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 multi-omic sequencing and imaging data.
While heterogeneity within cellular systems has long been widely recognized, only recently have technological advances enabled measurements to be made on a single cell level. We develop machine learning and other statistical approaches as open-source computational software to analyze such high-throughput single-cell resolution multi-omic and imaging data in order to identify and characterize varying aspects of heterogeneity (transcriptomic, epigenomic, spatial/contextual) and their interplay.
- Lyla Atta, Jean Fan^. VeloViz: RNA-velocity informed 2D embeddings for visualizing cellular trajectories. BioRxiv. 2021. doi: 10.1101/2021.01.28.425293
- 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
- Jean Fan, Neeraj Salathia, Rui Liu, Gwendolyn E Kaeser, Yun C Yung, et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nature Methods. 2016. doi: 10.1038/nmeth.3734
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 Slowkikowski, 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 2D embeddings for visualizing cellular trajectories on 29 January 2021
- Spatial organization of the transcriptome in individual neurons on 08 December 2020
- Single-cell transcriptomics in cancer - computational challenges and opportunities on 15 September 2020
- Fast, sensitive and accurate integration of single-cell data with Harmony on 19 November 2019
- Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression on 09 September 2019
- Dr. Fan gives an invited seminar and spends the day virtually with the SKCCC Research Program in Quantitative Sciences at JHMI on 18 February 2021
- Lyla presents her work on VeloViz at the JHU Genomics Collective meeting on 10 February 2021
- Check out our preprint led by JEFworks lab team member Lyla Atta on creating RNA-velocity informed 2D embeddings for visualizing cellular trajectories! on 29 January 2021
- Happy holidays from the JEFworks Lab! on 18 December 2020
- Check out our preprint led by Guiping Wang on atlasing the spatial organization of RNAs inside individual cultured neurons using MERFISH on 08 December 2020
Latest Blog Posts
- 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
- RNA Velocity Analysis (In Situ) - Tutorial and Tips on 14 January 2020
- How to write an abstract on 24 September 2019
- Figure style faux pas on 19 July 2019