從原始數據到生物學洞見——12 個步驟帶你掌握單細胞轉錄組分析的核心流程，搭配互動模擬與程式碼範例。
From raw data to biological insights — master single-cell transcriptomics in 12 steps, with interactive simulations and code examples.
根據 nFeature、nCount、MT% 過濾低品質細胞，移除空液滴與 doublets。
Filter low-quality cells by nFeature, nCount, MT%. Remove empty droplets and doublets.
校正細胞間定序深度差異，比較 LogNormalize、Scran 與 SCTransform。
Correct sequencing depth differences. Compare LogNormalize, Scran, and SCTransform.
挑選具有高生物變異的基因，作為 PCA 與聚類的核心輸入特徵。
Select genes with highest biological variation as key input features for PCA and clustering.
Z-score 標準化讓基因在相近尺度下進入下游分析。
Z-score normalization gives every gene equal weight in downstream analysis.
壓縮基因維度，保留關鍵變異、降低噪音。
Compress gene dimensions — retain key variation, reduce noise.
在 PCA 空間中建立近鄰圖，以 Louvain/Leiden 辨識細胞族群。
Build neighbor graphs in PCA space. Identify cell populations via Louvain/Leiden.
將高維數據投影到 2D，觀察細胞群間的相對關係。
Project high-dimensional data to 2D to visualize cell population relationships.
利用 marker genes 與參考數據集，為 cluster 指定細胞類型名稱。
Assign cell type identities to clusters using marker genes and reference datasets.
比較不同 cluster 或 condition 間的基因表達差異。
Compare gene expression differences between clusters or conditions.
整合多樣本/多批次資料，降低 batch effect 並保留真實生物差異。
Integrate multi-sample/multi-batch data. Remove batch effects while preserving true biology.
分析 ligand-receptor 交互作用，探索細胞間訊號傳遞與微環境調控。
Analyze ligand-receptor interactions. Explore intercellular signaling and microenvironment regulation.
推估細胞狀態轉變與發育路徑，適合分化或疾病進程研究。
Infer cell state transitions and developmental paths for differentiation or disease progression studies.
12 主題的學術論文、官方文件、Best Practices 綜述與 marker 資料庫整理，附 DOI 連結與疑義注記。
Academic papers, official docs, best-practice reviews, and marker databases for all 12 topics — with DOI links and errata notes.
涵蓋 12 主題的單選、多選、是非題，內建錯題複習、隨機練習與多使用者進度。
Single-choice, multi-select, and true/false questions covering all 12 topics, with wrong-answer review, random practice, and multi-user progress tracking.