Single-cell RNA sequencing (scRNA-seq) comprises a state-of-the-art technology for analysing the gene expression profile of individual cells from a complex or even a uniform specimen. Differential gene expression analysis and Uniform Manifold Approximation and Projection (UMAP) plots may reveal transcriptomic differences among cell populations, pointing out genes that are either upregulated or downregulated in relation to a desirable outcome (i.e. expression or not of a gene of interest or a marker). While currently extracellular protein information of same single cells can also be included in scRNA-seq analyses, both of the data can provide clues for identifying such interesting genes. Furthermore, to shorten the list of possible interesting genes, a gene set enrichment analysis (GSEA) could also suggest interesting pathways that are regulated on a particular cell population. In addition, constructing volcano plots when comparing two cell populations ameliorates the users for recognizing the fold increase or decrease in gene expression and the significance of that. Finally, more complicated computational methods (i.e. pseudotime analysis) could further reveal differentiated cellular states and suggest genes regulating these biological events.
Author: Filippos Charitidis (ESR9)
