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Convert peak list into an ordered peak table.

Source: R/get_peaktable.R
get_peaktable.Rd

Returns a peak_table object. The first slot contains a matrix of intensities, where rows correspond to samples and columns correspond to aligned features. The rest of the slots contain various meta-data about peaks, samples, and experimental settings.

Usage

get_peaktable(
  peak_list,
  chrom_list,
  response = c("area", "height"),
  use.cor = NULL,
  hmax = 0.2,
  plot_it = FALSE,
  ask = plot_it,
  clust = c("rt", "sp.rt"),
  sigma.t = NULL,
  sigma.r = 0.5,
  deepSplit = FALSE,
  verbose = FALSE,
  out = c("data.frame", "matrix")
)

Arguments

peak_list

A peak_list object created by get_peaks, containing a nested list of peak tables: the first level is the sample, and the second level is the spectral wavelength. Every component is described by a data.frame where every row is one peak, and the columns contain information on various peak parameters.

chrom_list

A list of chromatographic matrices.

response

Indicates whether peak area or peak height is to be used as intensity measure. Defaults to area setting.

use.cor

Logical. Indicates whether to use corrected retention times (rt.cor column) or raw retention times (rt column). Unless otherwise specified, the rt.cor column will be used by default if it exists in the provided peak_list.

hmax

Height at which the complete linkage dendrogram will be cut. Can be interpreted as the maximal inter-cluster retention time difference.

plot_it

Logical. If TRUE, for every component a stripplot will be shown indicating the clustering.

ask

Logical. Ask before showing new plot? Defaults to TRUE.

clust

Specify whether to perform hierarchical clustering based on spectral similarity and retention time (sp.rt) or retention time alone (rt). Defaults to rt. The sp.rt option is experimental and should be used with caution.

sigma.t

Width of gaussian in retention time distance function. Controls weight given to retention time if sp.rt is selected.

sigma.r

Width of gaussian in spectral similarity function. Controls weight given to spectral correlation if sp.rt is selected.

deepSplit

Logical. Controls sensitivity to cluster splitting. If TRUE, function will return more smaller clusters. See documentation for cutreeDynamic for additional information.

verbose

Logical. Whether to print warning when combining peaks into single time window. Defaults to FALSE.

out

Specify data.frame or matrix as output. Defaults to data.frame.

Value

The function returns an S3 peak_table object, containing the following elements:

  • tab: the peak table itself -- a data-frame of intensities in a sample x peak configuration.

  • pk_meta: A data.frame containing peak meta-data (e.g. the spectral component, peak number, and average retention time).

  • sample_meta: A data.frame of sample meta-data. Must be added using attach_metadata).

  • ref_spectra: A data.frame of reference spectra (in a wavelength x peak configuration). Must be added using attach_ref_spectra

  • args: A vector of arguments given to get_peaktable to generate the peak table.

Details

The function performs a complete linkage clustering of retention times across all samples, and cuts at a height given by the user (which can be understood as the maximal inter-cluster retention time difference) in the simple case based on retention times. Clustering can also incorporate information about spectral similarity using a distance function adapted from Broeckling et al., 2014: $$latexascii$$ If two peaks from the same sample are assigned to the same cluster, a warning message is printed to the console. These warnings can usually be ignored, but one could also consider reducing the hmax variable. However, this may lead to splitting of peaks across multiple clusters. Another option is to filter the peaks by intensity to remove small features.

Note

This function is adapted from getPeakTable function in the alsace package by Ron Wehrens.

References

  • Broeckling, C. D., Afsar F.A., Neumann S., Ben-Hur A., and Prenni J.E. 2014. RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics Data. Anal. Chem. 86:6812-6817. doi:10.1021/ac501530d .

  • Wehrens, R., Carvalho, E., Fraser, P.D. 2015. Metabolite profiling in LC–DAD using multivariate curve resolution: the alsace package for R. Metabolomics 11:143-154. doi:10.1007/s11306-014-0683-5 .

See also

attach_ref_spectra attach_metadata

Author

Ethan Bass

Examples

if (FALSE) { # interactive()
data(Sa_pr)
pks <- get_peaks(Sa_pr, lambdas = c('210'))
get_peaktable(pks, response = "area")
}