GenomicSuperSignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases

Sehyun Oh, Ludwig Geistlinger, Marcel Ramos, Daniel Blankenberg, Marius Beek, Jaclyn N. Taroni, Vincent J. Carey, Casey S. Greene, Levi Waldron and Sean Davis ()
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Sehyun Oh: City University of New York
Ludwig Geistlinger: Harvard Medical School
Marcel Ramos: City University of New York
Daniel Blankenberg: Lerner Research Institute, Cleveland Clinic
Marius Beek: The Pennsylvania State University
Jaclyn N. Taroni: Alex’s Lemonade Stand Foundation
Vincent J. Carey: Harvard Medical School
Casey S. Greene: University of Colorado Anschutz School of Medicine
Levi Waldron: City University of New York
Sean Davis: University of Colorado Anschutz School of Medicine

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a method for interpreting new transcriptomic datasets through instant comparison to public datasets without high-performance computing requirements. We apply Principal Component Analysis on 536 studies comprising 44,890 human RNA sequencing profiles and aggregate sufficiently similar loading vectors to form Replicable Axes of Variation (RAV). RAVs are annotated with metadata of originating studies and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrate the efficient and coherent database search, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature aids in analyzing new gene expression data in the context of existing databases using minimal computing resources.

Date: 2022
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DOI: 10.1038/s41467-022-31411-3

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