hvantk:resource-cptac-expression

Convert CPTAC protein expression matrices into AnnData objects with metadata integration.

Updated Feb 2, 2024

Search Tags:#bioinformatics #data-processing #proteomics #anndata #expression-analysis #cptac

Ready to run

npx skills add https://github.com/bigbio/hvantk --skill hvantk-resource-cptac-expression

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hvantk:resource-cptac-expression
Source: https://github.com/bigbio/hvantk/tree/main/hvantk/skills/cptac/expression
Command: npx skills add https://github.com/bigbio/hvantk --skill hvantk-resource-cptac-expression

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, anndata, and includes scripts (resource) components.

What problem does it solve?

This skill addresses the friction in proteomics data analysis by automating the transformation of long-format CPTAC protein expression data into standardized, analysis-ready AnnData objects.

Core Features & Use Cases

Standardized Pivoting: Converts long-format (sample, gene) expression tables into wide-format AnnData structures.
Metadata Integration: Automatically joins sample-level clinical metadata with expression matrices.
Use Case: Researchers can quickly prepare CPTAC cancer cohort data for downstream analysis in scanpy or other proteomics workflows by providing raw expression and metadata files.

Quick Start

Use the hvantk resource-cptac-expression skill to build an AnnData object from the provided expression and metadata files.

Frequently Asked Questions about hvantk:resource-cptac-expression

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert long-format CPTAC protein expression data into an AnnData object?▼

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.

What is the best way to prepare CPTAC cancer cohort data for scanpy workflows?▼

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.

Does this CPTAC expression transformation join clinical sample metadata automatically?▼

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.

Can I use hvantk core utilities to ensure consistent schema and provenance tracking for proteomics data?▼

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.

What are the dependencies needed to build AnnData protein expression matrices from CPTAC?▼

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.