Bayesian methods for improved analysis of single-cell RNA-seq data
The BitSeq package provides Bayesian methods for inferring the expression level of mRNA transcripts from the computational analysis of large-scale RNA sequencing (RNA-Seq) data (Glaus et al. 2012). BitSeq was shown to perform well in a recent large-scale benchmark study comparing different methods (SEQC/MAQC-III Consortium 2014) and is under constant development to improve accuracy and computation time (Hensman et al. 2014; Papastamoulis et al. 2014). BitSeq works by deconvolving the signal from large numbers of short RNA sequence reads which are derived from different transcripts that may share a significant proportion of their underlying mRNA sequence, e.g. different isoforms or allelic variants of the same gene. This deconvolution allows the inference of the expression-level of closely related transcripts, providing valuable insights into differences in splicing and allele usage across conditions or treatments.
The widespread adoption of single-cell RNA-Seq protocols now allows for transcriptomic profiling of single cells with the potential to revolutionise our understanding of cellular heterogeneity and to leverage this heterogeneity to infer gene regulatory networks. However, the inference of closely related transcripts from single-cell data is challenging as single-cell RNA-Seq experiments tend to be carried out with lower sequencing depth and the sample preparation is more challenging leading to greater biases in the data produced. Inferring transcription expression levels is therefore particularly challenging from single-cell RNA-Seq data. Downstream processing methods to infer regulatory networks are also of great interest and can benefit from improved low-level data analysis. In this project the student will work on a BitSeq analysis pipeline for single-cell RNA-seq processing. Manchester has recently received a £5m award from the UK’s Medical Research Council to establish a clinical single-cell research centre and the student will work with members of the centre to apply and benchmark new methodology developed in the project on clinical single-cell RNA-Seq datasets.
Please follow the full instructions on how to make an online application on the How to Apply page.
For this self-funded project, applicants are encouraged to contact the Principal Supervisor directly to discuss their application and the project. Please select any of the subject areas displayed on this project listing when making your online application.
Glaus, Peter, Antti Honkela, and Magnus Rattray. "Identifying differentially expressed transcripts from RNA-seq data with biological variation." Bioinformatics 28.13 (2012): 1721-1728.
Hensman, James, et al. "Fast and accurate approximate inference of transcript expression from RNA-seq data." arXiv preprint arXiv:1412.5995 (2014).
Papastamoulis, Panagiotis, et al. "Improved variational Bayes inference for transcript expression estimation." Statistical applications in genetics and molecular biology 13.2 (2014): 203-216.
SEQC/MAQC-III Consortium. "A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium." Nature Biotechnology 32.9 (2014): 903-914.