Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Osorio, D.; Capasso, A.; Eckhardt, S. G.; Giri, U.; Somma, A.; Pitts, T. M.; Lieu, C. H.; Messersmith, W. A.; Bagby, S. M.; Singh, H.; Das, J.; Sahni, N.; Yi, S. S.; Kuijjer, M. L.

doi:10.1101/2023.01.20.524974

bioRxiv · 10.1101/2023.01.20.524974

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.

Osorio, D.·Capasso, A.·Eckhardt, S. G.·Giri, U.·Somma, A.·Pitts, T. M.·Lieu, C. H.·Messersmith, W. A.·Bagby, S. M.·Singh, H.·Das, J.·Sahni, N.·Yi, S. S.·Kuijjer, M. L.

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Abstract

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. We present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single cell/nuclei RNA-seq data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperforms 12 other gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPIONs scalability to population-level analyses using a single-cell RNA-seq atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences detected by SCORPION between tumor regions are consistent across population cohorts, as well as with our understanding of disease progression and elucidate phenotypic regulators that may impact patient survival.

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Osorio, D., Capasso, A., Eckhardt, S. G., Giri, U., Somma, A., Pitts, T. M., Lieu, C. H., Messersmith, W. A., Bagby, S. M., Singh, H., Das, J., Sahni, N., Yi, S. S., Kuijjer, M. L.. 2023-01-20. Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data.. https://doi.org/10.1101/2023.01.20.524974

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