The method computes a robust Mahalanobis distance that can be mapped to a p-value and used to identify outlying samples
rmd_filter(omicsData, ignore_singleton_groups = TRUE, metrics = NULL)an object of the class 'pepData', 'proData', 'metabData',
'lipidData', or 'nmrData' created by as.pepData,
as.proData, as.metabData,
as.lipidData, or as.nmrData, respectively.
logical indicator of whether to remove singleton groups or not; defaults to TRUE. A singleton group is a group consisting of just a single sample. If TRUE, rmd_filter results are returned only for samples in groups of size greater than 1. This is used when calculating the correlation.
A character vector indicating which metrics should be used when calculating the robust Mahalanobis distance. This vector must contain between two and five of the following options: "MAD" (Median Absolute Deviation), "Kurtosis", "Skewness", "Correlation", and "Proportion_Missing". The default is NULL. When NULL a combination of metrics will be chosen depending on the class of omicsData.
An S3 object of class 'rmdFilt' containing columns for the sample identifier, log2 robust Mahalanobis distance, p-values, and robust Mahalanobis distance
The metrics on which the log2 robust Mahalanobis distance is based
can be specified using the metrics argument.
| pepData, proData | For pepData and proData objects, all five of the metrics "MAD", "Kurtosis", "Skewness", "Correlation", "Proportion_Missing" may be used (this is the default). |
| metabData, lipidData, nmrData | The use of "Proportion_Missing" is discouraged due to the general lack of missing data in these datasets (the default behavior omits "Proportion_Missing" from the metrics). |
Matzke, M., Waters, K., Metz, T., Jacobs, J., Sims, A., Baric, R., Pounds, J., and Webb-Robertson, B.J. (2011), Improved quality control processing of peptide-centric LC-MS proteomics data. Bioinformatics. 27(20): 2866-2872.
library(pmartRdata)
mymetab <- edata_transform(omicsData = metab_object, data_scale = "log2")
mymetab <- group_designation(omicsData = mymetab, main_effects = "Phenotype")
rmd_results <- rmd_filter(omicsData = mymetab,
metrics = c("MAD", "Skewness", "Correlation"))
rmd_results <- rmd_filter(omicsData = mymetab)
mypep <- edata_transform(omicsData = pep_object, data_scale = "log2")
mypep <- group_designation(omicsData = mypep, main_effects = "Phenotype")
rmd_results <- rmd_filter(omicsData = mypep)