We are a bioinformatics research lab in the Center for Computational Biology and the Department of Biomedical Engineering at Johns Hopkins University. We are also affiliated with the Department of Computer Science, the Center for Imaging Science, the Kavli Neuroscience Discovery Institute, and more.
We develop methods for analyzing spatially resolved transcriptomic sequencing and imaging data.
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.
- Brendan F Miller, Feiyang Huang, Lyla Atta, Arpan Sahoo, Jean Fan^. Reference-free cell type deconvolution of pixel-resolution spatially resolved transcriptomics data. Nature Communications. 2022. doi:/10.1038/s41467-022-30033-z
- Lyla Atta, Arpan Sahoo, Jean Fan^. VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories. Bioinformatics. 2021. /doi:10.1093/bioinformatics/btab653
- Lyla Atta, Jean Fan^. Computational challenges and opportunities in spatially resolved transcriptomic data analysis. Nature Communications. 2021. doi:10.1038/s41467-021-25557-9
- 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
We apply these methods to better understand the impact of cellular heterogeneity on cancer pathogenesis and prognosis.
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. 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
Latest Publications
- SEraster - a rasterization preprocessing framework for scalable spatial omics data analysis on 05 February 2024
- STalign - alignment of spatial transcriptomics data using diffeomorphic metric mapping on 08 December 2023
- Soluble PD-L1 reprograms blood monocytes to prevent cerebral edema and facilitate recovery after ischemic stroke on 07 December 2023
- Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys on 28 November 2023
- Characterizing cell-type spatial relationships across length scales in spatially resolved omics data on 06 October 2023
Latest News
- JEFworks Lab members give guest lectures in the Genomic Data Visualizations class. on 01 March 2024
- The Johns Hopkins News reports on STalign. on 20 February 2024
- Caleb Hallinan successfully completes his rotations and will join the lab for his PhD! Glad to have you on the team Caleb! on 19 February 2024
- Lyla gives a talk at the Spatial Multi-Omics for Cancer Systems Biology workshop at Dana Farber. on 12 February 2024
- Kalen gives an interview about spatial transcriptomics and STalign for an episode of the podcast CompBio Cafe produced by the Black Women Comp Bio network. on 12 February 2024
Latest Blog Posts
- Spatial Transcriptomics Analysis Of Xenium Lymph Node on 24 March 2024
- Querying Google Scholar with Rvest on 18 March 2024
- Alignment of Xenium and Visium spatial transcriptomics data using STalign on 27 December 2023
- Aligning 10X Visium spatial transcriptomics datasets using STalign with Reticulate in R on 05 November 2023
- Aligning single-cell spatial transcriptomics datasets simulated with non-linear disortions on 20 August 2023