Online Program

Comparative Analysis of Statistical Methods for Single-Cell RNA Sequencing Data (309955)

Keywords: scRNA sequencing, Imputation, Differential testing

Single-cell RNA (scRNA) sequencing is a high throughput analysis that enables researchers to understand the gene expression within a single cell, in what quantities they are expressed, and how they differ across thousands of cells within a heterogeneous sample (eg., early embryo development). Single-cell RNA datasets are mostly characterized by a high percentage of missing values due to technical limitations and stochastic gene expression. This could pose a major problem as the missing data can introduce bias and affect downstream analyses. Over the years, a number of methods have been developed to address the issue of missingness in single-cell RNA datasets and test the differential expression between group of cells. In this presentation, I will briefly outline the main differences between the traditional(Bulk)RNA sequencing and single-cell RNA, do a literature review of some scRNA sequencing imputation methods (addressing their advantages as well as limitations), and conduct an analysis using publicly available scRNA seq data to compare the various imputation methods. Finally, I will conduct a differential expression test on the sample data using different methods and compare my findings.