Abstract

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 2.30pm on the day, thanks!

Talk 1: Mike Quail (Sanger Sequencing R&D); “10x the work”

Talk 2: Ricard Argelaguet (EBI – Stegle group); “Disentangling the different sources of variation in single-cell multi-omics data sets”

Talk 3: Lia Chappell (Sanger- Voet group); “Mini journal club: Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing (Shendure lab)”

Mike’s talk: an update on 10X chromium single cell sequencing on campus.

Ricard’s talk: Single-cell RNA sequencing is becoming a well-established routine that is 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 MultiOmics Factor Analysis (MOFA), a generalisation of bayesian factor analysis that integrates multi-omics data sets and is suited to the analysis of noisy single-cell sequencing data. MOFA calculates a low dimensional representation of the data which captures latent structures such as cellular states that might be masked in the noisy high-dimensional representation, Furthermore, MOFA disentangles the different sources of variation and whether they are unique or shared by multiple omics, thereby revealing hidden sources 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 genome-wide bisulfite sequencing and RNA sequencing to perform a parallel profiling of the DNA methylation and the gene expression in single cells. Our results show the existence of several axes of variation related to known biological processes and suggest the existence of three subpopulations that are associated with different pluripotency potential and genome-wide methylation rate.

Lia’s talk: This bioRxiv paper (https://doi.org/10.1101/104844) has been brought to my attention by multiple campus folks. A revolutionary method? More hassle than it’s worth here? Have a quick look at the paper if you have time, then join the discussion.