ArrayViews-class: ArrayViews class, constructor, and methods
In VanillaICE: A Hidden Markov Model for high throughput genotyping arrays

Description Usage Arguments Slots See Also Examples

ArrayViews provides views to the low-level data – log R ratios, B allele frequencies, and genotypes that are stored in parsed files on disk, often scaled and coerced to an integer. Accessors to the low-level data are provided that extract the marker-level summaries from disk, rescaling when appropriate.

ArrayViews(
  class = "ArrayViews",
  colData,
  rowRanges = GRanges(),
  sourcePaths = character(),
  scale = 1000,
  sample_ids,
  parsedPath = getwd(),
  lrrFiles = character(),
  bafFiles = character(),
  gtFiles = character()
)

## S4 method for signature 'ArrayViews,ANY,ANY,ANY'
x[i, j, ..., drop = FALSE]

colnames(x) <- value

## S4 method for signature 'ArrayViews'
colnames(x, do.NULL = TRUE, prefix = "col")

## S4 method for signature 'ArrayViews'
x$name

## S4 replacement method for signature 'ArrayViews'
x$name <- value

## S4 method for signature 'ArrayViews'
show(object)

## S4 method for signature 'ArrayViews'
sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)

## S4 method for signature 'ArrayViews'
ncol(x)

## S4 method for signature 'ArrayViews'
nrow(x)

## S4 method for signature 'ArrayViews'
dim(x)

## S4 method for signature 'ArrayViews'
start(x)

`class`	character string
`colData`	DataFrame
`rowRanges`	GRanges object
`sourcePaths`	character string provide complete path to plain text source files (one file per sample) containing log R ratios and B allele frequencies
`scale`	log R ratios and B allele frequencies can be stored as integers on disk to increase IO speed. If scale =1, the raw data is not transformed. If scale = 1000 (default), the log R ratios and BAFs are multipled by 1000 and coerced to an integer.
`sample_ids`	character vector indicating how to name samples. Ignored if colData is specified.
`parsedPath`	character vector indicating where parsed files should be saved
`lrrFiles`	character vector of file names for storing log R ratios
`bafFiles`	character vector of file names for storing BAFs
`gtFiles`	character vector of file names for storing genotypes
`x`	a `ArrayViews` object
`i`	numeric vector or missing
`j`	numeric vector or missing
`...`	additional arguments to `FUN`
`drop`	ignored
`value`	a character-string vector
`do.NULL`	ignored
`prefix`	ignored
`name`	character string indicating name in colData slot of ArrayViews object
`object`	a `ArrayViews` object
`X`	a `ArrayViews` object
`FUN`	a function to apply to each column of `X`
`simplify`	logical indicating whether result should be simplied
`USE.NAMES`	whether the output should be a named vector

colData: A character string
rowRanges: A DataFrame. WARNING: The accessor for this slot is rowRanges, not rowRanges!
index: A GRanges object
sourcePaths: A character string providing complete path to source files (one file per sample) containing low-level summaries (Log R ratios, B allele frequencies, genotypes)
scale: A length-one numeric vector
parsedPath: A character string providing full path to where parsed files should be saved
lrrFiles: character vector of filenames for log R ratios
bafFiles: character vector of filenames for BAFs
gtFiles: character vector of filenames for genotypes

CopyNumScanParams parseSourceFile

ArrayViews()
## From unit test
  require(BSgenome.Hsapiens.UCSC.hg18)
  require(data.table)
  extdir <- system.file("extdata", package="VanillaICE", mustWork=TRUE)
  features <- suppressWarnings(fread(file.path(extdir, "SNP_info.csv")))
  fgr <- GRanges(paste0("chr", features$Chr), IRanges(features$Position, width=1),
                 isSnp=features[["Intensity Only"]]==0)
  fgr <- SnpGRanges(fgr)
  names(fgr) <- features[["Name"]]
  bsgenome <- BSgenome.Hsapiens.UCSC.hg18
  seqlevels(fgr, pruning.mode="coarse") <- seqlevels(bsgenome)[seqlevels(bsgenome) %in% seqlevels(fgr)]
  seqinfo(fgr) <- seqinfo(bsgenome)[seqlevels(fgr),]
  fgr <- sort(fgr)
  files <- list.files(extdir, full.names=TRUE, recursive=TRUE, pattern="FinalReport")
  ids <- gsub(".rds", "", gsub("FinalReport", "", basename(files)))
  views <- ArrayViews(rowRanges=fgr,
                      sourcePaths=files,
                      sample_ids=ids)
  lrrFile(views)
  ## view of first 10 markers and samples 3 and 5
  views <- views[1:10, c(3,5)]