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SUMMARY:Developing a single-cell transcriptomic data analysis pipeline - A
 mit Grover / Denise Vlachou\, GSK
DTSTART:20180227T130000Z
DTEND:20180227T140000Z
UID:TALK100801@talks.cam.ac.uk
CONTACT:Dr Vivien Gruar
DESCRIPTION:Recent technological advances in single-cell transcriptomics h
 ave the potential to have a huge impact in the field of drug discovery. Ho
 wever\, pipelines that we could use for target identification and validati
 on are only in their stages of infancy. The data produced from single cell
  RNA-seq experiments is huge\, complex\, and highly stochastic\, so comput
 ational methods and mathematical expertise are critical.\n\nIn this projec
 t\, the student will help us to develop a comprehensive and robust pipelin
 e(s) to analyse complex datasets generated through multiple readouts for s
 ingle cells. \nAs a starting point\, the student will use already establis
 hed methods on single-cell RNA-seq data (and bulk RNA-seq data) to generat
 e gene expression values. Using this the student will produce an analysis 
 (via clear visualisations) on single cell trajectories using appropriate m
 ethods of normalisation\, dimensionality reduction (eg PCA\, SNE)\, cluste
 r identification etc.\n\nAn ambitious student will then incorporate additi
 onal phenotypic datasets (eg the results of  machine learning analysis on 
 images of the cells) to this pipeline(s)\, requiring the development of no
 vel methods to analyse multiple data types\, ultimately resulting in a mas
 ter pipeline. \n\n
LOCATION:MR3 Centre for Mathematical Sciences
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