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Tcga preprocessing

Skill FridrichMethod/awesome-skills/skills/tcga-preprocessing

Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.From its SKILL.md

Install
npx -y skills add FridrichMethod/awesome-skills --skill tcga-preprocessing

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SKILL.md

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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

What ships with it: 1 file

877 B alongside SKILL.md

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