Bioinformatics Seminars

Bioinformatics Seminar

Time: 10:45am Tuesdays.
Level 7 Seminar Room 2, WEHI1

5 June 2018

Improving differential expression analysis for single-cell RNA-seq data: method and application

Chengzhong Ye
WEHI Bioinformatics

Single-cell RNA sequencing (scRNA-seq) technology enables high-throughput transcriptome profiling at single-cell resolution, providing researchers with an unprecedented opportunity for dissecting heterogeneous biological systems. Yet distinct features in the scRNA-seq data present a variety of analytical challenges. For example, extensive loss of molecules occurs at all stages of the experimental process, which gives rise to the characteristic dropout events in data. This talk consists of two parts, both focusing on bioinformatics approaches for scRNA-seq experiments. The first part describes the design of a novel statistical method for differential gene expression (DE) analysis. Through explicit modelling of the molecule capturing process, we are able infer and perform DE analysis on the unobserved pre-dropout molecule counts. Benchmarking using simulated and real data showed improved performance compared with existing methods. The second part discusses a data analysis workflow of a breast cancer tumour infiltrating T cell dataset. Leveraging several new dedicated scRNA-seq methods, including our DE model, we have identified and characterized a novel cell population, tissue-resident memory T cells, in the tumour infiltrating T cells.

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