RESEARCH
Cutting-edge single-cell RNA-sequencing and spatial -omic technologies are transforming the way we study complex biological systems. By enabling us to make high-throughput molecular measurements of cells, these technologies allow us to study cellular states across time and space, or under perturbation. However, new computational models and tools are key for unlocking biological insight from these large and highly complex datasets.
Our research focuses on developing new representation learning methods for modeling and analyzing biological data, particularly for single-cell RNA-sequencing and spatial -omics. Our goal is to automatically learn meaningful representations of and extract useful features from these high-dimensional datasets. We then use these models to make predictions about cellular function or behavior, or interrogate them to better understand the underlying biology.
Some ongoing projects include: (i) Development of multimodal methods for joint representations of H&E imaging and spatial -omic molecular data, as well as (ii) Development of a comparative analysis framework for spatial biomarker discovery.
Selected Publications
- Joanito, I., Wirapati, P., Zhao, N., Nawaz, Z., Yeo, G., Lee, F., Eng, C. L. P., Macalinao, D. C., Kahraman, M., Srinivasan, H., Lakshmanan, V., Verbandt, S., Tsantoulis, P., Gunn, N., Venkatesh, P. N., Poh, Z. W., Nahar, R., Oh, H. L. J., Loo, J. M., . . . Tan, I. B. (2022). Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer. Nature Genetics, 54(7), 963–975. https://doi.org/ 10.1038/s41588-022-01100-4
- Yeo, G. H. T.*, Saksena, S*. D., & Gifford, D. K. (2021). Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions. Nature Communications, 12(1), 3222. https://doi.org/10.1038/s41467-021-23518-w
- Yeo, G. H. T., Juez, O., Chen, Q., Banerjee, B., Chu, L., Shen, M. W., Sabry, M., Logister, I., Sherwood, R. I., & Gifford, D. K. (2021). Detection of gene cis-regulatory element perturbations in single-cell transcriptomes. PLoS Computational Biology, 17(3), e1008789.https://doi.org/10.1371/journal.pcbi.1008789
- Yeo, G. H. T., Lin, L., Qi, C. Y., Cha, M., Gifford, D. K., & Sherwood, R. I. (2020). A Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiation. Cell Stem Cell, 26(6), 938–950.e6.https://doi.org/10.1016/j.stem.2020.04.020
- Wilm, A., Aw, P. P. K., Bertrand, D., Yeo, G. H. T., Ong, S. H., Wong, C. H., Khor, C. C., Petric, R., Hibberd, M. L., & Nagarajan, N. (2012). LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets. Nucleic Acids Research, 40(22), 11189–11201.https://doi.org/10.1093/nar/gks918