We are a bioinformatics research lab in the Department of Biomedical Engineering at Johns Hopkins University. We are also a part of the Center for Computational Biology and Department of Computer Science.
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.
- 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. bioRxiv. 2021. doi: 10.1101/2021.01.28.425293
- 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. 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
- 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
- Reference-free cell-type deconvolution of pixel-resolution spatially resolved transcriptomics data on 16 June 2021
- Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma on 03 June 2021
- Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with nonuniform cellular densities on 25 May 2021
- We welcome Jose Delgado to the lab as part of the Introduction to Computing Research (ICR) program. Welcome Jose! on 21 September 2021
- We welcome Manjari (Manju) Anant to the lab! Welcome Manju! on 14 September 2021
- Brendan presents his work at the University of Sydney Statistical Bioinformatics Seminar series. on 13 September 2021
- Dr. Fan talks with JHU graduate students and post-docs about Research Intensive [STEM] Positions at the PHutures Academic Job Search Series. on 02 September 2021
- The JEFworks lab receives support from the NIH/NIGMS Maximizing Investigators' Research Award (R35 MIRA). on 01 September 2021
Latest Blog Posts
- 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
- Using scVelo in R using Reticulate on 25 August 2020
- A Guide to Responding to Scientific Peer Review on 17 June 2020
- Quickly Creating Pseudobulks on 06 April 2020
- A Guide to Scientific Peer Review on 23 March 2020
- Ten PhD Transition Tips for the Biological Sciences on 23 January 2020