GenomicSuperSignature: Interpretation of RNA-Seq Experiments through Robust, Efficient Comparison to Public Databases
Event Type
Workshop
Applications
Computational Science
Education and Training and Outreach
HPC Community Collaboration
HPC Training and Education
Machine Learning and Artificial Intelligence
Performance
Workforce
W
TimeSunday, 14 November 20214:45pm - 5pm CST
Location223
DescriptionMillions 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. All necessary data and functions to apply our approach to existing or new data are included in our software available as part of the Bioconductor project.
METHODS:
In brief, we performed Principal Component Analysis (PCA) on the results of 536 public genomics studies comprising 44,890 RNA sequencing profiles. Sufficiently similar loading vectors, when compared across studies, were aggregated to form Replicable Axes of Variation (RAV). We annotated RAVs with metadata of originating studies and by gene set enrichment analysis, forming a knowledge graph. 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 and facilitates the application of a knowledge graph where different prior knowledge databases are coherently linked, and enables 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.
SOFTWARE AVAILABILITY:
Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package (https://bioconductor.org/packages/GenomicSuperSignature).
METHODS:
In brief, we performed Principal Component Analysis (PCA) on the results of 536 public genomics studies comprising 44,890 RNA sequencing profiles. Sufficiently similar loading vectors, when compared across studies, were aggregated to form Replicable Axes of Variation (RAV). We annotated RAVs with metadata of originating studies and by gene set enrichment analysis, forming a knowledge graph. 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 and facilitates the application of a knowledge graph where different prior knowledge databases are coherently linked, and enables 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.
SOFTWARE AVAILABILITY:
Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package (https://bioconductor.org/packages/GenomicSuperSignature).