tcga与gtex原始count数据合并并去除批次效应代码
时间: 2024-02-11 14:04:08 浏览: 50
将TCGA和GTEx的原始count数据合并并去除批次效应的代码可以分为以下几个步骤:
1. 导入所需的Python库
```python
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from combat.pycombat import combat
```
2. 读取TCGA和GTEx的原始count数据
```python
tcga_counts = pd.read_csv("tcga_counts.csv", index_col=0)
gtex_counts = pd.read_csv("gtex_counts.csv", index_col=0)
```
3. 根据基因ID将TCGA和GTEx的count数据进行合并
```python
merged_counts = pd.concat([tcga_counts, gtex_counts], axis=1, sort=False)
merged_counts = merged_counts.loc[~merged_counts.index.duplicated(keep='first')]
```
4. 对合并后的count数据进行标准化
```python
scaler = StandardScaler()
scaled_counts = scaler.fit_transform(merged_counts.T)
scaled_counts = pd.DataFrame(scaled_counts.T, index=merged_counts.index, columns=merged_counts.columns)
```
5. 使用PCA方法进行批次效应的调整
```python
pca = PCA(n_components=min(scaled_counts.shape)-1)
pca.fit(scaled_counts.T)
batch = merged_counts.columns.str.extract(r'(.*)\.(.*)')[0]
adjusted_counts = combat(scaled_counts, batch=batch, model='pca', method='mean')
```
6. 将调整后的count数据保存到文件中
```python
adjusted_counts.to_csv("merged_counts_adjusted.csv")
```
以上是一个简单的合并TCGA和GTEx原始count数据并去除批次效应的流程,具体的实现方式可能因为数据的特殊性而有所不同。