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

Sehyun Oh, Ludwig Geistlinger, Marcel Ramos, Daniel Blankenberg, View ORCID ProfileMarius van den Beek, Jaclyn N. Taroni, View ORCID ProfileVincent Carey, View ORCID ProfileCasey Greene, Levi Waldron, View ORCID ProfileSean Davis
doi: https://doi.org/10.1101/2021.05.26.445900
Sehyun Oh
1City University of New York, New York, NY, USA
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Ludwig Geistlinger
2Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA
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Marcel Ramos
1City University of New York, New York, NY, USA
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Daniel Blankenberg
3Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
4Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
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Marius van den Beek
5The Pennsylvania State University, State College, PA, USA
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Jaclyn N. Taroni
6Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, PA, USA
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Vincent Carey
7Harvard Medical School, Boston, MA, USA
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Casey Greene
8University of Colorado Anschutz School of Medicine, Denver, CO, USA
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Levi Waldron
1City University of New York, New York, NY, USA
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Sean Davis
8University of Colorado Anschutz School of Medicine, Denver, CO, USA
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  • For correspondence: seandavi@gmail.com
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Abstract

Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. We performed Principal Component Analysis on 536 studies comprising 44,890 RNA sequencing profiles. Sufficiently similar loading vectors were aggregated to form Replicable Axes of Variation (RAV). RAVs were annotated with metadata of originating studies and samples 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 demonstrated the efficient and coherent database searching, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature will aid analyzing new gene expression data in the context of existing databases using minimal computing resources.

PURPOSE Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. Existing methods for leveraging these public resources have focused on the reanalysis of existing data or analysis of new datasets independently. We present a novel approach to interpreting new transcriptomic datasets by near-instantaneous comparison to public archives without high-performance computing requirements.

METHODS To identify replicable and interpretable axes of variation in any given gene expression dataset, we performed Principal Component Analysis (PCA) on 536 studies comprising 44,890 RNA sequencing profiles. Sufficiently similar loading vectors, when compared across studies, were aggregated to form Replicable Axes of Variation (RAV). RAVs were annotated with metadata of originating studies and samples 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.

RESULTS RAVs are robust to batch effects and the presence of low-quality or irrelevant studies, and identify signals that can be lost by merging samples across the training datasets. The GenomicSuperSignature package allows instantaneous matching of PCA axes in new datasets to pre-computed RAVs, cutting down the analysis time from days to the order of seconds on an ordinary laptop. We demonstrate that RAVs associated with a phenotype can provide insight into weak or indirectly measured biological attributes in a new study by leveraging accumulated data from published datasets. Benchmarking against complementary previous works demonstrates that the RAV index 1) identifies colorectal carcinoma transcriptome subtypes that are similar to but more correlated with clinicopathological characteristics than previous disease-specific efforts and 2) can estimate neutrophil counts through transfer learning on new data comparably to the previous efforts despite major differences in training datasets and model building processes with the additional benefits of flexibility and scalability of the model application.

CONCLUSION GenomicSuperSignature establishes an information resource and software tools to interrogate it. Prior knowledge databases are coherently linked, enabling researchers to analyze new gene expression data in the context of existing databases using minimal computing resources. The robustness of GenomicSuperSignature suggests that we can expand this approach beyond human gene expression profiles, such as single-cell RNA-seq, microbiome abundance, and different species’ transcriptomics datasets.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://www.bioconductor.org/packages/GenomicSuperSignature

  • https://github.com/shbrief/GenomicSuperSignature

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 14, 2022.
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GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases
Sehyun Oh, Ludwig Geistlinger, Marcel Ramos, Daniel Blankenberg, Marius van den Beek, Jaclyn N. Taroni, Vincent Carey, Casey Greene, Levi Waldron, Sean Davis
bioRxiv 2021.05.26.445900; doi: https://doi.org/10.1101/2021.05.26.445900
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GenomicSuperSignature: interpretation of RNA-seq experiments through robust, efficient comparison to public databases
Sehyun Oh, Ludwig Geistlinger, Marcel Ramos, Daniel Blankenberg, Marius van den Beek, Jaclyn N. Taroni, Vincent Carey, Casey Greene, Levi Waldron, Sean Davis
bioRxiv 2021.05.26.445900; doi: https://doi.org/10.1101/2021.05.26.445900

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