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
- Alignment of spatial transcriptomics data using diffeomorphic metric mapping on 12 April 2023
- A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming on 29 March 2023
- 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
Latest News
- Feiyang Huang graduates from Hopkins and will be heading to New York City to pursue his PhD at the Tri-Institutional Computational Biology and Medicine program! Thanks for being a part of the team and best of luck in your future pursuits! on 19 May 2023
- Dr. Fan shares her views on why it's worth making computational methods easy to use in a Nature Careers column. on 27 April 2023
- Dr. Fan gives an invited talk at BME Seminar Series at Columbia University. on 21 April 2023
- Brendan Miller transitions to a new role as a Spatial Transcriptomics Computational Biologist at Merck! Thanks for being a part of the team and best of luck in your future pursuits! on 13 April 2023
- Prospective BME PhD student Ruiyang visits the JEFworks Lab! on 31 March 2023
Latest Blog Posts
- Impact of normalizing spatial transcriptomics data in dimensionality reduction and clustering versus deconvolution analysis with STdeconvolve on 04 May 2023
- Aligning Spatial Transcriptomics Data With Stalign on 16 April 2023
- 3D animation of the brain in R on 08 November 2022
- Ethical Challenges in Biomedical Engineering - Data Collection, Analysis, and Interpretation on 15 October 2022
- I use R to (try to) figure out the cost of medical procedures by analyzing insurance data from the Transparency in Coverage Final Rule on 12 September 2022
- Annotating STdeconvolve Cell-Types with ASCT+B Tables on 30 August 2022
- Deconvolution vs Clustering Analysis: An exploration via simulation on 11 July 2022
- 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
