Single-cell RNA sequencing technologies are rapidly modifying the definitions of extant cell phenotypes. However, the analysis tools used to discover these novel populations remain beyond the reach of most bench scientists. SeqGeq is a desktop bioinformatics platform designed from the bench scientist’s perspective; datasets are composed of different types of cells, how do we identify their phenotypes? In this tutorial you will learn how to utilize quality control, normalization, visualization and clustering tools to identify populations of cells in a data set. Once populations have been found, you will learn how to identify differentially expressed genes that define any given population and link those genes to downstream analyses.
This tutorial will introduce the user to:
- Quality control steps: knee calling, isolating highly dispersed genes
- Dimension reduction: PCA, tSNE and others.
- Clustering tools
- Differential gene expression
- Linking to pathway analyses
- Increasing the power of your toolbox: Installing additional plugins