mWISE.annotation: Function to perform the complete annotation pipeline of mWISE
In b2slab/mWISE: Untargeted LC-MS data annotation

Description Usage Arguments Value Examples

Wrapper function that performs the complete workflow of mWISE to annotate a peak-intensity matrix.

mWISE.annotation(
  Peak.List,
  force.mass.range = TRUE,
  mass.range.type = "ppm.mode",
  mz.range = NULL,
  ppm = 10,
  polarity = "positive",
  Add.List = NULL,
  Cpd.Add,
  Intensity.idx,
  Rt.05 = 5,
  use = "everything",
  method = "pearson",
  Freq = 0.5,
  Add.Id = NULL,
  background = NULL,
  diffusion.input.type = "probability",
  Unique.Annotation = FALSE,
  graph = NULL,
  K = NULL,
  score = "z",
  graph.name = "fella",
  nClust = 2,
  do.Par = TRUE
)

`Peak.List`	Data frame containing the LC-MS features. Columns should contain: Peak.Id for a peak identifier mz for a mass-to-charge ratio value rt for the retention time Intensities for each sample
`force.mass.range`	Logical. If TRUE, the m/z range is computed using the user-defined ppm tolerance or mz.range (Def: TRUE).
`mass.range.type`	A character indicating if the m/z range computation is performed using tolerance in ppm ("ppm.mode"). or m/z uncertainty ("mz.range") (Def: "ppm.mode").
`mz.range`	If mass.range.type = "mz.range", it indicates the m/z range uncertainty (i.e. mz.range = 0.001).
`ppm`	If mass.range.type = "ppm.mode", it indicates the tolerance in ppm (i.e. ppm = 10).
`polarity`	Acquisition mode of the study. It can be "positive" or "negative".
`Add.List`	List of adducts or fragments to consider.
`Cpd.Add`	Compound to adduct matrix. It can be built using `CpdaddPreparation` function.
`Intensity.idx`	Numeric vector indicating the column index for the intensities
`Rt.05`	Retention time value to get a similarity of 0.5.
`method`	A character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman".
`Freq`	Minimum observed frequency to consider an adduct or a fragment to apply the cluster-based filter.
`Add.Id`	It indicates the adduct(s) or fragment(s) that are considered to apply the cluster-based filter. If NULL, those adducts with an observed frequency equal or higher than 0.10 will be used.
`background`	Vector containing a list of KEGG identifiers which will be set to 0 in the diffusion process. This will have an effect in the normalization process performed when using the z score. If NULL, the background will be set to all the compounds available in df.
`diffusion.input.type`	Diffusion input type per compound. "binary" 1 if the compound is proposed. "probability" computes the probability of existence of each compound.
`Unique.Annotation`	Logical (only available when input type="binary"). If TRUE, the binary diffusion input is computed by only considering those peaks with a unique annotation (Def: FALSE).
`graph`	Diffusion graph where nodes correspond to KEGG compounds. If NULL, the diffusion graph indicated in `graph.name` will be loaded.
`K`	Regularised Laplacian kernel. If NULL, it will be computed using the `regularisedLaplacianKernel` function from DiffuStats R package.
`score`	Method of diffusion. Def: c("raw", "ber_s", "z")
`graph.name`	Name of the diffusion graphs available in mWISE. The options are "fella", "RClass3levels" or "RClass2levels" (Def: "fella").
`nClust`	Number of clusters that may be used (Def: 2)
`do.Par`	TRUE if parallel computing is required (Def: TRUE)

Function mWISE.annotation returns a list containing a table with all the annotations (Annotated.Tab), a table containing the features clustering (Clustered.Tab), a filtered table (MH.Tab), a table containing the diffusion scores (Diff.Tab) and the final ranked table (Ranked.Tab).

data("sample.keggDB")
Cpd.Add <- CpdaddPreparation(KeggDB = sample.keggDB, 
do.Par = FALSE)
data(sample.dataset)
Peak.List <- sample.dataset$Negative$Input
Intensity.idx <- seq(27,38)
data("sample.graph")
g.metab <- igraph::as.undirected(sample.graph)
Annotated.List <- mWISE.annotation(Peak.List = Peak.List,
                                   polarity = "negative",
                                   diffusion.input.type = "binary",
                                   score = "raw",
                                   Cpd.Add = Cpd.Add,
                                   graph = g.metab,
                                   Unique.Annotation = TRUE,
                                   Intensity.idx = Intensity.idx,
                                   do.Par = FALSE)