Title:  Building atlases to benchmark transcriptomics studies    

Abstract: 
With the advent of new sequencing technologies, we face the issue of data obsolescence. Who uses microarray or bulk RNA-seq data, when all the hype is about single cell RNA-seq? We do!

In the first part of this presentation, I will summarise our past research attempting to re-use bulk transcriptomics data from the community to try answer three questions:

1.    How can I compare the expression of my genes of interest across multiple and independent transcriptomics studies to validate my results?

2.    How can I combine multiple and independent transcriptomics data sets to identify a platform-agnostic signature?

3.    How can I build transcriptomics atlases to benchmark my own study?

In the second part of the presentation, I will present our recent computational and statistical framework, Sincast* (SINgle-cell data CASTing onto reference) to annotate cell identity from single cell RNA-seq from bulk RNA-seq reference atlases. We solve structural discrepancies between bulk and single cell data by either aggregating or imputing single cells and discuss the most beneficial approach depending on the data context. Sincast can also be used to reveal intermediate single cell states when projected against bulk data.

I will also briefly discuss the benefit of using hackathon data to advance methods development in multi-omics single cell. 

This is some joint work with my PhD student Yidi Deng and Dr Jarny Choi (Centre for Stem Cell Systems, University of Melbourne)