University of Cambridge > Talks.cam > Cambridge BSU Lectures in Biomedical Data Science > “Learning from Data in Single-Cell Transcriptomics”

“Learning from Data in Single-Cell Transcriptomics”

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  • UserProfessor Sandrine Dudoit, University of California, Berkeley
  • ClockThursday 24 February 2022, 17:00-18:00
  • HouseVenue to be confirmed.

If you have a question about this talk, please contact Alison Quenault.

This will be a virtual lecture. To register for free, please click here: https://www.eventbrite.co.uk/e/cambridge-bsu-lecture-in-biomedical-data-science-prof-sandrine-dudoit-tickets-251259533027

I will discuss statistical methods and software for the analysis of single-cell transcriptome sequencing (RNA-Seq) data to investigate the differentiation of olfactory stem cells. RNA -Seq studies provide a great example of the range of questions one encounters in a Data Science workflow. I will survey the methods and software my group has developed for exploratory data analysis (EDA), dimensionality reduction, normalization, expression quantitation, cluster analysis, and the inference of cellular lineages. Our methods are implemented in open-source R software packages released through the Bioconductor Project (https://www.bioconductor.org).

This talk is part of the Cambridge BSU Lectures in Biomedical Data Science series.

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