tcga-bulk-data-preprocessing-with-omicverse

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

TCGA bulk data preprocessing with omicverse

使用omicverse进行TCGA批量数据预处理

Overview

概述

Follow this skill to recreate the preprocessing routine from

t_tcga.ipynb

. It automates loading TCGA downloads, generating raw/normalised matrices, initialising metadata, and running survival analyses through

ov.bulk.pyTCGA

按照本指南复现

t_tcga.ipynb

中的预处理流程。它通过

ov.bulk.pyTCGA

自动化加载TCGA下载数据、生成原始/归一化矩阵、初始化元数据并运行生存分析。

Instructions

操作步骤

Gather required downloads
- Confirm the user has:
  - ```
  gdc_sample_sheet.<date>.tsv
```
  from the TCGA Sample Sheet export.
- The decompressed
```
  gdc_download_xxxxx
```
  directory containing expression archives.
- The
```
  clinical.cart.<date>
```
  directory with clinical XML/JSON files.
- Mention that sample data are available under
```
omicverse_guide/docs/Tutorials-bulk/data/TCGA_OV/
```
  .
Initialise the TCGA helper
- Import
```
omicverse as ov
```
  (and
```
scanpy as sc
```
  if plotting) then call
```
ov.plot_set()
```
  .
- Instantiate
```
aml_tcga = ov.bulk.pyTCGA(sample_sheet_path, download_dir, clinical_dir)
```
  .
- Run
```
aml_tcga.adata_init()
```
  to build the AnnData object with raw counts, FPKM, and TPM layers.
Persist the dataset
- Encourage saving the initial AnnData:
```
aml_tcga.adata.write_h5ad('data/TCGA_OV/ov_tcga_raw.h5ad', compression='gzip')
```
  .
- When reloading, reconstruct the class with the same paths and call
```
aml_tcga.adata_read(<path>)
```
  .
Initialise metadata and clinical information
- Populate sample metadata using
```
aml_tcga.adata_meta_init()
```
  to convert gene IDs to symbols and attach patient info.
- Add survival attributes via
```
aml_tcga.survial_init()
```
  (note the intentional spelling in the API).
Perform survival analyses
- Plot gene-level survival curves with
```
aml_tcga.survival_analysis('GENE', layer='deseq_normalize', plot=True)
```
  .
- To process all genes, call
```
aml_tcga.survial_analysis_all()
```
  ; warn that it may take time.
Export results
- Save enriched metadata to a new AnnData file (
```
aml_tcga.adata.write_h5ad('.../ov_tcga_survial_all.h5ad', compression='gzip')
```
  ).
- Suggest exporting summary tables (e.g., survival statistics) if users need to share outputs outside Python.
Troubleshooting tips
- Ensure TCGA archives are fully extracted; missing XML/TSV files trigger parsing errors.
- The helper expects matching case IDs between the sample sheet and expression files—direct users to re-download if IDs do not align.
- Survival plots require clinical dates; if absent, instruct users to check the
```
clinical_cart
```
  contents.

准备所需的下载数据
- 确认用户已获取：
  - 从TCGA样本表导出的
```
gdc_sample_sheet.<date>.tsv
```
    文件。
  - 解压后的
```
gdc_download_xxxxx
```
    目录，其中包含表达档案。
  - 包含临床XML/JSON文件的
```
clinical.cart.<date>
```
    目录。
- 提示样本数据可在
```
omicverse_guide/docs/Tutorials-bulk/data/TCGA_OV/
```
  路径下获取。
初始化TCGA辅助工具
- 导入
```
omicverse as ov
```
  （如果需要绘图还需导入
```
scanpy as sc
```
  ），然后调用
```
ov.plot_set()
```
  。
- 实例化对象：
```
aml_tcga = ov.bulk.pyTCGA(sample_sheet_path, download_dir, clinical_dir)
```
  。
- 运行
```
aml_tcga.adata_init()
```
  构建包含原始计数、FPKM和TPM层的AnnData对象。
持久化数据集
- 建议保存初始AnnData对象：
```
aml_tcga.adata.write_h5ad('data/TCGA_OV/ov_tcga_raw.h5ad', compression='gzip')
```
  。
- 重新加载时，使用相同路径重建类并调用
```
aml_tcga.adata_read(<path>)
```
  。
初始化元数据和临床信息
- 使用
```
aml_tcga.adata_meta_init()
```
  填充样本元数据，将基因ID转换为基因符号并关联患者信息。
- 通过
```
aml_tcga.survial_init()
```
  添加生存属性（注意API中的拼写为survial）。
执行生存分析
- 使用
```
aml_tcga.survival_analysis('GENE', layer='deseq_normalize', plot=True)
```
  绘制基因水平的生存曲线。
- 若要对所有基因进行分析，调用
```
aml_tcga.survial_analysis_all()
```
  ；需注意此过程可能耗时较长。
导出结果
- 将富含元数据的数据集保存为新的AnnData文件：
```
aml_tcga.adata.write_h5ad('.../ov_tcga_survial_all.h5ad', compression='gzip')
```
  。
- 建议用户若需在Python外共享输出结果，可导出汇总表格（如生存统计数据）。
故障排除提示
- 确保TCGA档案已完全解压；缺失XML/TSV文件会触发解析错误。
- 辅助工具要求样本表和表达文件中的病例ID匹配；若不匹配，指导用户重新下载数据。
- 生存曲线绘制需要临床日期数据；若缺失，指导用户检查
```
clinical_cart
```
  目录内容。

Examples

示例

"Read my TCGA OV download, initialise metadata, and plot MYC survival curves using DESeq-normalised counts."
"Reload a saved AnnData file, attach survival annotations, and export the updated
```
.h5ad
```
."
"Run survival analysis for all genes and store the enriched dataset."

"读取我的TCGA OV下载数据，初始化元数据，并使用DESeq归一化计数绘制MYC基因的生存曲线。"
"重新加载已保存的AnnData文件，添加生存注释，并导出更新后的
```
.h5ad
```
文件。"
"对所有基因运行生存分析并存储富含信息的数据集。"

References

参考资料

Tutorial notebook:
```
t_tcga.ipynb
```
Sample dataset:
```
data/TCGA_OV/
```
Quick copy/paste commands:
```
reference.md
```

教程笔记本：
```
t_tcga.ipynb
```
样本数据集：
```
data/TCGA_OV/
```
快速复制命令：
```
reference.md
```