exportTable: Exports an object to file.

In CRUKMI-ComputationalBiology/twoddpcr: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules

Description

If given a data frame, exportTable exports the whole data frame to file. This could be a data frame of any form. A few options are available that can be used to determine the format of the file that is exported, e.g. using a heading for the row names 'column', or omitting row names altogether.

If a ddpcrWell is given, exportTable exports to a single file with specified/all classification methods.

If a ddpcrPlate is given, exportTable exports to a directory in the given location, where one file is created for each of the wells. If it does not exist, the directory location will be created as long as all other parent directories exist.

exportZip takes a ddpcrPlate object and exports it as a zip file.

Usage

exportTable(theObject, location, delim = ",", ...)

## S4 method for signature 'data.frame'
exportTable(
  theObject,
  location,
  delim = ",",
  leadingColName = NULL,
  row.names = TRUE
)

## S4 method for signature 'ddpcrWell'
exportTable(theObject, location, delim = ",", cMethod = NULL)

## S4 method for signature 'ddpcrPlate'
exportTable(
  theObject,
  location,
  delim = ",",
  cMethod = NULL,
  prefix = "",
  suffix = "_Amplitude.csv"
)

exportZip(
  theObject,
  location,
  delim = ",",
  cMethod = NULL,
  prefix = "",
  suffix = "_Amplitude.csv"
)

## S4 method for signature 'ddpcrPlate'
exportZip(
  theObject,
  location,
  delim = ",",
  cMethod = NULL,
  prefix = "",
  suffix = "_Amplitude.csv"
)

Arguments

theObject	The dataframe to export.
location	The location to export to. This should be a filename if we are using exportZip, or we are using exportTable and theObject is a data frame or ddpcrWell object. If theObject is a ddpcrPlate object, this should be a directory.
delim	The character to use as a field separator. Defaults to ",", i.e. export a CSV.
...	Other options depending on the type of theObject.
leadingColName	The name of the leading column, i.e. the 'row names' of the dataframe. This could be a patient identifier or the well used in the ddPCR experiment. If NULL, the exported heading will be an empty string. Defaults to NULL.
row.names	If NULL, exports a column corresponding to the row names; if FALSE, no such column is included. If 'leadingColName' is not FALSE, row.names is assumed to be FALSE. Defaults to TRUE.
cMethod	The name or column number of the classification methods in a ddpcrWell or ddpcrPlate object to export to file. If NULL, all of the classification methods are exported. Defaults to NULL.
prefix	For ddpcrPlate objects, this is the prefix to prepend to the output filenames.
suffix	For ddpcrPlate objects, this is the suffix to append to the output filenames. This is typically the filename extension, e.g. ".csv" or ".txt". Defaults to ".csv".

Details

Note that filenames of the form Anything_A01_Amplitude.csv can be read by readCSVDataFrame so that the well name can be extracted successfully (in this case A01). Where it is used, see the default value of the parameter suffix.

Value

Exports a file.

Author(s)

Anthony Chiu, [e-mail]

Examples

## Output to a temporary directory.
tmpOut <- file.path(normalizePath(tempdir()))

## Read some counts data and generate a summary data frame.
df <- fullCountsSummary(KRAScountsQS)
summaryDf <- fullCopiesSummary(df)

## Write the summary to a CSV file.
exportTable(summaryDf, file.path(tmpOut, "summary-table.csv"))

## Write the summary to a tab-separated text file.
exportTable(summaryDf, file.path(tmpOut, "summary-table.txt"), delim="\t")

## Write the summary to a CSV file with leading column labelled "Patient".
exportTable(summaryDf, file.path(tmpOut, "summary-table.csv"),
            leadingColName="Patient")

## Read a droplet amplitude CSV file to a ddpcrWell object.
ampFile <- system.file("extdata", "amplitudes", "sample_B03_Amplitude.csv",
                       package="twoddpcr")
aWell <- ddpcrWell(well=ampFile)

## Classify the droplets into 4 clusters.
aWell <- kmeansClassify(aWell, centres=4)

## Write the amplitudes to a CSV file with the old and new classifications.
exportTable(aWell,
  location=file.path(tmpOut, "With_Kmeans_B03_Amplitude.csv"))

## Write the amplitudes to a CSV file with the new classification only.
exportTable(aWell,
  location=file.path(tmpOut, "With_Kmeans_B03_Amplitude.csv"),
  cMethod="kmeans")

## Read all amplitude files in a directory to a ddpcrPlate object.
moreAmpsDir <- system.file("extdata", "more-amplitudes", package="twoddpcr")
krasPlate <- ddpcrPlate(wells=moreAmpsDir)

## Classify the droplets into 4 clusters.
krasPlate <- kmeansClassify(krasPlate, centres=4)

## Write the amplitudes to multiple files in a directory with the old and
## new classifications.
exportTable(krasPlate, location=file.path(tmpOut, "amplitudes-classified"))

## Write the amplitudes to multiple files with the new classification only
## and a custom prefix for the filenames.
exportTable(krasPlate, location=file.path(tmpOut, "amplitudes-classified"),
            cMethod="kmeans", prefix="Kmeans_Only_")

## Export to a zip file.
exportZip(krasPlate,
          location=file.path(tmpOut, "amplitudes-classified/all.zip"),
          cMethod="kmeans", prefix="Kmeans_Only_")

CRUKMI-ComputationalBiology/twoddpcr documentation built on Feb. 14, 2021, 9:18 p.m.