Hosted by Cardiff University's School of Medicine

Prof. 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.

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Bio: Rafael Irizarry is 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. He received a Ph.D. in Statistics in 1998 from the University of California, Berkeley. Since 1999, Rafael Irizarry's work has focused on Genomics and Computational Biology problems. In particular, he has worked on the analysis and signal processing of microarray, next-generation sequencing, and genomic data. He is currently interested in leveraging his knowledge in translational work, e.g. developing diagnostic tools and discovering biomarkers.

Professor Irizarry also develops open source software implementing his statistical methodology. His software tools are widely used and he is one of the leaders and founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. Prof. Irizarry is the developer and instructor for the online Data Analysis for Life Sciences course on the Harvard University edX platform; this course enrolls over 30,000 students per year.

He was awarded the Benjamin Franklin Award in Bioinformatics for his promotion of free and open-access materials and methods in the life sciences in 2017. He was also elected as a Fellow of the International Society for Computational Biology in 2020.