Motivation
Spatially resolved transcriptomics (SRT) was named the 2020 Method of the Year by Nature Methods. By pairing transcriptional data with spatial data to create maps of gene expression, it enables researchers to spatially localize and quantify gene expression in the form of mRNA transcripts within cells or tissues in their native state. With explosive popularity, it provides valuable insights into the biology of cells and tissues while retaining information about the spatial context. Our first three talks will highlight recent advances in statistical methods and applications of SRT.
Dr. Mingyao Li will present methods to integrate gene expression with histology to computationally reconstruct SRT data that cover the entire transcriptome with near-single-cell resolution. Through comprehensive analysis of diverse datasets generated from both diseased and normal tissues, she will show that their super-resolution gene prediction is accurate and useful for different applications in tissue architecture inference.
Dr. Xiang Zhou will present a computational method, IRIS (Integrative and Reference-Informed tissue Segmentation), that can characterize the spatial organization of complex tissues through accurate and efficient detection of spatial domains. IRIS is unique in leveraging single-cell RNA-seq data for reference-informed spatial domain detection, integrating multiple SRT tissue slices jointly while explicitly accounting for the correlation both within and across slices, and taking advantage of multiple algorithmic innovations for highly scalable computation.
Dr. Julia Wrobel will discuss utilizing spatial summary statistics to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, she will introduce an approach to model the nonlinear association between summary spatial functions and subject-level outcomes. They apply the proposed method to cancer data collected using multiplex immunohistochemistry (mIHC).
Besides spatial variation, there is huge heterogeneity across cell types in bulk tissue genomics data. To address this issue, Dr. Yun Li will introduce efficient computational deconvolution of bulk RNA-seq to reveal the cell-type specificity mechanism in Alzheimer’s disease.
All four talks will interconnect various research areas and inspire cross-area collaborations. They share similar statistical challenges and methodologies and promote biometric applications in biological and life sciences.
Proposed Speakers & Discussant
Mingyao Li, University of Pennsylvania (USA)
- Integrating spatial transcriptomics with histology to infer super-resolution tissue architecture
Wenjing Ma, University of Michigan (USA)
Julia Wrobel, Colorado School of Public Health (USA)
- Analysis of immune cell spatial clustering using functional data models
Yun Li, The University of North Carolina at Chapel Hill (USA)
- Efficient computational deconvolution of bulk RNA-seq reveals cell-type specificity mechanism in Alzheimer’s disease
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