October ABC Seminar: Weiruo Zhang, PhD, Stanford University

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

Spatial biology is a new frontier that has become accessible through advances in spatial profiling technologies, such as multiplexed in situ imaging spatial proteomics, which can provide single-cell resolution up to 60 markers. In this talk, I will introduce a computational analysis pipeline that performs integrative analysis of spatial proteomics and single-cell RNA sequencing to identify clinically-relevant cellular interactions. The pipeline features (1) CELESTA, an unsupervised machine learning method for cell type identification in multiplexed spatial proteomics data; (2) a geospatial statistical method to identify cell-cell colocalizations; and (3) an integrative coupling of spatial proteomics and single-cell RNA sequencing data that identified cell-cell crosstalk associated with lymph node metastasis in head and neck cancer which we have validated through mouse model studies.

Research link:
https://profiles.stanford.edu/weiruo-zhang

Dr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang received her M.S. and Ph.D. in Electrical Engineering, both from Stanford University, with a focus on bioinformatics and developing computational algorithms for metabolomics data analysis. Her current research at Stanford primarily focuses on developing and implementing computational methods to integrate and analyze single-cell and spatial multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. Her research aims to apply quantitative approaches that bridge multi-omics, imaging, machine learning, and artificial intelligence to decipher biology for cancer progression and guide treatment responses.

 

Weiruo Zhang, PhD, Stanford University-“Integrative spatial-omics analysis of cellular architecture mediating lymph node metastasis in head and neck cancer”

Spatial biology is a new frontier that has become accessible through advances in spatial profiling technologies, such as multiplexed in situ imaging spatial proteomics, which can provide single-cell resolution up to 60 markers. In this talk, I will introduce a computational analysis pipeline that performs integrative analysis of spatial proteomics and single-cell RNA sequencing to identify clinically-relevant cellular interactions. The pipeline features (1) CELESTA, an unsupervised machine learning method for cell type identification in multiplexed spatial proteomics data; (2) a geospatial statistical method to identify cell-cell colocalizations; and (3) an integrative coupling of spatial proteomics and single-cell RNA sequencing data that identified cell-cell crosstalk associated with lymph node metastasis in head and neck cancer which we have validated through mouse model studies.

 

Research link:
https://profiles.stanford.edu/weiruo-zhang

Dr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang received her M.S. and Ph.D. in Electrical Engineering, both from Stanford University, with a focus on bioinformatics and developing computational algorithms for metabolomics data analysis. Her current research at Stanford primarily focuses on developing and implementing computational methods to integrate and analyze single-cell and spatial multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. Her research aims to apply quantitative approaches that bridge multi-omics, imaging, machine learning, and artificial intelligence to decipher biology for cancer progression and guide treatment responses.

 

Tal Korem PhD, Columbia – “Contamination and genomic variability in microbiome data”

Microbiome studies hold tremendous potential along with substantial computational challenges. I will present two computational approaches for microbiome data analysis. First, I will present SCRuB, a new method for in silico removal of contamination from microbiome data. We show that modeling the taxonomic composition of contamination sources, rather than trying to infer whether specific taxa are categorically contaminant, allows for more robust decontamination and improves downstream phenotypic prediction. Second, I will present copangraph, a new graph-based approach for representing genomic variability within and across microbiomes. We demonstrate a hybrid co-assembly approach that yields high-quality representation of the microbiome.

Tal Korem’s research program focuses on the development of computational methods that identify and interpret host-microbiome interactions in various clinical setting. The ultimate goal of his research is to translate microbiome findings to clinical care, with microbiome-based therapeutics and microbiome-informed clinical practices. He has developed several approaches for microbiome data analysis, inferring microbial growth rates, structural variants, and microbiome-metabolite interactions; and has applied these methods in diverse clinical and biological investigations, most notably for personalization of dietary treatment and predicting preterm birth. He is a member of Columbia’s Program for Mathematical Genomics (PMG), an Assistant Professor in the Departments of Systems Biology and Obstetrics & Gynecology, and was previously a CIFAR-Azrieli global scholar by the Canadian Institute for Advanced Research.

All Welcome! Note this event will take place on Zoom: https://partners.zoom.us/j/84799112738

To be added to the ABC seminar email list, please email tarnoldmages@bwh.harvard.edu

Nov. 14, 2022: Martin Jankowiak, PhD, Broad Institute

“Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection,” https://partners.zoom.us/j/86162386947; We develop Bayesian Viral Allele Selection (BVAS), a method that leverages the millions of SARS-CoV-2 viral genomes that have been sequenced across the globe to identify mutations linked to increased viral fitness.