Single cell RNA sequencing (scRNA-seq) is a powerful tool to analyze cellular heterogeneity, identify new cell types, and infer developmental trajectories, which has greatly facilitated studies on development, immunity, cancer, neuroscience, and so on. Visualizing of scRNA-Seq data is fundamental and essential because it is critical to biological interpretation. Although principal component analysis (PCA) is used for visualizing scRNA-seq at early studies, t-Distributed Stochastic Neighbor embedding (t-SNE), an unsupervised nonlinear dimensionality reduction technique, is widely used nowadays due to its advantage in visualization of scRNA-seq data. Here, we detailed the process of visualization of single-cell RNA-seq data using t-SNE via Seurat, an R toolkit for single cell genomics.
Keywords: Dimension reduction; Seurat; Single cell RNA sequencing (scRNA-seq); Visualization of scRNA-seq data; t-Distributed Stochastic Neighbor embedding (t-SNE).