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A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases

Mardinoglu, Adil, Li, Mengzhen

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Abstract

This dataset accompanies the manuscript titled “A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases” Whole-blood transcriptomics (WBT) provides critical insights into systemic health and disease. In this study, we established a large-scale WBT Atlas comprising 4,444 samples across 98 distinct health conditions. Through integrative analyses, we identified disease-specific gene expression signatures and developed a multi-omics classification framework capable of distinguishing among these conditions based on their unique transcriptomic profiles. The dataset includes RNA-seq data from 4,444 samples used for atlas construction, along with 10 cohorts utilized for machine learning–based external validation.

About this research paper

What this paper is about

This dataset accompanies the manuscript titled “A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases” Whole-blood transcriptomics (WBT) provides critical insights into systemic health and disease. In this study, we established a large-scale WBT Atlas comprising 4,444 samples across 98 distinct health conditions. Through integrative analyses, we identified disease-specific gene expression signatures and developed a multi-omics classification framework capable of distinguishing among these conditions based on their unique transcriptomic profiles. The dataset includes RNA-seq data from 4,444 samples used for atlas construction, along with 10 cohorts utilized for machine learning–based external validation.

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Available abstract

This dataset accompanies the manuscript titled “A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases” Whole-blood transcriptomics (WBT) provides critical insights into systemic health and disease. In this study, we established a large-scale WBT Atlas comprising 4,444 samples across 98 distinct health conditions. Through integrative analyses, we identified disease-specific gene expression signatures and developed a multi-omics classification framework capable of distinguishing among these conditions based on their unique transcriptomic profiles. The dataset includes RNA-seq data from 4,444 samples used for atlas construction, along with 10 cohorts utilized for machine learning–based external validation.

Key concepts: Transcriptome, Atlas (anatomy), Computational biology, Biology, Whole blood, Human Protein Atlas, Gene expression profiling, Human blood

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