Agent skill
tcga-bulk-data-preprocessing-with-omicverse
Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.
Install this agent skill to your Project
npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tcga-preprocessing
SKILL.md
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
- Gather required downloads
- Confirm the user has:
gdc_sample_sheet.<date>.tsvfrom the TCGA Sample Sheet export.- The decompressed
gdc_download_xxxxxdirectory 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/.
- Confirm the user has:
- Initialise the TCGA helper
- Import
omicverse as ov(andscanpy as scif plotting) then callov.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.
- Import
- 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>).
- Encourage saving the initial AnnData:
- 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).
- Populate sample metadata using
- 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.
- Plot gene-level survival curves with
- 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.
- Save enriched metadata to a new AnnData file (
- 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_cartcontents.
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
- Tutorial notebook:
t_tcga.ipynb - Sample dataset:
data/TCGA_OV/ - Quick copy/paste commands:
reference.md
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