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Today, I will talk to you about how to use Rtsne package for t-SNE dimensionality reduction analysis, many people may not know much about it. In order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

T-SNE dimensionality reduction algorithm is an efficient dimensionality reduction algorithm proposed by Daniel in the field of machine learning in 2008, which is a kind of nonlinear dimensionality reduction algorithm. Compared with the commonly used PCA algorithm, this algorithm is more advanced and has many applications. In the data analysis of single-cell transcripts, t-SNE is more widely used.

T-SNE reduction and visualization analysis are provided in cell ranger and other special software packages for analyzing single cell data. However, due to different software requirements for different data formats, in some cases, we can not directly use the existing software package, for example, we may have only one gene expression data in all cells, which can not be directly imported into cell ranger or Seurat.

Because of the different software interface settings, in order to carry out data analysis more flexibly, it is necessary for us to master some small and beautiful analysis tools, which can only complete a certain item of data analysis. The singleness of its function further reduces the learning cost and significantly improves the flexibility.

Rtsne is an R package specially designed for t-SNE dimensionality reduction analysis, which is installed as follows

Install.packages ("Rtsne")

All you need to do is enter a table of expression, one cell per behavior, each listed as a gene, as shown below

The basic usage is as follows

Tsne_out

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