Spatial organization at both the subcellular-level within cells as well as the cellular-level within tissues play important roles in regulating cell identity and function. Recent technological advances have enabled high-throughput spatially resolved transcriptomic profiling at single-molecule and near-single-cell resolution. We develop machine learning and other statistical approaches as open-source computational software to take advantage of this new spatial information in deriving biological insights regarding how spatial organization plays a role in both healthy and diseased settings.

• Lyla Atta, Jean Fan^. Computational challenges and opportunities in spatially resolved transcriptomic data analysis. Nature Communications. September 6, 2021. doi.org/10.1038/s41467-021-25557-9
• Brendan F Miller, Lyla Atta, Arpan Sahoo, Feiyang Huang, Jean Fan^. Reference-free cell-type deconvolution of pixel-resolution spatially resolved transcriptomics data. bioRxiv. 2021. doi: 10.1101/2021.06.15.448381
• Lyla Atta, Arpan Sahoo, Jean Fan^. VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories. Bioinformatics. 2021. https://doi.org/10.1093/bioinformatics/btab653
• Brendan F Miller, Dhananjay Bambah-Mukku, Catherine Dulac, Xiaowei Zhuang, Jean Fan^. Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities. Genome Research. 2021. doi:10.1101/gr.271288.120
• Chenglong Xia*, Jean Fan*, George Emanuel*, Junjie Hao, and Xiaowei Zhuang. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. PNAS. 2019. doi:10.1073/pnas.1912459116

Advancements in high-throughput sequencing and imaging technologies have uncovered tremendous genetic, epigenetic, transcriptional, and spatial heterogeneity in various cancers but their impact on clinical outcomes is not well understood. We establish close collaborations with clinical collaborators to develop and apply bioinformatics methods that contribute to a more complete understanding of how cellular heterogeneity impacts tumor progression, therapeutic resistance, and ultimately clinical prognosis. We are particularly interested in pediatric gliomas.

• Jean Fan^, Kamil Slowikowski, Fan Zhang. Single-cell transcriptomics in cancer - computational challenges and opportunities. Nature Experimental and Molecular Medicine. Sept 15, 2020, doi.org:10.1038/s12276-020-0422-0
• Jean Fan*, Hae-Ock Lee*, Soohyun Lee, Da-eun Ryu, Semin Lee, et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq. Genome Research. 2018. doi:10.1101/gr.228080.117
• Lili Wang*, Jean Fan*, Joshua M. Francis, George Georghiou, Sarah Hergert, et al. Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia. Genome Research. 2017. doi:10.1101/gr.217331.116