ramclustR: ramclustR

In sneumann/RAMClustR: RAMClust: a novel feature clustering method enables spectral-matching based annotation for metabolomics data

Description

Main clustering function for grouping features based on their analytical behavior.

Usage

ramclustR(xcmsObj = NULL, ms = NULL, idmsms = NULL,
  taglocation = "filepaths", MStag = NULL, idMSMStag = NULL,
  featdelim = "_", mzpos = 1, timepos = 2, st = NULL, sr = NULL,
  maxt = NULL, deepSplit = FALSE, blocksize = 2000, mult = 5,
  hmax = NULL, sampNameCol = 1, collapse = TRUE, usePheno = TRUE,
  mspout = TRUE, ExpDes = NULL, normalize = "TIC", minModuleSize = 2,
  linkage = "average", mzdec = 4, cor.method = "pearson",
  fftempdir = NULL)

Arguments

xcmsObj	xcmsObject: containing grouped feature data for clustering by ramclustR
ms	filepath: optional csv input. Features as columns, rows as samples. Column header mz_rt
idmsms	filepath: optional idMSMS / MSe csv data. same dim and names as ms required
taglocation	character: "filepaths" by default, "phenoData[,1]" is another option. referse to xcms slot
idMSMStag	character: character string in 'taglocation' to designat idMSMS / MSe files e.g. "02.cdf"
featdelim	character: how feature mz and rt are delimited in csv import column header e.g. ="-"
timepos	integer: which position in delimited column header represents the retention time (csv only)
st	numeric: sigma t - time similarity decay value
sr	numeric: sigma r - correlational similarity decay value
maxt	numeric: maximum time difference to calculate retention similarity for - all values beyond this are assigned similarity of zero
deepSplit	logical: controls how agressively the HCA tree is cut - see ?cutreeDynamicTree
blocksize	integer: number of features (scans?) processed in one block =1000,
mult	numeric: internal value, can be used to influence processing speed/ram usage
hmax	numeric: precut the tree at this height, default 0.3 - see ?cutreeDynamicTree
sampNameCol	integer: which column from the csv file contains sample names?
collapse	logical: reduce feature intensities to spectrum intensities?
usePheno	logical: tranfer phenotype data from XCMS object to SpecAbund dataset?
mspout	logical: write msp formatted specta to file?
ExpDes	either an R object created by R ExpDes object: data used for record keeping and labelling msp spectral output
normalize	character: either "none", "TIC", or "quantile" normalization of feature intensities
minModuleSize	integer: how many features must be part of a cluster to be returned? default = 2
linkage	character: heirarchical clustering linkage method - see ?hclust
mzdec	integer: number of decimal places used in printing m/z values
cor.method	character: which correlational method used to calculate 'r' - see ?cor
fftempdir	valid path: if there are file size limitations on the default ff pacakge temp directory - getOptions('fftempdir') - you can change the directory used as the fftempdir with this option.

Details

Main clustering function output - see citation for algorithm description of vignette('RAMClustR') for a walk through

Value

$featclus: integer vector of cluster membership for each feature

$frt: feature retention time, in whatever units were fed in (xcms uses seconds, by default)

$fmz: feature retention time, reported in number of decimal points selected in ramclustR function

$xcmsOrd: the original XCMS (or csv) feature order for cross referencing, if need be

$clrt: cluster retention time

$clrtsd: retention time standard deviation of all the features that comprise that cluster

$nfeat: number of features in the cluster

$nsing: number of 'singletons' - that is the number of features which clustered with no other feature

$ExpDes: the experimental design object used when running ramclustR. List of two dataframes.

$cmpd: compound name. C#### are assigned in order of output by dynamicTreeCut. Compound with the most features is classified as C0001...

$ann: annotation. By default, annotation names are identical to 'cmpd' names. This slot is a placeholder for when annotations are provided

$MSdata: the MSdataset provided by either xcms or csv input

$MSMSdata: the (optional) MSe/idMSMS dataset provided be either xcms or csv input

$SpecAbund: the cluster intensities after collapsing features to clusters

$SpecAbundAve: the cluster intensities after averaging all samples with identical sample names

- 'spectra' directory is created in the working directory. In this directory a .msp is (optionally) created, which contains the spectra for all compounds in the dataset following clustering. if MSe/idMSMS data are provided, they are listed witht he same compound name as the MS spectrum, with the collision energy provided in the ExpDes object provided to distinguish low from high CE spectra.

Author(s)

Corey Broeckling

References

Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE. RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem. 2014 Jul 15;86(14):6812-7. doi: 10.1021/ac501530d. Epub 2014 Jun 26. PubMed PMID: 24927477.

Broeckling CD, Ganna A, Layer M, Brown K, Sutton B, Ingelsson E, Peers G, Prenni JE. Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction. Anal Chem. 2016 Sep 20;88(18):9226-34. doi: 10.1021/acs.analchem.6b02479. Epub 2016 Sep 8. PubMed PMID: 7560453.

sneumann/RAMClustR documentation built on May 30, 2019, 5:05 a.m.