hvantk:resource-cptac-expression

To convert long-format CPTAC protein expression data into an AnnData object, this skill pivots the expression values and merges clinical sample metadata to create a standardized, analysis-ready matrix. It requires pandas and hvantk core utilities to ensure consistent schema and provenance tracking.

Preparing CPTAC cancer cohort data for scanpy workflows requires building an AnnData object from raw expression and metadata files. This skill standardizes the pivoting of sample and gene expression tables and automatically joins sample-level clinical metadata into the final structure.

Yes, this CPTAC expression transformation automatically joins sample-level clinical metadata with the expression matrices. It integrates cohort-specific data by pivoting long-format expression values and merging the clinical metadata directly into the standardized AnnData object.

Yes, you can use hvantk core utilities to ensure consistent schema and provenance tracking for proteomics data. The skill relies on these utilities alongside pandas and numpy to transform CPTAC expression matrices into standardized AnnData objects for research.

The dependencies needed to build AnnData protein expression matrices from CPTAC are pandas, numpy, anndata, and hvantk core utilities. These libraries support the pivoting of long-format expression tables and the integration of clinical sample metadata.