hvantk:resource-expression-atlas

Build standardized AnnData objects from EBI Expression Atlas bulk-RNA-seq TPM matrices and SDRF metadata.

Updated Feb 2, 2024

Search Tags:#bioinformatics #data-pipeline #anndata #rna-seq #transcriptomics #expression-atlas

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill automates the complex process of transforming raw EBI Expression Atlas bulk-RNA-seq data into standardized AnnData objects, removing the need for manual parsing and metadata alignment.

Core Features & Use Cases

Automated Data Pipeline: Downloads and parses gene-centric TPM matrices and SDRF metadata files directly from EBI FTP servers.
Standardized Output: Produces clean, provenance-stamped AnnData objects ready for downstream transcriptomics analysis.
Use Case: Researchers can quickly build a local AnnData dataset from an EBI accession like E-GTEX-8 to perform tissue-specificity scoring or cross-tissue expression comparisons.

Quick Start

Use the hvantk reprocess command to build an AnnData object for the E-GTEX-8 dataset by specifying the expression-atlas plugin and the desired output path.

Frequently Asked Questions about hvantk:resource-expression-atlas

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

FAQPage Schema

How do I convert EBI Expression Atlas bulk RNA-seq data into an AnnData object?▼

You can convert EBI Expression Atlas bulk RNA-seq data into an AnnData object by using a data pipeline that downloads gene-centric TPM matrices and SDRF metadata, then transposes and reshapes them into a standardized format for transcriptomics analysis.

What is the best way to parse SDRF sample metadata for baseline bulk-RNA-seq experiments?▼

Parsing SDRF sample metadata for baseline bulk-RNA-seq experiments is best handled by automated pipelines that extract sample attributes directly from EBI FTP servers and align them with TPM expression matrices to ensure reproducible dataset construction.

Does this transcriptomics workflow require specific Python dependencies to build AnnData objects?▼

Building AnnData objects in this transcriptomics workflow requires Python dependencies including anndata, pandas, and numpy to perform matrix transposition and metadata reshaping, along with requests and tqdm for data downloading.

Can I use EBI Expression Atlas TPM matrices for cross-tissue expression comparisons?▼

EBI Expression Atlas TPM matrices can be used for cross-tissue expression comparisons by constructing a local AnnData dataset from an EBI accession like E-GTEX-8, which integrates gene-centric expression values with sample metadata for downstream analysis.

Why does my AnnData construction fail when aligning heterogeneous omics sources from EBI?▼

AnnData construction from heterogeneous omics sources often fails due to misaligned SDRF metadata and TPM matrices; automating the parsing and matrix transposition ensures provenance-stamped, clean objects ready for analysis.

When do I need to build standardized AnnData objects from EBI Expression Atlas?▼

You need to build standardized AnnData objects from EBI Expression Atlas when performing tissue-specificity scoring or cross-tissue expression comparisons that require reproducible dataset construction from heterogeneous bulk-RNA-seq sources.