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.
- 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
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
- 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
- Multi Scale Diffeomorphic Metric Mapping of Spatial Transcriptomics Datasets on 25 June 2021
- Reference-free cell-type deconvolution of pixel-resolution spatially resolved transcriptomics data on 16 June 2021
- Dr. Fan gives an invited talk at the MIT Bioinformatics Seminar series. on 20 October 2021
- Lyla presents VeloViz at the JHU Symposium on Genomics and Bioinformatics 2021. on 14 October 2021
- Dr. Fan gives an invited talk at The SBP Medical Discovery Institute for the C3 Single Cell Space Force seminar series. on 04 October 2021
- Dr. Fan gives an invited talk at Next Generation Genomics 2021. on 27 September 2021
- We welcome Jose Delgado to the lab as part of the Introduction to Computing Research (ICR) program. Welcome Jose! on 21 September 2021
Latest Blog Posts
- 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
- 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