MS Data Analysis

Description Usage Arguments Details Value Note Author(s) References

The function performs retention time correction by assessing the retention time deviation across all samples using peak groups (features) containg chromatographic peaks present in most/all samples. The retention time deviation for these features in each sample is described by fitting either a polynomial (smooth = "loess") or a linear (smooth = "linear") model to the data points. The models are subsequently used to adjust the retention time for each spectrum in each sample.

do_adjustRtime_peakGroups(peaks, peakIndex, rtime, minFraction = 0.9,
  extraPeaks = 1, smooth = c("loess", "linear"), span = 0.2,
  family = c("gaussian", "symmetric"), peakGroupsMatrix = matrix(ncol =
  0, nrow = 0), subset = integer(), subsetAdjust = c("average",
  "previous"))

`peaks`	a `matrix` or `data.frame` with the identified chromatographic peaks in the samples.
`peakIndex`	a `list` of indices that provides the grouping information of the chromatographic peaks (across and within samples).
`rtime`	a `list` of `numeric` vectors with the retention times per file/sample.
`minFraction`	`numeric(1)` between 0 and 1 defining the minimum required fraction of samples in which peaks for the peak group were identified. Peak groups passing this criteria will aligned across samples and retention times of individual spectra will be adjusted based on this alignment. For `minFraction = 1` the peak group has to contain peaks in all samples of the experiment. Note that if `subset` is provided, the specified fraction is relative to the defined subset of samples and not to the total number of samples within the experiment (i.e. a peak has to be present in the specified proportion of subset samples).
`extraPeaks`	`numeric(1)` defining the maximal number of additional peaks for all samples to be assigned to a peak group (i.e. feature) for retention time correction. For a data set with 6 samples, `extraPeaks = 1` uses all peak groups with a total peak count `<= 6 + 1`. The total peak count is the total number of peaks being assigned to a peak group and considers also multiple peaks within a sample being assigned to the group.
`smooth`	character defining the function to be used, to interpolate corrected retention times for all peak groups. Either `"loess"` or `"linear"`.
`span`	`numeric(1)` defining the degree of smoothing (if `smooth = "loess"`). This parameter is passed to the internal call to `loess`.
`family`	character defining the method to be used for loess smoothing. Allowed values are `"gaussian"` and `"symmetric"`.See `loess` for more information.
`peakGroupsMatrix`	optional `matrix` of (raw) retention times for peak groups on which the alignment should be performed. Each column represents a sample, each row a feature/peak group. If not provided, this matrix will be determined depending on parameters `minFraction` and `extraPeaks`. If provided, `minFraction` and `extraPeaks` will be ignored.
`subset`	`integer` with the indices of samples within the experiment on which the alignment models should be estimated. Samples not part of the subset are adjusted based on the closest subset sample. See description above for more details.
`subsetAdjust`	`character` specifying the method with which non-subset samples should be adjusted. Supported options are `"previous"` and `"average"` (default). See description above for more information.

The alignment bases on the presence of compounds that can be found in all/most samples of an experiment. The retention times of individual spectra are then adjusted based on the alignment of the features corresponding to these house keeping compounds. The paraneters minFraction and extraPeaks can be used to fine tune which features should be used for the alignment (i.e. which features most likely correspond to the above mentioned house keeping compounds).

Parameter subset allows to define a subset of samples within the experiment that should be aligned. All samples not being part of the subset will be aligned based on the adjustment of the closest sample within the subset. This allows to e.g. exclude blank samples from the alignment process with their retention times being still adjusted based on the alignment results of the real samples.

A list with numeric vectors with the adjusted retention times grouped by sample.

The method ensures that returned adjusted retention times are increasingly ordered, just as the raw retention times.

Colin Smith, Johannes Rainer

Colin A. Smith, Elizabeth J. Want, Grace O'Maille, Ruben Abagyan and Gary Siuzdak. "XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification" Anal. Chem. 2006, 78:779-787.

anupbharade09/xcms_test documentation built on May 14, 2019, 4:07 a.m.