R Tutorial: The ExpressionSet class

Want to learn more? Take the full course at https://learn.datacamp.com/courses/differential-expression-analysis-with-limma-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now you'll learn how to manage gene expression data using Bioconductor classes. So far you have been dealing with 3 separate objects for a given experiment. This can become tedious and precarious when you want to subset the features and/or samples in your data. For example, to subset to include only the 1000th gene and the first 10 samples, you would need to write 3 separate lines of code, making sure to always subset the correct dimension depending on which object you are subsetting. A single misplaced comma looks almost identical but will cause a huge problem. To make analysis easier and less error-prone, Bioconductor provides classes to store the data for complex biological experiments. This is an approach known as object-oriented programming. A class defines a structure to hold complex data, and a variable of a given class is referred to as an object of that class. Every class has methods, or functions that work in a special way for objects of that class. Two common types of methods are getters, which retreive the data in an object, and setters, which modify the data. As you'll see, methods for Bioconductor classes can be both getters and setters. The core Bioconductor classes are in the package Biobase. If you've used any Bioconductor packages, you likely already have this installed. However, to specifically install it, you can follow the standard Bioconductor installation process by running these two lines. You create an ExpressionSet object with the function of the same name. You'll pass it three objects. You pass the expression matrix as assayData, the phenotype data frame as phenoData, and the feature data frame as featureData. Note that you first need to convert the phenotype and feature data frames into annotate