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.
- 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
- Blue B Lake*, Song Chen*, Brandon C Sos*, Jean Fan*, Gwendolyn E Kaeser, et al. Integrative single-cell analysis by transcriptional and epigenetic states in human adult brain. Nature Biotechnology 2017. doi:10.1038/nbt.4038
- 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*, 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
- Lili Wang*, Angela N. Brooks*, Jean Fan*, Youzhong Wan*, Rutendo Gambe, et al. Transcriptomic characterization of SF3B1 mutation reveals its pleiotropic effects in chronic lymphocytic leukemia. Cancer Cell. Nov 3, 2016. doi: 10.1016/j.ccell.2016.10.005
Latest Publications
- 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
- A Murine Model of Chronic Lymphocytic Leukemia Based on B Cell-Restricted Expression of Sf3b1 Mutation and Atm Deletion on 11 February 2019
- RNA velocity of single cells on 08 August 2018
Latest News
- Catherine Lo successfully completes her lab internship and will be writing up her research results for the Regeneron Science Talent Search competition. Best of luck Catherine! on 09 November 2020
- Dr. Fan gives a virtual invited talks as part of the Cold Spring Harbor Asia conference, the JHU Symposium on Genomics and Bioinformatics, and the Kavli NDI Breakfast Club on 27 October 2020
- Dr. Fan gives a virtual invited talk as part of the Keck Seminar Series. on 02 October 2020
- Lyla Atta successfully completes her rotations and will official join the lab for her PhD! Glad to have you on the team Lyla! on 19 September 2020
- Our invited review on computational challenges and opportunities in single cell transcriptomics analysis in cancer is published in Nature Experimental and Molecular Medicine as part of their special Single Cell Genomics feature! on 15 September 2020
Latest Blog Posts
- 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
- Single-Cell RNA-seq Dimensionality Reduction with Deep Learning in R using Keras on 17 May 2019
- Automate testing of your R package using Travis CI, Codecov, and testthat on 17 February 2019