FAMEoutliers: FAME outlier detection
In TargetSearch: A package for the analysis of GC-MS metabolite profiling data

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

A function to detect retention time marker (FAME) outliers.

1 2	FAMEoutliers(samples, RImatrix, pdffile = NA, startDay = NA, endDay = NA, threshold = 3, group.threshold = 0.05)

`samples`	A `tsSample` object created by `ImportSamples` function.
`RImatrix`	A retention time matrix of the found retention time markers.
`pdffile`	A character string naming a PDF file where the FAMEs report will be saved.
`startDay`	A numeric vector with the starting days of your day groups.
`endDay`	A numeric vector with the ending days of your day groups.
`threshold`	A standard deviations cutoff to detect outliers.
`group.threshold`	A numeric cutoff to detect day groups based on hierarchical clustering. Must be between `0..1`.

If no pdffile argument is given, the report will be saved on a file called "TargetSearch-YYYY-MM-DD.FAME-report.pdf", where YYYY-MM-DD is a date.

If both startDay and endDay are not given, the function will try to detect day groups using a hierarchical clustering approach by cutting the tree using group.threshold as cutoff height.

Retention time markers that deviate more than threshold standard deviations from the mean of their day group will be identified as outliers.

A logical matrix of the same size of RImatrix. A TRUE value indicates that the retention time marker in that particular sample is an outlier.

Alvaro Cuadros-Inostroza, Matthew Hannah, Henning Redestig

RIcorrect, ImportSamples, TSExample

# load pre-calculated example data and objects
data(TSExample)

# find the retention marker outliers of the example data and save it in "outlier.pdf"
outliers <- FAMEoutliers(sampleDescription, RImatrix, pdffile = "outlier.pdf")

# find the outliers (although they are reported in the output PDF file)
apply(outliers, 1, which)