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 single-cell 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
- Reference-free cell type deconvolution of pixel-resolution spatially resolved transcriptomics data on 29 April 2022
- Single cell analysis reveals immune dysfunction from the earliest stages of CLL that can be reversed by ibrutinib on 12 January 2022
- VeloViz - RNA-velocity informed embeddings for visualizing cellular trajectories on 28 September 2021
- Computational challenges and opportunities in spatially resolved transcriptomic data analysis on 06 September 2021
- Rewiring of human neurodevelopmental gene regulatory programs by human accelerated regions on 03 September 2021
Latest News
- We welcome Mayling Chen to the lab! Welcome Mayling! on 21 June 2022
- Manju Anant successfully completes her rotations and will join the lab for her PhD! Glad to have you on the team Manju! on 20 June 2022
- Lyla gives an invited talk for the virtual forum on Understanding Cells in Context - Spatial Transcriptomics organized by The Jackson Laboratory. on 23 May 2022
- Arpan Sahoo graduates from Hopkins! Thanks for being a part of the team and best of luck in your future pursuits! on 20 May 2022
- Dr. Fan co-chairs the Single Cell and Developmental Biology session at the Biology of Genomes conference at Cold Spring Harbor. on 12 May 2022
Latest Blog Posts
- Coloring SVGs in R on 17 June 2022
- Deconvolution vs Clustering Analysis for Multi-cellular Pixel-Resolution Spatially Resolved Transcriptomics Data on 03 May 2022
- Exploring UMAP parameters in visualizing single-cell spatially resolved transcriptomics data on 19 January 2022
- Animating RNA velocity with moving arrows on 15 October 2021
- A tale of two cell populations: integrating RNA velocity information in single cell transcriptomic data visualization with VeloViz on 06 October 2021
- Story-telling with Data Visualization on 12 August 2021
- Complementing single-cell clustering analysis with MERINGUE spatial analysis on 21 June 2021
- Randomly Generating Music with R on 19 April 2021
- Animating the Cell Cycle on 28 December 2020
- Using R To Find The Missing Faculty on 30 November 2020