Goeva Lab

Themes

Detecting subtle and heterogeneous biological signals

Many disease-relevant effects are present only in subsets of cells or are masked by dominant sources of variation, making them difficult to detect with standard approaches. We develop statistical and machine learning methods to identify weak, sparse, and heterogeneous signals in high-dimensional biological data.


Interpretable models for single-cell and spatial genomics

We design interpretable computational frameworks that integrate prior biological knowledge with machine learning to model cellular identity, state, and spatial organization. A central goal is to produce models whose outputs can be directly linked to biological mechanisms and validated experimentally.


Modeling cellular interactions and tissue organization

We develop methods to understand how cells interact within tissues, including frameworks for studying cell–cell communication and the emergence of coordinated behavior across cell populations. We are particularly interested in moving beyond cell-centric views toward modeling interactions as fundamental units of biological systems.


Statistical frameworks for robust biological discovery

We study how data structure, noise, and experimental design shape what can be reliably detected in biological systems. Our work emphasizes uncertainty, identifiability, and detectability, with the goal of improving the reliability and reproducibility of discoveries in modern genomics.

Source availability

Source code for all lab projects (including this website) is hosted on GitHub, under the goeva-lab organization!

Selected Work

For a complete list of published works, please see Dr. Goeva’s Google Scholar.

Preprints

Peer-reviewed Publications

  1. Campbell, K. R., & Goeva, A. (2025). Cells Keep Diverse Company in Diseased Tissues. Cancer Research, 85(13), 2351–2352. https://doi.org/10.1158/0008-5472.can-25-2070
  2. Goeva, A., Dolan, M.-J., Luu, J., Garcia, E., Boiarsky, R., Gupta, R. M., & Macosko, E. (2024). HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-53666-8
  3. Yang, A., Chidiac, R., Russo, E., Steenland, H., Pauli, Q., Bonin, R., Blazer, L. L., Adams, J. J., Sidhu, S. S., Goeva, A., Salahpour, A., & Angers, S. (2024). Exploiting spatiotemporal regulation of FZD5 during neural patterning for efficient ventral midbrain specification. Development, 151(5). https://doi.org/10.1242/dev.202545
  4. Dolan, M.-J., Therrien, M., Jereb, S., Kamath, T., Gazestani, V., Atkeson, T., Marsh, S. E., Goeva, A., Lojek, N. M., Murphy, S., White, C. M., Joung, J., Liu, B., Limone, F., Eggan, K., Hacohen, N., Bernstein, B. E., Glass, C. K., Leinonen, V., … Stevens, B. (2023). Exposure of iPSC-derived human microglia to brain substrates enables the generation and manipulation of diverse transcriptional states in vitro. Nature Immunology, 24(8), 1382–1390. https://doi.org/10.1038/s41590-023-01558-2
  5. Adler, M., Moriel, N., Goeva, A., Avraham-Davidi, I., Mages, S., Adams, T. S., Kaminski, N., Macosko, E. Z., Regev, A., Medzhitov, R., & Nitzan, M. (2023). Emergence of division of labor in tissues through cell interactions and spatial cues. Cell Reports, 42(5), 112412. https://doi.org/10.1016/j.celrep.2023.112412
  6. Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2021). Robust decomposition of cell type mixtures in spatial transcriptomics. Nature Biotechnology, 40(4), 517–526. https://doi.org/10.1038/s41587-021-00830-w
  7. Goeva, A., Stoudt, S., & Trisovic, A. (2020). Toward Reproducible and Extensible Research: from Values to Action. Harvard Data Science Review, 2(4). https://doi.org/10.1162/99608f92.1cc3d72a
  8. Goeva, A., Lam, H., Qian, H., & Zhang, B. (2019). Optimization-Based Calibration of Simulation Input Models. Operations Research, 1362–1382. https://doi.org/10.1287/opre.2018.1801
  9. Kramer, J., Helfrich, C., Schwartz, A., Samuel, P., Kolaczyk, E., & Goeva, A. (2019). Outcomes of an Environmental-Focused, Problem-Solving Intervention for Transition-Age Youth: Project TEAM. American Journal of Occupational Therapy, 73(4_Supplement_1), 7311515321p1. https://doi.org/10.5014/ajot.2019.73s1-rp101c
  10. Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019). Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science, 363(6434), 1463–1467. https://doi.org/10.1126/science.aaw1219
  11. Kunin, D., Bloom, J., Goeva, A., & Seed, C. (2019). Loss Landscapes of Regularized Linear Autoencoders. In K. Chaudhuri & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning: Vol. 97. Proceedings of the 36th International Conference on Machine Learning. https://proceedings.mlr.press/v97/kunin19a.html
  12. Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., Rivera, H. de, Bien, E., Baum, M., Bortolin, L., Wang, S., Goeva, A., Nemesh, J., Kamitaki, N., Brumbaugh, S., Kulp, D., & McCarroll, S. A. (2018). Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain. Cell, 174(4), 1015–1030.e16. https://doi.org/10.1016/j.cell.2018.07.028
  13. Kramer, J. M., Helfrich, C., Levin, M., Hwang, I., Samuel, P. S., Carrellas, A., Schwartz, A. E., Goeva, A., & Kolaczyk, E. D. (2018). Initial evaluation of the effects of an environmental‐focused problem‐solving intervention for transition‐age young people with developmental disabilities: Project TEAM. Developmental Medicine & Child Neurology, 60(8), 801–809. https://doi.org/10.1111/dmcn.13715
  14. Goeva, A., Lam, H., & Zhang, B. (2014). Reconstructing input models via simulation optimization. Proceedings of the Winter Simulation Conference 2014, 698–709. https://doi.org/10.1109/wsc.2014.7019933

Invited Discussions, Commentaries, and Explainers

  1. Blumenthal, K., Goeva, A., Stoudt, S., & Trisovic, A. (2021). Why Do We Plot Data?. Harvard Data Science Review.
  2. Goeva, A., Jones, P., Stoudt, S., & Trisovic, A. (2021). Recipes for Connector Courses From the Early-Career Board Kitchen. Harvard Data Science Review. https://doi.org/10.1162/99608f92.f7c60746
  3. Frost, S., Goeva, A., Pombra, J., Seaton, W., Stoudt, S., Trisovic, A., Wang, C., & Zucker, C. (2021). Kaleidoscopic Perspectives on Practicum-Based Data Science Education. Harvard Data Science Review. https://doi.org/10.1162/99608f92.ba6336bc
  4. Goeva, A., & Stoudt, S. (2021). COVID-19 Case Fatality Rate Bias Visual Explainer. Harvard Data Science Review, Special Issue 1.
  5. Frost, S., Goeva, A., Seaton, W., Stoudt, S., & Trisovic, A. (2020). Early-Career View on Data Science Challenges: Responsibility, Rigor, and Accessibility. Harvard Data Science Review. https://doi.org/10.1162/99608f92.b211611a
  6. Goeva, A., & Kolaczyk, E. D. (2016). Comment. Journal of the American Statistical Association, 111(516), 1405–1408. https://doi.org/10.1080/01621459.2016.1245072