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SUMMARY:Single Cell Seminar reboot: first 3 short talks (10x\, variation i
 n multi-omics\, combinatorial indexing JC)  - Mike Quail (Sanger Sequencin
 g R&D)\; Ricard Argelaguet (EBI - Stegle group)\; Lia Chappell (Sanger- Vo
 et group)
DTSTART:20170428T140000Z
DTEND:20170428T150000Z
UID:TALK71883@talks.cam.ac.uk
CONTACT:Dr Lia Chappell
DESCRIPTION:Note: Non-campus people should email LC5@sanger.ac.uk so that 
 you can be signed in as a visitor at reception. Please do this before 2.30
 pm on the day\, thanks!\n\n*Talk 1: Mike Quail (Sanger Sequencing R&D)\;* 
 "10x the work"\n\n*Talk 2: Ricard Argelaguet (EBI - Stegle group)\;* "Dise
 ntangling the different sources of variation in single-cell multi-omics da
 ta sets"\n\n*Talk 3: Lia Chappell (Sanger- Voet group)\;* "Mini journal cl
 ub: Comprehensive single cell transcriptional profiling of a multicellular
  organism by combinatorial indexing (Shendure lab)"\n\n*Mike's talk:* an u
 pdate on 10X chromium single cell sequencing on campus.\n\n*Ricard's talk:
 * Single-cell RNA sequencing is becoming a well-established routine that i
 s revolutionising our understanding of cellular phenotypes. Interestingly\
 , other data modalities are also starting to be assayed at the single-cell
  level\, including epigenetics\, proteomics and metabolomics\, raising the
  question of how to jointly analyse these set of complex high-dimensional 
 data sets using a statistically rigorous framework. Here we present MultiO
 mics Factor Analysis (MOFA)\, a generalisation of bayesian factor analysis
  that integrates multi-omics data sets and is suited to the analysis of no
 isy single-cell sequencing data. MOFA calculates a low dimensional represe
 ntation of the data which captures latent structures such as cellular stat
 es that might be masked in the noisy high-dimensional representation\, Fur
 thermore\, MOFA disentangles the different sources of variation and whethe
 r they are unique or shared by multiple omics\, thereby revealing hidden s
 ources of covariation. We applied MOFA to a data set of 61 embryonic stem 
 cells generated by scMT-seq\, a recent method which uses single-cell genom
 e-wide bisulfite sequencing and RNA sequencing to perform a parallel profi
 ling of the DNA methylation and the gene expression in single cells. Our r
 esults show the existence of several axes of variation related to known bi
 ological processes and suggest the existence of three subpopulations that 
 are associated with different pluripotency potential and genome-wide methy
 lation rate.\n\n*Lia's talk:* This bioRxiv paper (https://doi.org/10.1101/
 104844) has been brought to my attention by multiple campus folks. A revol
 utionary method? More hassle than it's worth here? Have a quick look at th
 e paper if you have time\, then join the discussion.
LOCATION:C302\, Sulston Building\, Wellcome Genome Campus\, Hinxton. CB10 
 1SA
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