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Bioinformatics Training and Education Program

Statistical Methods for Single-Cell RNA-Seq Analysis and Spatial Transcriptomics

Distinguished Speakers Seminar Series

Statistical Methods for Single-Cell RNA-Seq Analysis and Spatial Transcriptomics

 When: Jun. 20th, 2024 1:00 pm - 2:00 pm

Seminar Series Details:

Presented By:
Rafael Irizarry, Ph.D. (Harvard)
Where:
Online Webinar
Organized By:
BTEP
Rafael Irizarry, Ph.D. (Harvard)

About Rafael Irizarry, Ph.D. (Harvard)

Rafael Irizarry is a Professor of Applied Statistics at Harvard and the Dana-Farber Cancer Institute. He was recently named Chair of the Department of Data Sciences at the Dana-Farber Cancer Institute and is a Professor of Biostatistics at Harvard T.H. Chan School of Public Health.

Rafael Irizarry received his Bachelor's in Mathematics in 1993 from the University of Puerto Rico and went on to receive a Ph.D. in Statistics in 1998 from the University of California, Berkeley. His thesis work was on Statistical Models for Music Sound Signals. He joined the faculty of the Department of Biostatistics in the Johns Hopkins Bloomberg School of Public Health in 1998 and was promoted to Professor in 2007. He is now Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute and a Professor of Biostatistics at Harvard School of Public Health. 

About this Class

Dr. Irizarry will share findings demonstrating limitations of current
workflows that are popular in single cell RNA-Seq data analysis.
Specifically, he will describe challenges and solutions to dimension
reduction, cell-type classification, and statistical significance
analysis of clustering. Dr. Irizarry will end the talk describing some of his
work related to spatial transcriptomics. Specifically, he will describe
approaches to cell type annotation that account for presence of
multiple cell-types represented in the measurements, a common
occurrence with technologies such as Visium and SlideSeq. He will
demonstrate how this approach facilitates the discovery of spatially
varying genes.
Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=m9dcd9ce21f4fa6b1a8e2d998a88c2c2b 
 
Meeting number:
2317 712 9095
Password:
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