Abstract
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.
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Acknowledgments
This work was supported by National Key R&D Program of China (2018YFC1004500), the National Science Foundation of China (81872330 and 31741077), and Basic Research Grant from Science and Technology Innovation Commission of Shenzhen Municipal Government (JCYJ20170817111841427).
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Zhou, B., Jin, W. (2020). Visualization of Single Cell RNA-Seq Data Using t-SNE in R. In: Kidder, B. (eds) Stem Cell Transcriptional Networks. Methods in Molecular Biology, vol 2117. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0301-7_8
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DOI: https://doi.org/10.1007/978-1-0716-0301-7_8
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