Research
Spatial transcriptomics
Lulu Shang, Xiang Zhou
Nature Communications, 2022
Analyzing spatial transcriptomics data is computationally challenging, as the data are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially aware dimension reduction method, SpatialPCA, that can extract low dimensional representations of the spatial transcriptomics data with biological signal and preserved spatial correlation structure.
Lulu Shang*, Peijun Wu*, Xiang Zhou
Nature Communications, 2025
An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs), a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types.
Jiaqiang Zhu*, Lulu Shang*, Xiang Zhou
Genome Biology, 2023
Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated and evaluated in silico using simulated data. We present SRTsim, an SRT-specific simulator for scalable, reproducible and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns.
Integrating genome-wide association studies (GWAS) with single cell
Lulu Shang, Jennifer A Smith, Xiang Zhou
PLOS Genetics, 2020
Understanding the biological functions of identified single-nucleotide polymorphism (SNP) associations requires identifying disease/trait relevant tissues or cell types. We developed a network method, CoCoNet, to incorporate tissue-specific gene co-expression networks constructed from single cell RNA sequencing studies with genome-wide association studies (GWAS) data for trait-tissue inference.
Quantitative trait locus (QTL) analysis
Lulu Shang, Wei Zhao, Yi Zhe Wang, Zheng Li, Jerome J Choi, Minjung Kho, Thomas H Mosley, Sharon LR Kardia, Jennifer A Smith, Xiang Zhou
Nature Communications, 2023
Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.
Lulu Shang, Jennifer A Smith, Wei Zhao, Minjung Kho, Stephen T Turner, Thomas H Mosley, Sharon LR Kardia, Xiang Zhou
The American Journal of Human Genetics, 2020
Most existing expression quantitative trait locus (eQTL) mapping studies have been focused on individuals of European ancestry and are underrepresented in other populations including populations with African ancestry. We fill this critical knowledge gap by performing a large-scale in-depth eQTL mapping study on 1,032 African Americans (AA) and 801 European Americans (EA) in the GENOA cohort.