Kate Hoff Shutta

tcga-data-nf and NetworkDataCompanion

The Quackenbush lab has published a Nextflow workflow for reproducible processing and analysis of gene regulatory networks in TCGA! Check out the results in our preprint, where we use DRAGON and PANDA networks to investigate consensus molecular subtypes of colon cancer, identifying key epigenetic features related to subtype-specific regulatory differences.

I’m very grateful to have had the opportunity to work on this project, which is led by my talented colleague Viola Fanfani. I’m the lead developer and maintainer of the associated R package NetworkDataCompanion, which can be used with the Nextflow workflow or as a standalone tool to assist with analysis of TCGA data. We hope this tool will be useful for many and we welcome contributions from the community!

Citation: Fanfani, V., Shutta, K.H., Mandros, P., Fischer, J., Saha, E., Micheletti, S., Chen, C., Guebila, M.B., Lopes-Ramos, C.M. and Quackenbush, J., 2024. Reproducible processing of TCGA regulatory networks. bioRxiv. https://doi.org/10.1101/2024.11.05.622163

DRAGON for multi-omic GGMs

DRAGON, our tool for multi-omic Gaussian graphical modeling, has been published in Nucleic Acids Research! DRAGON lives in the Network Zoo and is available as part of netZooPy and netZooR.

Citation: Shutta, K.H.⁺, Weighill, D.⁺, Burkholz, R., Guebila, M.B., Zacharias, H.U., Quackenbush, J. and Altenbuchinger, M., 2023. DRAGON: determining regulatory associations using graphical models on multi-omic networks. Nucleic Acids Research, 51(3), e15-e15. https://doi.org/10.1093/nar/gkac1157 ⁺: equal contribution

Metabolomics of mental health and cardiometabolic health

I’m proud to have contributed to several publications that use metabolomics to understand the relationship between mental health and cardiometabolic health! This work was supported by the NIH NIA (award number R01-AG051600). Some of my primary contributions are cited below.

• Metabolites associated with chronic psychosocial distress Shutta, K.H. ⁺, Balasubramanian, R. ⁺, Huang, T., Jha, S.C., Zeleznik, O.A., Kroenke, C.H., Tinker, L.F., Smoller, J.W., Casanova, R., Tworoger, S.S., Manson, J.E., Clish, C.B., Rexrode, K.M., Hankinson, S.E.⁺⁺, and Kubzansky, L.D.⁺⁺, 2021. Plasma metabolomic profiles associated with chronic distress in women. Psychoneuroendocrinology, 133, p.105420. https://doi.org/10.1016/j.psyneuen.2021.105420.⁺, ⁺⁺: equal contribution
• Mental health and incident cardiovascular disease Balasubramanian, R., Shutta, K. H., Guasch-Ferre, M., Huang, T., Jha, S. C., Zhu, Y., … Hankinson, S.E., & Kubzansky, L. D. (2023). Metabolomic profiles of chronic distress are associated with cardiovascular disease risk and inflammation-related risk factors. Brain, Behavior, and Immunity, 114, 262-274. https://doi.org/10.1016/j.bbi.2023.08.010
• Mental health and diabetes risk Huang, T., Zhu, Y., Shutta, K. H., Balasubramanian, R., Zeleznik, O. A., Rexrode, K. M., … Kubzansky, L.D., & Hankinson, S. E. (2023). A Plasma Metabolite Score Related to Psychological Distress and Diabetes Risk: A Nested Case-control Study in US Women. The Journal of Clinical Endocrinology & Metabolism, dgad731. https://doi.org/10.1210/clinem/dgad731

SpiderLearner

SpiderLearner, our ensemble method for estimating Gaussian graphical models, has been published in Statistics in Medicine! Our SpiderLearner Quickstart Guide will get you up and running with the corresponding R package ensembleGGM in just a few minutes. We are always looking for feedback via the ensembleGGM Github repository! Citation: Shutta, K. H., Balzer, L. B., Scholtens, D. M., & Balasubramanian, R. (2023). SpiderLearner: An ensemble approach to Gaussian graphical model estimation. Statistics in Medicine, 42(13), 2116-2133. https://doi.org/10.1002/sim.9714

Learning about GGMs

Our tutorial on Gaussian graphical models is a great starting point for applied statisticians to get up and running with GGM analyses. You can find a stand-alone RMarkdown document with the tutorial code here. Citation: Shutta, K.H., De Vito, R., Scholtens, D.M. and Balasubramanian, R., 2022. Gaussian graphical models with applications to omics analyses. Statistics in medicine. https://doi.org/10.1002/sim.9546

Factor analysis for network models of multi-study data

Check out our preprint on graphical modeling of multi-study data! Our method builds on the multi-study factor analysis (MSFA) method of Roberta De Vito and her colleagues. We use latent variables to estimate shared and condition-specific Gaussian graphical models. Citation: Shutta, K.H., Scholtens, D.M., Lowe Jr., W.L., Balasubramanian, R., and De Vito, R., 2022 arXiv, https://doi.org/10.48550/arXiv.2210.12837