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Representing genome-scale data with R

For most practical purposes, ``genomes" are big data, and even if we consider management of genomic sequence data to be well under control, management of the refinement of reference sequences and annotations at the species or individual level rapidly involves us in substantial problems of complexity and volume. The internet is a unifying principle, and some approaches to large-scale distributed computing (Amazon EC2, Google Compute) confer some unification on the approaches that will often be taken to deal with very large-scale genomic data processing.

We adopt the R programming language for the representation and analysis of genome-scale data for many reasons. Some are listed here.

  • Many statisticians implement new algorithms for inference in R for the purpose of testing, refining, illustrating, and distributing new methods. We want access to these methods for our genomic research.
  • R is freely distributed and redistributable, and is maintained in a freely redistributable form on a number of important hardware and software platforms.
  • Contributions to the R data analysis environment foster interoperability with other software tools including relational databases, tools for network visualization and inference, sequence management software, and high-performance computation, so it is seldom the case that use of R is an important restriction on data-analytic versatility.

In any event, we'll consider some very basic issues of representation here.

Introduction to S4 object-oriented programming

Object-oriented programming is a style of programming that emphasizes the formalized unification of heterogeneous data in "objects", defining relationships among classes of objects, and promoting software designs in which function behavior can vary according to the classes of objects on which the function is evaluated. In R's "S4" object-oriented methodology, we use

  • setClass( [classname], [representation], ...) to define a class of objects and the classes of entities from which instances of the class are composed,
  • setGeneric( [genericName], [interface], ...) to establish a generic function whose behavior will depend upon the classes of objects on which it will be evaluated,
  • setMethod( [methodName], [signature], ...) to establish the method for a specific ordered combination of objects (the ``signature'') on which the generic function is to be evaluated.

For full details, see monographs by Chambers (Software for Data Analysis) and Gentleman (R Programming for Bioinformatics).

Genomic sequence: BSgenome

In this section we use the \textit{Biostrings} infrastructure to check the genomic location of a yeast microarray probe.

First, we obtain the reference genomic sequence for sacCer2 version of yeast.

library(BSgenome.Scerevisiae.UCSC.sacCer2)
class(Scerevisiae)
Scerevisiae

We focus attention on chrIV for now.

c4 = Scerevisiae$chrIV
class(c4)
c4

Now we obtain the probe and annotation data for the Affy yeast2 array.

library(yeast2.db)
library(yeast2probe)
yeast2probe[1:3,]

We'll pick one probe set, and then the sequence of one probe.

ypick = yeast2probe[yeast2probe$Probe.Set.Name=="1769311_at",]
dim(ypick)
ypick[1:3,]
a = "ATGAGCACTATGTTTTCTGTTGGAT"
ra = reverseComplement(DNAString(a))

Now we use the simple lookup of Biostrings.

matchPattern(ra, c4)
get("1769311_at", yeast2CHRLOC)
get("1769311_at", yeast2CHRLOCEND)

Exercise. Discuss how to identify probes harboring SNP in the affy u133plus2 array.

ExpressionSet and self-description

The ExpressionSet class unifies information on a microarray experiment.

library(ALL)
data(ALL)
getClass(class(ALL))
experimentData(ALL)

A very high-level aspect of self-description is the facility for binding information on the publication of expression data to the data object itself. The abstract method gives even more detail.

Fundamental operations on ExpressionSet instances are

  • [ : subsetting, so that X[G, S] restricts the object to genes or probes enumerated in G and samples enumerated in S,
  • exprs(): return G × N matrix of expression values
  • pData(): return N × R data.frame instance with sample-level data

To enumerate additional formal methods, try showMethods(classes="eSet", inherited=FALSE, showEmpty=FALSE, where="package:Biobase")

SummarizedExperiment, VCF

Microarrays were used primarily with named probes. You would find differential intensity for 1007_s_at and look that token up. With short read sequencing or very dense arrays, it is more relevant to have access to the genomic location of the read or probe for interpretation. The SummarizedExperiment class addresses this concern.

A large-scale illustration of this class is in the \textit{dsQTL} package, but it is very large so I do not require that you download it.

library(dsQTL)
data(DSQ_17)
DSQ_17
rowData(DSQ_17)[1:3]

You can use \verb+example("SummarizedExperiment-class")+ to get a working minimal example.

We use range-based operations to subset features; column subscripting still selects samples.

The VCF class extends SummarizedExperiment to manage information on variant calls on a number of samples.

library(VariantAnnotation)
fl <- system.file("extdata", "structural.vcf", package="VariantAnnotation")
vcf <- readVcf(fl, genome="hg19")
vcf

Management of information on HTS experiments

We'll get into details on this later. For now, consider what aspects of the structures you've encountered so far appear relevant to the structure and management of HTS data.

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