R Tutorial: Load, create, and access single-cell datasets in R

DataCamp · Beginner ·🔢 Mathematical Foundations ·6y ago

Key Takeaways

Loads, creates, and accesses Single Cell RNA-Seq data in R

Original Description

Want to learn more? Take the full course at https://learn.datacamp.com/courses/single-cell-rna-seq-with-bioconductor-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Let's learn how to load, create, and access single-cell data in R. In this video, we focus on the SingleCellExperiment class. It’s a S4 class developed by Aaron Lun and Davide Risso. And it’s very useful to analyze single-cell data because it allows you to easily store and retrieve the matrix of counts but also information about the cells and the genes. You remember the three matrices (one with the raw counts, and the other two for gene and cell-level information) I showed in the previous video, the idea here is that we are going to use only one R object to store these three matrices. How does it work in practice? First, you need to install and load the SingleCellExperiment package using the usual biocLite and library functions. The next step is to create a SingleCellExperiment object. And, there are two ways to do this. The first way is to use the SingleCellExperiment function. For that, we'll create a small matrix counts with 4 genes and 2 cells where we simulate counts from a Poisson distribution using the rpois() function. Let's use the rownames() and colnames() functions to name the rows as the gene names and the columns as the cell names. Here, you see that gene names are Lamp5, Fam19a1, Cnr1, and RORB and cell names start with SRR and then a number. Using the matrix counts we just created, we create a sce object using the SingleCellExperiment() function. The first argument is assays which takes a list of count matrices. Here we'll only use our matrix counts with 4 genes and 2 cells. The second and third arguments are rowData and colData for data frames with information about the genes and cells. Here we only store the gene and cell names which are the row and column names of our matrix counts. You see that we have created
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