Grouping of features

Group equal features across analyses.

groupFeatures(obj, algorithm, ...)

# S4 method for class 'features'
groupFeatures(obj, algorithm, ..., verbose = TRUE)

# S4 method for class 'data.frame'
groupFeatures(obj, algorithm, ..., verbose = TRUE)

# S4 method for class 'featuresSet'
groupFeatures(obj, algorithm, ..., verbose = TRUE)

Arguments

obj

Either a features object to be grouped, or a data.frame with analysis info to be passed to groupFeaturesSIRIUS

algorithm

A character that specifies the algorithm to be used: either "openms", "xcms", "xcms3" or "kpic2" (features method), or "sirius" (data.frame method).

...

Further parameters passed to the selected grouping algorithm.

verbose

if FALSE then no text output will be shown.

Value

An object of a class which is derived from featureGroups.

The featuresSet method (for sets workflows) returns a featureGroupsSet object.

Details

After features have been found, the next step is to align and group them across analyses. This process is necessary to allow comparison of features between multiple analyses, which otherwise would be difficult due to small deviations in retention and mass data. Thus, algorithms of 'feature groupers' are used to collect features with similar retention and mass data. In addition, advanced retention time alignment algorithms exist to enhance grouping of features even with relative large retention time deviations (e.g. possibly observed from analyses collected over a long period). Like findFeatures, various algorithms are supported which may have many parameters that can be fine-tuned. This fine-tuning is likely to be necessary, since optimal settings often depend on applied methodology and instrumentation.

groupFeatures is a generic function that will groupFeatures by one of the supported algorithms. The actual functionality is provided by algorithm specific functions such as groupFeaturesOpenMS and groupFeaturesXCMS3. While these functions may be called directly, groupFeatures provides a generic interface and is therefore usually preferred.

The data.frame method for groupFeatures is a special case that currently only supports the "sirius" algorithm.

See also

The featureGroups output class and its methods and the algorithm specific functions: groupFeaturesOpenMS, groupFeaturesXCMS, groupFeaturesXCMS3, groupFeaturesKPIC2, groupFeaturesSIRIUS