Imports feature groups from Bruker TASQ

Imports screening results from Bruker TASQ as feature groups.

Usage

importFeatureGroupsBrukerTASQ(
  input,
  analysisInfo,
  clusterRTWindow = defaultLim("retention", "medium")
)

Arguments

input: The file path to an Excel export of the Global results table from TASQ, converted to .csv format.
analysisInfo: A data.frame (or data.table) with Analysis information.
clusterRTWindow: This retention time window (in seconds) is used to group hits across analyses together. See also the details section.

Value

A new featureGroups object containing converted screening results from Bruker TASQ.

Details

This function imports data from Bruker TASQ. This function is called when calling importFeatureGroups with type="brukertasq".

The feature groups across analyses are formed based on the name of suspects and their closeness in retention time. The latter is necessary because TASQ does not necessarily perform checks on retention times and may therefore assign a suspect to peaks with different retention times across analyses (or within a single analysis). Hence, suspects with equal names are hierarchically clustered on their retention times (using fastcluster) to form the feature groups. The cut-off value for this is specified by the clusterRTWindow argument. The input for this function is obtained by generating an Excel export of the 'global' results and subsequently converting the file to .csv format.

Note

This function uses estimated min/max values for retention times and dummy min/max m/z values for conversion to features, since this information is not (readily) available. Hence, when plotting, for instance, extracted ion chromatograms (with plotChroms) the integrated chromatographic peak range shown is incorrect.

This function may use suspect names to base file names used for reporting, logging etc. Therefore, it is important that these are file-compatible names.

References

Müllner D (2013). “fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python.” Journal of Statistical Software, 53(9), 1–18. doi:10.18637/jss.v053.i09 .