Project details

Single cell multiomics data has become a widely used technique in various research areas (Yuhan Hao, et.al, Cell, 2022). The current methods, however, show shortcomings in normalising and integrating several data modalities from the same tissue, due to the complexity and scale of data compositions present in data, which is still a challenge. We aim to develop a normalisation method for scATAC-seq data and then develop statistical methods to integrate RNA-seq and ATAC-seq data from the same tissues. 

In this project the student will learn how to (a) work with scRNA-seq, ATAC-seq data, (b) identify and quantify different sources of unwanted variation and (c) normalise and integrate these datasets.

About our research group

We have been applying and developing novel analytical methods for normalising and integrating large genomics data for many years. In our recent work, which has been accepted in Nature Biotechnology, we have identified batch effects in the TCGA data with 33 cancer types and more than 11000 RNA-seq samples. We have developed RUV-III-PRPS method that can normalise RNA-seq data for library size, batch effects and tumour purity variation. We have developed Rshiny and R package for normalisation of the TCGA RNA-seq data. We are now developing fast RUV-III-PRPS that can normalise scRNA-seq data with one million cells in less than two minutes.