Search the output of Dyscovr, an integrative linear regression-based method developed by the Singh lab at Princeton University, by driver gene(s), putative target gene(s), and cancer type(s). If you use Dyscovr's output in your own work, please cite as:
Geraghty, S., Boyer, J.A., Fazel-Zarandi, M., Arzouni, N., Ryseck, R., McBride, M., Parsons, L.R., Rabinowitz, J., Singh M. (2024). Integrative Computational Framework, Dyscovr, Links Mutated Driver Genes to Expression Dysregulation Across 19 Cancer Types. bioRxiv.


The name of a cancer driver gene from Vogelstein et al. (Science 2013, 10.1126/science.1235122) whose mutation status is correlated to target gene transcriptional dysregulation by Dyscovr.

The name of a putative target gene whose expression in cancer is correlated to the mutation status of a cancer driver gene from Vogelstein et al. (Science 2013, 10.1126/science.1235122).


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Suggested: 0.01 for PanCancer, 0.2 for all other cancer types.

The adjusted p-value, which represents the statistical significance of the correlation between driver gene mutation status and target gene expression. Multiple hypothesis testing correction performed using the q-value method by Storey et al. (Ann. Statist. 2003, 10.1214/aos/1074290335).


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Dyscovr overview