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SUMMARY:“Learning from Data in Single-Cell Transcriptomics” - Professo
 r Sandrine Dudoit\, University of California\, Berkeley
DTSTART:20220224T170000Z
DTEND:20220224T180000Z
UID:TALK168854@talks.cam.ac.uk
CONTACT:Alison Quenault
DESCRIPTION:I will discuss statistical methods and software for the analys
 is of single-cell transcriptome sequencing (RNA-Seq) data to investigate t
 he differentiation of olfactory stem cells. RNA-Seq studies provide a grea
 t example of the range of questions one encounters in a Data Science workf
 low. I will survey the methods and software my group has developed for exp
 loratory 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).
LOCATION:Venue to be confirmed
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