AgentSkillsCN

tcga-bulk-data-preprocessing-with-omicverse

引导Claude将TCGA样本表、表达档案和临床数据集导入omicverse，初始化生存元数据，并导出带注释的AnnData文件。

SKILL.md
--- frontmatter
name: tcga-bulk-data-preprocessing-with-omicverse
title: TCGA bulk data preprocessing with omicverse
description: Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.

TCGA bulk data preprocessing with omicverse

Overview

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.

Instructions

  1. •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/.
  2. •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.
  3. •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>).
  4. •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).
  5. •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.
  6. •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.
  7. •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.

Examples

  • •"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."

References