ArrayViews class, constructor, and methods

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Description

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.

Usage

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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)

Arguments

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

Slots

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

See Also

CopyNumScanParams parseSourceFile

Examples

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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) <- 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)]

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