|Our lab focuses on developing computational methods to discover heterogeneous transcriptional regulatory mechanisms from single-cell sequencing data. Our efforts are split between inferring cell-type-specific regulatory signals and constructing reliable gene regulatory networks via the integration of single-cell multi-omics. We are also going into the development of novel computational algorithms for spatial transcriptomic data analysis in immuno-oncology and neuron science. https://bmbl.bmi.osumc.edu|
|We are developing enabling tools and databases, using metagenomic and metatranscriptomic data, to elucidate microbial systems and their interactions with human diseases. Our goal is to deliver reproducible and integrated analyses of complex gut microbiome data, detect functional and taxonomic abundances (biomarkers) in a microbial community, and infer significant associations between clinical measurements of human diseases and transformed microbial biomarkers.|
[Aug-4-2020] The manuscript “scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses.” has been released in BioRxiv!
[Jul-23-2020] The grant Administrative Supplements Equipment (R01GM131399-03S1) is funded for equipment purchases. Congratulations!
[Jun-23-2020] Jennifer Xu, a summer intern student in 2019, has started a career in the Duke Clinical Research Institute. Congratulations!
[Jun-19-2020] Patrick J. Lawrence, a graduate student from Biomedical Sciences Graduate Program, OSU, has joined the lab as a rotating student. Welcome!
[May-21-2020] The grant “Deep Transfer Learning of Drug Sensitivity by Integrating Bulk and Single-cell RNA-Seq data” has been selected for funding the CCTS Pilot Translational & Clinical Studies Program of the CCTS!
[May-18-2020] The manuscript “IRIS3: Integrated Cell-type-specific Regulon Inference Server from Single-cell RNA-Seq” has been officially published by Nucleic Acids Research!