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This article will explain in detail how to use the scater package to analyze the dimensionality reduction of single-cell transcriptome data. The content of the article is of high quality, so the editor will share it for you as a reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.

For single-cell transcriptome data, there are three commonly used dimensionality reduction methods

PCA

T-SNE

Difffusion map

Through the R package scater, it is convenient to carry out dimensionality reduction analysis. The installation method is as follows.

BiocManager::install ("scater", version = "3.8")

The specific steps are as follows

1. Building SingleCellExperiment objects

For single-cell data, a class called SingleCellExperiment is specially developed to store related data.

The first thing we need to do is to import the relevant data into R, and we only need the next two kinds of data. the first is the expression data of genes, with each row representing a gene and each column representing a cell, as shown below.

The second is the cell-related information, which can be the cell name, sampling time, source tissue, processing conditions and other metadata. Each row is a cell, and each column is an attribute, as shown below.

From these two types of data, you can build a SingleCellExperiment object with the following code

Sce

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