Investigating Inconsistencies in Single-Cell RNA Sequencing Data

Student: Pranay V. Singh (Bowdoin College, University of Southern California)
Supervisor: Juliet Emamaullee (University of Southern California)

Abstract: Single-cell RNA sequencing (scRNA-seq) has emerged as a robust method for computational genomics. It combines cell biology with computer science in order to analyze thousands of cells at a single-cell level, allowing us to observe gene expression levels and how they differ across diverse samples. This translates to a knowledge about the exact functions of these cells which can be used to better understand disease processes and discover exciting new potential therapies. However, very few studies analyze the effects of preanalytical variables such as tissue handling and scRNA-seq technique on sequencing results. In this study, we examined three normal ‘human liver’ scRNA-seq datasets from high-quality peer-reviewed publications to see if they yielded comparable results. We hypothesized the pre-analytical variables in scRNA-seq studies would have an impact on characterization of the human liver immune cell transcriptome.

ACM-SRC Semi-Finalist: no

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