Computational strategies for incompleteness and heterogeneity in multi-omic data
Jingwen Yan
Large-scale multi-omic data collected from multiple projects is often heterogeneous and incomplete, impeding data integration for joint analysis. To address these issues, we developed a model for joint network module detection and feature selection that identifies multi-omic subnetworks as disease biomarkers. Furthermore, we created a sparse association model to select associated features between heterogeneous -omics layers. These approaches eliminate the need for data exclusion and enhance disease biomarker discovery.
Gene co-expression underlying the connectomic alterations in Alzheimer’s disease
Jingwen Yan
Gene co-expression underlying the connectomic alterations in Alzheimer’s disease aims to investigate the relationship between gene expression patterns and changes in brain networks in Alzheimer's disease (AD). By analyzing brain-wide transcriptome data, we seek to identify genes crucial to the connection between co-expression networks and AD-altered networks.
Mapping RNA protein interaction networks in the human genome
Sarath Janga
Increasing number of RNA-binding proteins (RBPs) have been implicated in human diseases, but many RBPs and their cognate motifs are still unknown. This project aims to develop robust computational techniques for predicting RNA-binding protein (RBP) motifs. By integrating expression associations, sequence information, and RBP-centric features, these techniques will facilitate the construction of tissue-specific RBP-RNA networks using genome-wide data from protein protection assays (POP-seq).
Graph-based Spatial Transcriptomics Computational Methods in Kidney Diseases
Juexin Wang
Chronic Kidney Disease (CKD) and Acute Kidney Injury (AKI) are intersecting diseases affecting a significant portion of the global population. Spatial transcriptomics technology provides insights into cell type heterogeneity, but identifying colocalizing cell types and understanding fibrosis, immune interactions, and epithelial repair in kidney spatial transcriptomics data present computational challenges. In this project, we use AI-based spatial transcriptomics methods with multi-omics cell atlas data to understand the pathogenesis of kidney disease.
Dimension-agnostic and granularity-based spatially variable genes identification
Juexin Wang
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. We developed BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. We will apply BSP in substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
