MS Data Analysis

Description Usage Arguments Details Value Note Author(s) See Also

This function handles the construction of xcmsSet objects. It finds peaks in batch mode and pre-sorts files from subdirectories into different classes suitable for grouping.

xcmsSet(files = NULL, snames = NULL, sclass = NULL, phenoData = NULL,
        profmethod = "bin", profparam = list(),
        polarity = NULL, lockMassFreq=FALSE,
	mslevel=NULL, nSlaves=0, progressCallback=NULL,
        scanrange = NULL, BPPARAM = bpparam(),
        stopOnError = TRUE, ...)

`files`	path names of the NetCDF/mzXML files to read
`snames`	sample names. By default the file name without extension is used.
`sclass`	sample classes.
`phenoData`	`data.frame` or `AnnotatedDataFrame` defining the sample names and classes and other sample related properties. If not provided, the argument `sclass` or the subdirectories in which the samples are stored will be used to specify sample grouping.
`profmethod`	Method to use for profile generation. Supported values are `"bin"`, `"binlin"`, `"binlinbase"` and `"intlin"` (for methods `profBin`, `profBinLin`, `profBinLinBase` and `profIntLin`, respectively). See help on `profBin` for a complete list of available methods and their supported parameters.
`profparam`	parameters to use for profile generation.
`polarity`	filter raw data for positive/negative scans
`lockMassFreq`	Performs correction for Waters LockMass function
`mslevel`	perform peak picking on data of given mslevel
`nSlaves`	DEPRECATED, use `BPPARAM` argument instead.
`progressCallback`	function to be called, when progressInfo changes (useful for GUIs)
`scanrange`	scan range to read
`BPPARAM`	a `BiocParallel` parameter object to control how and if parallel processing should be performed. Such objects can be created by the `SerialParam`, `MulticoreParam` or `SnowParam` functions.
`stopOnError`	Logical specifying whether the feature detection call should stop on the first encountered error (the default), or whether feature detection is performed in all files regardless eventual failures for individual files in which case all errors are reported as `warnings`.
`...`	further arguments to the `findPeaks` method of the `xcmsRaw` class

The default values of the files, snames, sclass, and phenoData arguments cause the function to recursively search for readable files. The filename without extention is used for the sample name. The subdirectory path is used for the sample class. If the files contain both positive and negative spectra, the polarity can be selected explicitly. The default (NULL) is to read all scans.

If phenoData is provided, it is stored to the phenoData slot of the returned xcmsSet class. If that data.frame contains a column named “class”, its content will be returned by the sampclass method and thus be used for the group/class assignment of the individual files (e.g. for peak grouping etc.). For more details see the help of the xcmsSet-class.

The step size (in m/z) to use for profile generation can be submitted either using the profparam argument (e.g. profparam=list(step=0.1)) or by submitting step=0.1. By specifying a value of 0 the profile matrix generation can be skipped.

The feature/peak detection algorithm can be specified with the method argument which defaults to the "matchFilter" method (findPeaks.matchedFilter). Possible values are returned by getOption("BioC")$xcms$findPeaks.methods.

The lock mass correction allows for the lock mass scan to be added back in with the last working scan. This correction gives better reproducibility between sample sets.

A xcmsSet object.

The arguments profmethod and profparam have no influence on the feature/peak detection. The step size parameter step for the profile generation in the findPeaks.matchedFilter peak detection algorithm can be passed using the ....

Colin A. Smith, csmith@scripps.edu

xcmsSet-class, findPeaks, profStep, profMethod, profBin, xcmsPapply