My research project
Statistical methods to the analysis of large scale single cell RNA-seq data
With the advance in next-generation sequencing technologies, single-cell RNA sequencing (scRNA-seq) allows researchers to analyze transcriptomic information of individual cells. To dissect scRNA-seq data, computational methods are applied in several steps including mapping, quality control, quantification, clustering or differentially expressed gene analysis. There are challenges that remained in the computational analysis of scRNA-seq data. For example, high level of noise, sparsity and batch effects are some reported properties of scRNA-seq data. The importance of scRNA-seq analysis has been demonstrated in several studies, for example, from differentially expressed gene analysis of scRNA-seq data between circulating tumor cells and primary tumor cells of hepatocellular carcinoma patients, chemokine CCL5 was identified as the mediator for immune evasion of circulating tumor cells. Developing computational tools which allows to overcome challenges such as noise or batch effects in scRNA-seq data analysis based on gene regulatory network inference is the main goal of our research which can induce single-cell analyses such as cell-type, cell-state identification using scRNA-seq data.