Abstract

Identifying Minor Cell Populations from Single-Cell Nucleus RNA-Seq Data with Partek Flow Software

Paul Fullerton, Field Application Scientist, Partek, Inc.

Single-cell RNA sequencing (scRNA-Seq) shows enormous promise to expand our understanding of heterogeneous tissues and complex diseases. A major bottleneck in scRNA-Seq experiments is bioinformatic analysis of data sets with thousands of cells. This session demonstrates scRNA-Seq analysis in Partek Flow, a robust Next-Generation Sequencing data analysis platform with an interactive graphical user interface that does not sacrifice power, speed, or flexibility when compared with command-line tools. In this session, we analyze a recently published data set that classified cell types in the mouse brain using single-cell nucleus RNA sequencing from frozen mouse brain tissue (Habib et al., Massively parallel single-nucleus RNA-seq with DroNc-seq, Nature Methods, 2017). We extend the analysis performed in the manuscript, using Partek Flow tools, such as t-SNE and K-means clustering, to focus on minor cell populations.