TCGA bulk data preprocessing with omicverse

Preprocess TCGA bulk RNA-seq data by ingesting sample sheets, expression archives, and clinical metadata into an AnnData object with raw counts, FPKM, and TPM layers, then attach patient clinical attributes for survival analysis. This Skill automates the entire workflow from file ingestion through metadata initialization to gene-level survival curve generation.

Yes. omicverse, combined with scanpy and pandas, enables loading TCGA expression matrices and clinical data into a unified AnnData object with multiple normalization layers (raw counts, FPKM, TPM) and attached sample and patient metadata for integrated analysis.

Load your TCGA sample sheet, decompressed expression archives, and clinical cart into an annotated AnnData object with initialized expression layers and survival attributes. This unified format streamlines downstream gene-level analysis, survival plotting, and export for further research.

No. This Skill automatically integrates TCGA expression files, sample metadata, and clinical information into a single AnnData object, eliminating manual alignment steps and reducing preprocessing burden for survival analysis workflows.

After preprocessing TCGA data into an AnnData object with DESeq-normalized counts and survival attributes, plot gene-level survival curves using the integrated clinical metadata. This Skill automates both data preparation and survival analysis setup for genes like MYC.

Prepare a gdc_sample_sheet.tsv file, decompressed expression archives containing bulk RNA-seq matrices, and a clinical data cart from the GDC. This Skill ingests these three file types to initialize expression layers, metadata, and survival attributes in an AnnData object.