• A Package is a collection of functions
• Associated with a given task/analysis/data-type
• The main repository is "The Comprehensive R Archive Network" (https://cran.r-project.org/)

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Home > Packages > dplyr

Here you can see all of the functions in dplyr

Many packages also define objects of a given class,
e.g. a data_frame, tbl_df or tibble

• All packages (~1200) are for Bioinformatics
  • Statistical Analysis; Databases & Data Handling; Visualisation
  • NGS data, microarrays, flow cytometry, proteomics…
• New releases every ~6 months
• All packages come with a descriptive vignette

browseVignettes()

Also has an active support community:

• https://support.bioconductor.org/
• Large suite of tutorials & workflows

A recent training course:

http://www.bioconductor.org/help/course-materials/2016/BioC2016/

Some upcoming courses:

http://www.abacbs.org/biocasia2016workshop

3 Broad Headings based on package tags, or biocViews

1. Software
2. AnnotationData
3. ExperimentData

1. Software

• Currently >1000 packages, primarily for analysis
• Heavily used array packages: affy, gcrma, limma
• Access to external databases: biomaRt, topGO
• Rich in Seq analysis packages: edgeR, DESeq, RSamtools
• Wrappers for external Seq tools: muscle, RBowtie
• Lots of new object classes defined

2. Annotation

• Currently >900 packages
• Set database classes (OrgDb, TxDb, OrganismDb, BSgenome)
• Annotations for common microarrays (e.g. Affy & Illumina)

3. Experiment Data

• Currently ~300 packages
• Includes standard datasets for algorithm testing
• Also those included in many training courses

• Packages don't appear in the drop-down menu for RStudio
  • Tools > Install Packages > ???
• Can be added to your default repositories, but there is a preferred installation procedure

source("http://bioconductor.org/biocLite.R")

• This installs the package BiocInstaller
• Manages the synchronisation of R releases and Bioconductor updates
• The main installation function is biocLite()
• Installs from both CRAN & Bioconductor

R dependencies can be challenging!

To check that you have the tested package versions and fix them

library(BiocInstaller)
biocValid(fix = TRUE)

R has two common types of objects

• Built on top of (and including) vectors, lists etc
• S3 and S4
• S3 are very common
  • Usually list-type objects
  • e.g. results from lm() or t.test()

Each defined class has a set of methods associated with it

iris_lm <- lm(Petal.Length ~ Species, data = iris)
class(iris_lm)
typeof(iris_lm)

At it's heart an object of class lm is a list

• It has a specific structure
• Methods for lm objects depend on this structure

methods(class = "lm")

These are all functions that can be applied to an lm object

Let's pick one of those defined methods, e.g. residuals()

• We can find what other classes of object have these

methods("residuals")

Note the suffixes!!

Functions that end in name.something() apply to objects of class something

• S3 objects are very loose
• We can mess with them

class(iris_lm) <- "htest"

No error message!!!???

iris_lm

Did we break it?

str(iris_lm)

body(summary.lm)
body(summary.default)

Many packages define methods for given objects

methods(class = "data.frame")

library(dplyr)
methods(class = "data.frame")

body(print)

This doesn't seem to make sense

Now type a . after print, then Tab

These are all the visible classes a print method exists for

methods(print)

Many Bioconductor Packages define S4 objects

• Very strict controls on data structure
• Can be frustrating at first
• Use the @ symbol as well as $
• Methods are also strictly defined

Some packages use S4 implementations of S3 objects, e.g.

• data.frame (S3) Vs DataFrame (S4)
• rle (S3) Vs Rle

Look and behave very similarly, but can trip you over

• Object may require a DataFrame and you give it a data.frame

• data.frame
  1. Can set rownames
  2. Dumps all data to your screen
• tbl_df aka data_frame
  1. rownames are always 1:nrow(df)
  2. Prints a summary

library(tibble)
mtcars
mtcars %>% rownames_to_column("model") %>% as_tibble()
mtcars %>% rownames_to_column("model") %>% as_tibble() %>% print(n=3)

I this last line we used print.tbl_df

library(S4Vectors)
?DataFrame

• An S4 version
• Can have columns of lists (so can tbl_df objects)
• The function CharacterList() from IRanges is used
• Incompatible with dplyr unless coerced

library(IRanges)
genes <- c("A", "B")
transcripts <- CharacterList(c("A1", "A2", "A3"),
                             c("B1", "B2"))
transcripts

DF <- DataFrame(Gene = genes, Transcripts = transcripts)
DF

## DataFrame with 2 rows and 2 columns
##          Gene     Transcripts
##   <character> <CharacterList>
## 1           A        A1,A2,A3
## 2           B           B1,B2

Enables addition of metadata

library(Biobase)
metaData <- data.frame(labelDescription = c("The measured length", 
                                       "The Delivery Method",
                                       "The Dose in mg"))
newTooth <- AnnotatedDataFrame(data = ToothGrowth,
                               varMetadata = metaData)

newTooth
pData(newTooth)
varMetadata(newTooth)