tcga-bulk-data-preprocessing-with-omicverse

name	tcga-bulk-data-preprocessing-with-omicverse
title	TCGA bulk data preprocessing with omicverse
description	TCGA bulk RNA-seq preprocessing with pyTCGA: GDC sample sheets, expression archives, clinical metadata, Kaplan-Meier survival analysis, and annotated AnnData export.

TCGA Bulk Data Preprocessing with OmicVerse

Overview

Use this skill for loading TCGA data from GDC downloads, building normalised expression matrices, attaching clinical metadata, and running survival analyses through ov.bulk.pyTCGA.

Instructions

1. Gather required downloads

Confirm the user has three items from the GDC Data Portal:

• gdc_sample_sheet.<date>.tsv — the sample sheet export
• Decompressed gdc_download_xxxxx/ directory with expression archives
• clinical.cart.<date>/ directory with clinical XML/JSON files

2. Initialise the TCGA helper

import omicverse as ov
import scanpy as sc
ov.plot_set()

aml_tcga = ov.bulk.pyTCGA(sample_sheet_path, download_dir, clinical_dir)
aml_tcga.adata_init()  # Builds AnnData with raw counts, FPKM, and TPM layers

3. Persist and reload

aml_tcga.adata.write_h5ad('data/ov_tcga_raw.h5ad', compression='gzip')

# To reload later:
new_tcga = ov.bulk.pyTCGA(sample_sheet_path, download_dir, clinical_dir)
new_tcga.adata_read('data/ov_tcga_raw.h5ad')

4. Initialise metadata and survival

aml_tcga.adata_meta_init()   # Gene ID → symbol mapping, patient info
aml_tcga.survial_init()      # NOTE: "survial" spelling — see Critical API Reference below

5. Run survival analysis

# Single gene
aml_tcga.survival_analysis('MYC', layer='deseq_normalize', plot=True)

# All genes (can take minutes for large gene sets)
aml_tcga.survial_analysis_all()  # NOTE: "survial" spelling

6. Export results

aml_tcga.adata.write_h5ad('data/ov_tcga_survival.h5ad', compression='gzip')

Critical API Reference

IMPORTANT: Method Name Spelling Inconsistency

The pyTCGA API has an intentional spelling inconsistency. Two methods use "survial" (missing the 'v') while one uses the correct "survival":

Method	Spelling	Purpose
survial_init()	survial (no 'v')	Initialize survival metadata columns
survival_analysis(gene, layer, plot)	survival (correct)	Single-gene Kaplan-Meier curve
survial_analysis_all()	survial (no 'v')	Sweep all genes for survival significance

# CORRECT — use the exact method names as documented
aml_tcga.survial_init()                    # "survial" — no 'v'
aml_tcga.survival_analysis('MYC', layer='deseq_normalize', plot=True)  # "survival" — correct
aml_tcga.survial_analysis_all()            # "survial" — no 'v'

# WRONG — these will raise AttributeError
# aml_tcga.survival_init()                 # AttributeError! Use survial_init()
# aml_tcga.survival_analysis_all()         # AttributeError! Use survial_analysis_all()

Survival Analysis Methodology

survival_analysis() performs Kaplan-Meier analysis:

1. Splits patients into high/low expression groups using the median as cutoff
2. Computes a log-rank test p-value to assess significance
3. If plot=True, renders survival curves with confidence intervals

Layer selection matters: Use layer='deseq_normalize' (recommended) because DESeq2 normalization accounts for library size and composition bias, making expression comparable across samples. Alternative: layer='tpm' for TPM-normalized values.

Defensive Validation Patterns

import os

# Before pyTCGA init: verify all paths exist
for name, path in [('sample_sheet', sample_sheet_path),
                    ('downloads', download_dir),
                    ('clinical', clinical_dir)]:
    if not os.path.exists(path):
        raise FileNotFoundError(f"TCGA {name} path not found: {path}")

# After adata_init(): verify expected layers were created
expected_layers = ['counts', 'fpkm', 'tpm']
for layer in expected_layers:
    if layer not in aml_tcga.adata.layers:
        print(f"WARNING: Missing layer '{layer}' — check if TCGA archives are fully extracted")

# Before survival analysis: verify metadata is initialized
if 'survial_init' not in dir(aml_tcga) or aml_tcga.adata.obs.shape[1] < 5:
    print("WARNING: Run adata_meta_init() and survial_init() before survival analysis")

Troubleshooting

• AttributeError: 'pyTCGA' object has no attribute 'survival_init': Use the misspelled name survial_init() (missing 'v'). Same for survial_analysis_all(). See Critical API Reference above.
• KeyError during adata_meta_init(): Gene IDs in the expression matrix don't match expected format. TCGA uses ENSG IDs; the method maps them to symbols internally. Ensure archives are from the same GDC download.
• Empty survival plot or NaN p-values: Clinical XML files are missing date fields (days_to_death, days_to_last_follow_up). Check that the clinical.cart.* directory contains complete XML files, not just metadata JSONs.
• survial_analysis_all() runs very slowly: This tests every gene individually. For a genome with ~20,000 genes, expect 5-15 minutes. Consider filtering to genes of interest first.
• Sample sheet column mismatch: Verify the TSV uses tab separators and the header row matches GDC's expected format. Re-download from GDC if column names differ.
• Missing deseq_normalize layer: This layer is created during adata_meta_init(). If absent, re-run the metadata initialization step.

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 page: t_tcga
• Quick copy/paste commands: reference.md