The long-term goals of our lab are to develop computational methods to overcome the challenges for analyzing, interpreting, and integrating various genomic-scale data types. and to partner with experimental biologists to uncover the fundamental principles in gene regulatory mechanisms and to use some knowledge to develop effective treatment for various human diseases.  Our current research is focused on the following areas.

(1) Single-cell multi-omics analysis. How many distinct cell types are there in a human and, for comparison, in a model organism? How do individual cells interact with each other and work coordinately to maintain the function and structure of a tissue or organ? The rapid development of single-cell multi-omic technologies has provided a great opportunity to address this fundamental question in depth. We develop computational methods to systematically characterize the heterogeneity, interaction, and dynamics of cellular states by analyzing single-cell RNAseq data (Marco et al. 2014; Jiang et al. 2016; Tsoucas et al. 2019) and apply these methods to characterize the cell state changes in diseases such as cancer (Luoma et al. 2020). Recently, we have extended our research to single-cell multi-omic analyses.

(2) Spatial transcriptomics/multi-omics. While powerful, single-cell RNAseq analysis provides little information about the spatial structure within a tissue/organ. Because cells do not live in isolation but constantly interact with their neighboring cells and extra-cellular environment, it is crucial to map out the spatial distribution of cell types in their native environment. This can be done by the recently developed spatial transcriptomic technologies. We develop computational methods to comprehensively analyze spatial data and user-friendly, generally applicable software packages that enable experimental biologists to analyze their own data without the need to write their own programs (Zhu et al. 2018; Dong and Yuan 2021; Dries et al. 2021). See the Giotto project for details. In addition, our bioinformatic expertise has contributed to the development of seqFISH+ (Eng et al. 2019) and DNA seqFISH+ technologies (Takei et al. 2021a; Takei et al. 2021b).

(3) Gene regulatory network analysis. Gene expression is regulated by the concerted actions of many factors such as transcription factors and chromatin regulators. Furthermore, the maintenance and function of an tissue/organ are mediated by the tissue microenvironment and cell-cell interactions interactions. Our long-term goal is to integrate spatial and single-cell multi-omic information to elucidate the gene regulation and cell-cell interaction mechanism. We have developed computational methods to analyze such data as well as integrative approaches to build gene regulatory networks (Pinello et al. 2014; Pinello et al. 2016; Marco et al. 2017; Huang et al. 2018; Suo et al. 2018; Zhu et al. 2019) by integrating genomic, transcriptomic, and epigenomic data.  With the rapid development of new technologies, we will continue to develop new methods to address the challenges and opportunities.