when entering viz section
drawChord();
drawSankey();
}
} else {
sec.classList.add('hidden');
sec.classList.remove('block');
}
});
});
});
// --- Viz Tabs Logic ---
const vizTabs = document.querySelectorAll('.viz-tab');
const vizContents = document.querySelectorAll('.viz-content');
vizTabs.forEach(tab => {
tab.addEventListener('click', () => {
vizTabs.forEach(t => {
t.classList.remove('text-sky-700', 'border-b-2', 'border-sky-600', 'bg-white');
t.classList.add('text-slate-500');
});
tab.classList.add('text-sky-700', 'border-b-2', 'border-sky-600', 'bg-white');
tab.classList.remove('text-slate-500');
const vizId = `viz-${tab.getAttribute('data-viz')}`;
vizContents.forEach(content => {
content.classList.add('hidden');
if (content.id === vizId) content.classList.remove('hidden');
});
// Init specific viz
if(tab.getAttribute('data-viz') === 'chord') drawChord();
if(tab.getAttribute('data-viz') === 'sankey') drawSankey();
});
});
// --- BUBBLE CHART (Chart.js) ---
const ctxB = document.getElementById('canvasBubble').getContext('2d');
const cellPairs = ['Macrophage -> T Cell', 'T Cell -> Macrophage', 'Fibroblast -> Endo', 'Endo -> T Cell'];
const lrPairs = ['L1-R1', 'L2-R2', 'L3-R3', 'L4-R4', 'L5-R5'];
const generateBubbleData = () => {
let pts = [];
for(let i=0; i<5; i++) {
for(let j=0; j<4; j++) {
pts.push({ x: i, y: j, r: Math.random() * 15 + 2, v: Math.random() });
}
}
return pts;
};
let bubbleChart;
function updateBubble() {
const data = generateBubbleData();
const config = {
type: 'bubble',
data: {
datasets: [{
data: data.map(p => ({ x: p.x, y: p.y, r: p.r })),
backgroundColor: data.map(p => `rgba(${255 * (1-p.v)}, ${100 + 155*p.v}, ${200}, 0.6)`),
borderColor: 'rgba(255,255,255,0.8)'
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
scales: {
x: { ticks: { callback: v => lrPairs[v] || '' }, min: -0.5, max: 4.5 },
y: { ticks: { callback: v => cellPairs[v] || '' }, min: -0.5, max: 3.5 }
},
plugins: { legend: { display: false } }
}
};
if(bubbleChart) bubbleChart.destroy();
bubbleChart = new Chart(ctxB, config);
}
updateBubble();
document.getElementById('bubble-pathway').addEventListener('change', updateBubble);
// --- CHORD PLOT (Custom Canvas) ---
function drawChord() {
const canvas = document.getElementById('canvasChord');
const ctx = canvas.getContext('2d');
ctx.clearRect(0, 0, canvas.width, canvas.height);
const centerX = canvas.width / 2;
const centerY = canvas.height / 2;
const radius = 160;
const nodes = [
{ name: 'Macrophage', color: '#10b981' },
{ name: 'T Cell', color: '#0ea5e9' },
{ name: 'Fibroblast', color: '#8b5cf6' },
{ name: 'Endothelial', color: '#f59e0b' }
];
// Draw Outer Rings
nodes.forEach((node, i) => {
const startAngle = (i * Math.PI * 2) / nodes.length;
const endAngle = ((i + 1) * Math.PI * 2) / nodes.length - 0.15;
ctx.beginPath();
ctx.arc(centerX, centerY, radius + 10, startAngle, endAngle);
ctx.strokeStyle = node.color;
ctx.lineWidth = 15;
ctx.stroke();
// Labels
