Abstract
Single cell pseudotime algorithms attempt to extract temporal information from cross-sectional molecular profiles of single cells. Whilst there are a plethora of algorithmic methods for single cell pseudotime estimation, our focus has been on the development of model-based probabilistic approaches using Bayesian inference. I shall talk about a suite of pseudotime methods that have been developed in my group and their application to single cell genomics and beyond.
Biography
Dr. Yau obtained his PhD in Statistics at The Queen's College, University of Oxford, in 2009. He was a Medical Research Council Research postdoctoral fellow in Biomedical Informatics up to 2012. He was then appointed as a Lecturer in Statistics at the Imperial College London. He launched the Genomic Medicine Group at the Welcome Trust Centre for Human Genetics in 2014 and became Associate Professor in 2016. Since 2017 he is a Reader in Computational Biology in Statistical Machine Learning for BioHealth at the University of Birmingham.
Website
http://cwcyau.github.io/index.html.
Meet the speaker
If you want to meet Dr. Christopher Yau, please book a time slot at https://doodle.com/poll/wkxkwqb6gzqvde5v and send an email to anthony.mathelier@ncmm.uio.no.
Junior talk
Dr. Christopher Yau's lecture will be preceded by a talk by Xiaoran Lai, PhD student in Biostatistics at the University of Oslo. His talk is entitled "Towards the personalized computer simulation of breast cancer treatment: a multiscale pharmacokinetic and pharmacodynamic model informed by multisource patient data."