racExSet-class {GGtools} | R Documentation

## Class "racExSet" for combining RareAlleleCount representations of SNPs, gene expression data, and other phenotype data

### Description

A container for combining RareAlleleCount representations of SNPs, gene expression data, and other phenotype data

### Objects from the Class

Objects can be created by calls of the form `new("racExSet", phenoData, experimentData, annotation, exprs, racs, ...)`.

### Slots

`racAssays`:
  Object of class `"AssayData"` SxN matrix of 0, 1, 2, or NA, rownames are RS numbers from dbSNP, column names are sampleNames
`assayData`:
  Object of class `"AssayData"` GxN matrix of expression values
`rarebase`:
  character vector of length G stating the rare base of the SNP
`SNPalleles`:
  character vector of length G stating the alleles in the form "A/B"
`phenoData`:
  Object of class `"AnnotatedDataFrame"` standard phenoData instance
`experimentData`:
  Object of class `"MIAME"` experiment level metadata
`annotation`:
  Object of class `"character"` tag
`.__classVersion__`:
  Object of class `"Versions"` R class version control metadata

### Extends

Class `"eSet"`, directly. Class `"VersionedBiobase"`, by class `"eSet"`. Class `"Versioned"`, by class `"eSet"`.

### Methods

snps
  `signature(x = "racExSet")`: accessor for the racAssay matrix
snpNames
  `signature(x = "racExSet")`: give RS numbers for rows of racAssay matrix
racAssays
  `signature(x = "racExSet")`: accessor for the racAssays slot
oneFit
  `signature(racExSet = "racExSet", geneid = "genesym", snpid = "character", fitfun = "function")`: use a genesym instance (e.g., `genesym("DDR1")` to identify the expression values, an rs number to identify the snp genotype information, and fitfun to fit a model with expression as dependent variable and genotype (rare allele counts) as predictor. Any fitting function accepting a formula and data frame should succeed (e.g., `lm`).
oneFit
  `signature(racExSet = "racExSet", geneid = "character", snpid = "character", fitfun = "function")`: as above, but can avoid the lookup of the gene symbol by supplying the probe set identifier directly. This is problematic at present as most probe set identifier strings are not valid R variable names, and so cannot serve directly in the formula. Some backtick coding is needed to get this to work generally.
oneFit
  `signature(racExSet = "racExSet", geneid = "list", snpid = "character", fitfun = "function")`: the `geneid` parameter must be a named list of length one with content a numeric vector of expression values conforming to the snp values. Avoids lookup of gene name and of expression values.
oneFit
  `signature(racExSet = "racExSet", geneid = "list" , snpid="formula", fitfun="function")`: the `geneid` parameter must be bound to a named list of length 1 with content a numeric vector with one element per sample; the name of the list element is the gene symbol on which the numeric vector is a vector of expression values; the `snpid` parameter is bound to a formula template, either `~.` or `~factor(.)` expressing choice between an additive genetic model and a general model.

### Notes

Helper functions `SNPalleles` and `rarebase` are accessors. `snps5PrimeTo(gene,rad)` will obtain rs numbers for SNPs in selected locations relative to the specified gene. `snps3PrimeTo` and `snpsNear` have similar purposes.

### Note

The `genesym` class works in conjunction with annotation to allow translation from HUGO symbols to affy probe set names according to Bioconductor annotation environments. The `getpsid` function carries out the translation; the `genesym` class/wrapper just tells us that a string will be serving the role of a HUGO symbol and allows us to dispatch accordingly. The `snpID` class has similar role. It just lets us know that a string is intended to be regarded as an rs number.

`hgfocusSYMMEMO` is a lookup table quickly establishing the probeset ID based on a HUGO symbol.

`chr20GGdem` is a demonstration example of chromosome 20 hapmap snps and hgfocus expression data from Cheung.

The `exclMono` function eliminates loci that happen to be monomorphic in the sample.

### Author(s)

VJ Carey <stvjc@channing.harvard.edu>

### See Also

`make_racExSet` for construction; `eSet` ancestor class

### Examples

    example(make_racExSet)
    dem
    snpNames(dem)[1:4]
    featureNames(dem)[1:4]
    oneFit(dem, genesym("DDR1"), "rs4814683", lm)
    ddr1 = list(DDR1=exprs(dem)["1007_s_at",])
    oneFit(dem, ddr1, "rs4814683", aov)
    data(chr20GGdem)
    ddr1 = list(DDR1=exprs(chr20GGdem)["1007_s_at",])
    summary(oneFit(chr20GGdem, ddr1, ~rs4814683+factor(rs6060535), lm))
    dim(snps(chr20GGdem))
    dim(snps(exclMono(chr20GGdem)))

[Package GGtools version 1.2.0 Index]