tcga-preprocessing_skill

Overview

This skill guides Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse to produce annotated AnnData files ready for downstream analysis. It automates building raw and normalized matrices, initializing sample and survival metadata, and exporting enriched .h5ad files. The workflow mirrors a reproducible Jupyter notebook routine for TCGA bulk RNA-seq preprocessing with omicverse.

How this skill works

The skill instructs loading three inputs: the TCGA sample sheet TSV, the decompressed expression download directory, and the clinical cart directory. It shows how to instantiate ov.bulk.pyTCGA, run adata_init() to assemble raw counts, FPKM and TPM layers, initialize metadata and survival attributes (noting the API method name survial_init()), and perform gene-level survival analyses. Final steps cover saving the AnnData object and exporting summary tables for sharing.

When to use it

• Preparing TCGA bulk RNA-seq downloads for omicverse analysis and visualization.
• Standardizing raw and normalized expression matrices into a single AnnData file.
• Annotating samples with clinical metadata and survival attributes for downstream modeling.
• Running gene-level survival plots or full-cohort survival scans before downstream statistics.
• Recreating a published preprocessing pipeline or sharing processed TCGA datasets.

Best practices

• Ensure the sample sheet, extracted expression archives, and clinical cart are complete and match by case IDs.
• Save the initial assembled AnnData after adata_init() to avoid reprocessing large downloads.
• Verify clinical XML/JSON contain date fields required for survival; otherwise survival will be incomplete.
• Use consistent file paths when reconstructing the pyTCGA helper; call adata_read() to reload saved .h5ad.
• Be aware that survial_analysis_all() processes many genes and can be time-consuming; run on a cluster if available.

Example use cases

• Read a TCGA OV download set, build the AnnData with raw and DESeq-normalised layers, then plot MYC survival curves.
• Reload a previously saved ov_tcga_raw.h5ad, run metadata and survival initialization, and export ov_tcga_survial_all.h5ad.
• Run survial_analysis_all() to compute survival statistics across all genes and save summary tables for publication.
• Troubleshoot ID mismatches between sample_sheet and expression files and re-download specific archives when needed.