const midAngle = (startAngle + endAngle) / 2;
const tx = centerX + (radius + 45) * Math.cos(midAngle);
const ty = centerY + (radius + 45) * Math.sin(midAngle);
ctx.fillStyle = '#475569';
ctx.font = 'bold 12px Noto Sans TC';
ctx.textAlign = 'center';
ctx.fillText(node.name, tx, ty);
});
// Draw Chords
nodes.forEach((node, i) => {
nodes.forEach((target, j) => {
if(i === j) return;
const sAngle = (i * Math.PI * 2) / nodes.length + 0.5;
const eAngle = (j * Math.PI * 2) / nodes.length + 0.5;
const sx = centerX + radius * Math.cos(sAngle);
const sy = centerY + radius * Math.sin(sAngle);
const ex = centerX + radius * Math.cos(eAngle);
const ey = centerY + radius * Math.sin(eAngle);
ctx.beginPath();
ctx.moveTo(sx, sy);
ctx.quadraticCurveTo(centerX, centerY, ex, ey);
ctx.strokeStyle = node.color + '44'; // Translucent
ctx.lineWidth = 3 + Math.random() * 5;
ctx.stroke();
});
});
}
// --- SANKEY PLOT (Custom Canvas) ---
function drawSankey() {
const canvas = document.getElementById('canvasSankey');
const ctx = canvas.getContext('2d');
ctx.clearRect(0, 0, canvas.width, canvas.height);
const padding = 50;
const colWidth = 150;
const boxH = 40;
const gap = 30;
const senders = ['Tumor', 'TME_Macro', 'Fibro'];
const pathways = ['WNT', 'TGF-b', 'VEGF'];
const receivers = ['T_Cell', 'Endo', 'Myo'];
const drawBox = (x, y, text, color) => {
ctx.fillStyle = color;
ctx.roundRect ? ctx.roundRect(x, y, 100, boxH, 4) : ctx.rect(x, y, 100, boxH);
ctx.fill();
ctx.fillStyle = '#fff';
ctx.font = 'bold 11px sans-serif';
ctx.textAlign = 'center';
ctx.fillText(text, x + 50, y + 25);
};
const drawFlow = (x1, y1, x2, y2, color) => {
ctx.beginPath();
ctx.moveTo(x1 + 100, y1 + 5);
ctx.bezierCurveTo(x1 + 150, y1 + 5, x2 - 50, y2 + 5, x2, y2 + 5);
ctx.lineTo(x2, y2 + boxH - 5);
ctx.bezierCurveTo(x2 - 50, y2 + boxH - 5, x1 + 150, y1 + boxH - 5, x1 + 100, y1 + boxH - 5);
ctx.fillStyle = color + '33';
ctx.fill();
};
// Draw flows first (background)
senders.forEach((s, si) => {
pathways.forEach((p, pi) => {
if(Math.random() > 0.4) drawFlow(padding, padding + si*(boxH+gap), padding+colWidth+50, padding + pi*(boxH+gap), '#3b82f6');
});
});
pathways.forEach((p, pi) => {
receivers.forEach((r, ri) => {
if(Math.random() > 0.3) drawFlow(padding+colWidth+50, padding + pi*(boxH+gap), padding+colWidth*2+100, padding + ri*(boxH+gap), '#10b981');
});
});
// Draw nodes
senders.forEach((s, i) => drawBox(padding, padding + i*(boxH+gap), s, '#1e293b'));
pathways.forEach((p, i) => drawBox(padding+colWidth+50, padding + i*(boxH+gap), p, '#3b82f6'));
receivers.forEach((r, i) => drawBox(padding+colWidth*2+100, padding + i*(boxH+gap), r, '#10b981'));
// Column Headers
ctx.fillStyle = '#94a3b8';
ctx.font = 'bold 10px sans-serif';
ctx.fillText('SENDERS', padding + 50, 30);
ctx.fillText('PATHWAYS', padding + colWidth + 100, 30);
ctx.fillText('RECEIVERS', padding + colWidth*2 + 150, 30);
}
// Initialize first section
window.onload = () => {
drawChord();
drawSankey();
};
</script>
</body>
</html>