A Human Pan-Disease Whole Blood Transcriptomics Atlas Reveals Systemic Signatures Across Diseases
Mardinoglu, Adil, Li, Mengzhen
Abstract
Mardinoglu, Adil, Li, Mengzhen
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.
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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