tcga-bulk-data-preprocessing-with-omicverse

TCGA bulk data preprocessing with omicverse

Overview

Follow this skill to recreate the preprocessing routine from [t_tcga.ipynb](../../omicverse_guide/docs/Tutorials-bulk/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/](../../omicverse_guide/docs/Tutorials-bulk/data/TCGA_OV/).

1. 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.

1. 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>).

1. 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).

1. 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.

1. 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.

1. 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

• Tutorial notebook: [t_tcga.ipynb](../../omicverse_guide/docs/Tutorials-bulk/t_tcga.ipynb)
• Sample dataset: [data/TCGA_OV/](../../omicverse_guide/docs/Tutorials-bulk/data/TCGA_OV/)
• Quick copy/paste commands: [reference.md](reference.md)