S4 Classes and Methods

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关键词：Methods classes Method ETH SES

1. library(ALL)
   
2. library(GenomicRanges)

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Use the following commands to install these packages in R.

1. source("http://www.bioconductor.org/biocLite.R")
   
2. biocLite(c("ALL" "GenomicRanges"))

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## Overview
The S4 system in R is a system for object oriented programing. Confusingly, R has support for at least 3 different systems for object oriented programming: S3, S4 and S5 (also known as reference classes).

The S4 system is heavily used in Bioconductor, whereas it is very lightly used in “traditional” R and in packages from CRAN. As a user it can be useful to recognize S4 objects and to learn some facts about how to explore, manipulate and use the help system when encountering S4 classes and methods.

The S4 system in R is a system for object oriented programing. Confusingly, R has support for at least 3 different systems for object oriented programming: S3, S4 and S5 (also known as reference classes).

The S4 system is heavily used in Bioconductor, whereas it is very lightly used in “traditional” R and in packages from CRAN.

As a user it can be useful to recognize S4 objects and to learn some facts about how to explore, manipulate and use the help system when encountering S4 classes and methods.

[hr]Important note for programmers
If you have experience with object oriented programming in other languages, for example java, you need to understand that in R, S4 objects and methods are completely separate. You can use S4 classes without every using S4 methods and vice versa.

S3 and S4 classes

Based on years of experience in Bioconductor, it is fair to say that S4 classes have been very successful in this project. S4 classes has allowed us to construct rich and complicated data representations that nevertheless seems simple to the end user. An example, which we will return to, are the data containers ExpressionSet and SummarizedExperiment.

Let us look at a S3 object, the output of the linear model function lm in base R:

1. df <- data.frame(y = rnorm(10), x = rnorm(10))
   
2. lm.object <- lm(y ~ x, data = df)
   
3. lm.object

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1. ##
   
2. ## Call:
   
3. ## lm(formula = y ~ x, data = df)
   
4. ##
   
5. ## Coefficients:
   
6. ## (Intercept)            x  
   
7. ##      0.3147      -0.7131

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1. names(lm.object)

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1. ##  [1] "coefficients"  "residuals"     "effects"       "rank"         
   
2. ##  [5] "fitted.values" "assign"        "qr"            "df.residual"  
   
3. ##  [9] "xlevels"       "call"          "terms"         "model"

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1. class(lm.object)

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1. ## [1] "lm"

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In standard R, an S3 object is essentially a list with a class attribute on it. The problem with S3 is that we can assign any class to any list, which is nonsense. Let us try an example

1. xx <- list(a = letters[1:3], b = rnorm(3))
   
2. xx

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1. ## $a
   
2. ## [1] "a" "b" "c"
   
3. ##
   
4. ## $b
   
5. ## [1]  0.3380950  0.8861906 -1.1216766

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1. class(xx) <- "lm"
   
2. xx

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1. ##
   
2. ## Call:
   
3. ## NULL
   
4. ##
   
5. ## No coefficients

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At least we don’t get an error when we print it.

S4 classes have a formal definition and formal validity checking. To the end user, this gurantees validity of the object.