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Updated: Sep 17, 2025

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Consistently processed RNA sequencing data from 50 sources enriched for pediatric data.

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Summary

We created five large, consistently processed gene expression compendia from 16,446 RNA sequencing datasets. This harmonized data enhances tumor gene expression analysis and enables new research discoveries.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Tumor gene expression analysis requires large cohorts for statistical power.
  • Inconsistent data processing and metadata accuracy hinder the analysis of diverse datasets.
  • Existing datasets often suffer from variability, limiting their utility for robust research.

Purpose of the Study:

  • To develop consistently processed gene expression compendia from a large number of diverse RNA sequencing datasets.
  • To harmonize clinical metadata alongside gene expression values for comprehensive analysis.
  • To provide unrestricted access to high-quality, standardized datasets for cancer research.

Main Methods:

  • Acquired RNA sequencing data from public repositories and clinical partners.
  • Assessed data quality, quantified gene expression, and harmonized clinical metadata.
  • Developed a dockerized pipeline for consistent data processing and analysis.

Main Results:

  • Generated five compendia comprising 16,446 RNA sequencing datasets.
  • Ensured consistent processing and harmonized metadata across all datasets.
  • Released expression values and metadata without access restrictions.

Conclusions:

  • The developed compendia provide a powerful, standardized resource for tumor gene expression analysis.
  • These datasets facilitate diverse research applications, including tumor type comparison, cell line validation, and n-of-1 studies.
  • The freely available pipeline enables the comparison of new data to the compendia, advancing cancer genomics research.