Discovery of gene-gene co-perturbation through big data

Wang, J.; Wan, Y.-W.; Al-Ouran, R.; Liu, Z.

doi:10.1101/2022.10.17.512527

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bioRxiv · 10.1101/2022.10.17.512527

Discovery of gene-gene co-perturbation through big data

Wang, J.·Wan, Y.-W.·Al-Ouran, R.·Liu, Z.

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Abstract

Millions of RNA sequencing samples have been deposited into public databases, providing a rich resource for biological research. These datasets encompass tens of thousands of experiments and offer comprehensive insights into human cellular regulation. However, a major challenge is how to integrate these experiments that acquired at different conditions. We propose a new statistical tool based on beta-binomial distributions that can construct robust gene co-regulation network (CoRegNet) across tens of thousands of experiments. Our analysis of over 12,000 experiments involving human tissues and cells shows that CoRegNet significantly outperforms existing gene co-expression-based methods. Although the majority of the genes are linearly co-regulated, we did discover an interesting set of genes that are non-linearly co-regulated; half of the time they change in the same direction and the other half they change in the opposite direction. Additionally, we identified a set of gene pairs that follows the Simpsons paradox. By utilizing public domain data, CoRegNet offers a powerful approach for identifying functionally related gene pairs, thereby revealing new biological insights.

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Wang, J., Wan, Y.-W., Al-Ouran, R., Liu, Z.. 2022-10-21. Discovery of gene-gene co-perturbation through big data. https://doi.org/10.1101/2022.10.17.512527

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