课程核心案例
案例名称:《EGFR突变肺癌免疫治疗响应预测的多组学研究》
期刊参考:Nature Communications (IF=16.6) 同类研究框架
数据构成:RNA-seq + 临床随访 + 影像组学特征
09:45-10:30 期刊选择策略
import scholarly
def get_journal_impact(keywords):
search_query = scholarly.search_pubs(keywords)
journal_counts = {}
for i, result in enumerate(search_query):
journal = result.bib.get('journal', 'Unknown')
journal_counts[journal] = journal_counts.get(journal, 0) + 1
return sorted(journal_counts.items(), key=lambda x: x[1], reverse=True)
模块2:核心分析代码逐行解析 (10:45-12:30)
10:45-11:30 多组学整合分析
import WGCNA
datExpr = pd.read_csv('RNAseq_normalized.csv', index_col=0)
net = WGCNA.WGCNA(
datExpr,
power=6,
minModuleSize=30,
mergeCutHeight=0.25)
moduleTraitCor = cor(net.MEs, clinicalTraits, use='p')
plt.figure(figsize=(12,9))
sns.heatmap(moduleTraitCor, annot=True, cmap='coolwarm')
11:30-12:30 机器学习建模
from sksurv.ensemble import RandomSurvivalForest
from sklearn.inspection import permutation_importance
X = df[['基因特征1', '临床指标2', '影像组学3']]
y = np.array([(bool(e), t) for e,t in zip(events, times)], dtype=[('status', bool), ('time', float)])
rsf = RandomSurvivalForest(n_estimators=1000, min_samples_split=10, random_state=42)
rsf.fit(X_train, y_train)
result = permutation_importance(rsf, X_test, y_test, n_repeats=15, random_state=42)
pd.Series(result.importances_mean, index=X.columns).plot.barh()
模块3:现场实操工作坊 (13:30-16:00)
13:30-15:00 分组实战
实操案例:构建PD-L1表达预测模型
任务流程:
- TCGA数据加载与清洗(提供预处理代码模板)
- 差异表达分析(DESeq2 R包Python接口)
- LASSO回归特征筛选
- 构建XGBoost预测模型
- 生成发表级可视化图表
15:00-16:00 成果展示与点评
评审标准:
- 分析方法合规性(P值校正/FDR控制)
- 可视化专业度(符合SCI图表规范)
- 临床意义解读深度
模块4:投稿与回复技巧 (16:15-18:00)
16:15-17:00 审稿意见应对策略
from transformers import pipeline
classifier = pipeline("text-classification", model="bert-journal-review")
reviews = ["The sample size is too small...", "The method section lacks details..."]
for result in classifier(reviews):
print(f"类型: {result['label']} (置信度: {result['score']:.2f})")
17:00-18:00 学术伦理与AI工具声明
AI使用声明要点:
- 方法部分注明使用的AI工具(如"Figures were generated using Matplotlib v3.7")
- 致谢章节声明AI辅助内容(如"Language polishing was assisted by Grammarly")
- 避免使用的敏感表述(如"The AI concluded that...")