November ABC Seminar: Gennady Gorin, PhD – CalTech

November ABC Seminar: Gennady Gorin, PhD – CalTech

Stochastic foundations for single-cell RNA sequencing

Single-cell RNA sequencing, which quantifies cell transcriptomes, has seen widespread adoption, accompanied by a proliferation of analytic methods. However, there has been relatively little systematic investigation of its best practices and their underlying assumptions, leading to challenges and discrepancies in analysis. I motivate a set of generic, principled strategies for modeling the biological and technical stochasticity in sequencing experiments, and use case studies to illustrate their prospects for the discovery and interpretation of biophysical kinetics.

Research links:


Dr. Gennady Gorin is a chemical engineer working at the exciting intersection of bioinformatics, stochastic biophysics, and statistics. He completed his Ph.D. with Lior Pachter at the California Institute of Technology, adapting theory from fluorescence transcriptomics to the unique features of single-cell RNA sequencing. Prior, he completed a B.S./B.A. at Rice University and performed transcriptional modeling research in the Golding laboratory at Baylor College of Medicine. Gennady is transitioning to industrial bioinformatics, and excited about the prospects for rigorous, physics-informed methods in method development.

All Welcome! Note this event will take place on Zoom.

Date: Monday November 20, 2023
Time: 4:00-5:00PM

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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:

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.


Jan. 30, 2023: Lambda Moses, California Institute of Technology

“From geospatial to spatial -omics with SpatialFeatureExperiment and Voyager”;; To bring more of the geospatial tradition to spatial -omics, we developed SpatialFeatureExperiment (SFE), which extends the existing Bioconductor data structures SingleCellExperiment (SCE) and SpatialExperiment (SPE) with Simple Features to represent the geometries of Visium spots and cell segmentation and perform geometric operations. We developed the Voyager package that performs exploratory spatial data analysis (ESDA) on SFE objects.

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

“Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection,”; 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.