Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.
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.
omicverse_guide/docs/Tutorials-bulk/data/TCGA_OV/omicverse as ov (and scanpy as sc if plotting) then call ov.plot_set().aml_tcga = ov.bulk.pyTCGA(sample_sheet_path, download_dir, clinical_dir).aml_tcga.adata_init() to build the AnnData object with raw counts, FPKM, and TPM layers.aml_tcga.adata.write_h5ad('data/TCGA_OV/ov_tcga_raw.h5ad', compression='gzip').aml_tcga.adata_read(<path>).aml_tcga.adata_meta_init() to convert gene IDs to symbols and attach patient info.aml_tcga.survial_init() (note the intentional spelling in the API).aml_tcga.survival_analysis('GENE', layer='deseq_normalize', plot=True).aml_tcga.survial_analysis_all(); warn that it may take time.aml_tcga.adata.write_h5ad('.../ov_tcga_survial_all.h5ad', compression='gzip')).clinical_cart contents..h5ad."t_tcga.ipynbdata/TCGA_OV/reference.md