Quantifying Tumor Heterogeneity Using Single-Cell and Spatial Sequencing

Seminar Series

March 23, 2022 11:00 a.m. - 12:00 p.m. ET

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During this webinar, Dr. Ben Raphael will: describe computational approaches to quantify tumor heterogeneity and reconstruct tumor evolution using data from single-cell DNA and spatial RNA sequencing technologies. explain how tumors are heterogeneous mixtures of normal and cancerous cells with distinct genetic/transcriptional profiles. present two approaches to analyze the spatial organization of tumors and tissues using spatial transcriptomics, a technology that produces paired measurements of RNA expression and 2D spatial coordinates at thousands of locations from a tissue slice. demonstrate the advantages of modeling spatial correlations when analyzing spatial transcriptomics data from both normal and cancer tissues. The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.

During this webinar, Dr. Ben Raphael will:

describe computational approaches to quantify tumor heterogeneity and reconstruct tumor evolution using data from single-cell DNA and spatial RNA sequencing technologies.
explain how tumors are heterogeneous mixtures of normal and cancerous cells with distinct genetic/transcriptional profiles.
present two approaches to analyze the spatial organization of tumors and tissues using spatial transcriptomics, a technology that produces paired measurements of RNA expression and 2D spatial coordinates at thousands of locations from a tissue slice.
demonstrate the advantages of modeling spatial correlations when analyzing spatial transcriptomics data from both normal and cancer tissues.

The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.

Ben Raphael, Ph.D.

Dr. Raphael is a professor at Princeton University where he develops algorithms and mathematical models for addressing biological problems. His major areas of interest include computational cancer genomics, human structural variation, and comparative genomics.