rc.get.csv.data: rc.get.csv.data

In RAMClustR: Mass Spectrometry Metabolomics Feature Clustering and Interpretation

rc.get.csv.data

Description

extractor for csv objects in preparation for normalization and clustering

Usage

rc.get.csv.data(
  csv = NULL,
  phenoData = NULL,
  idmsms = NULL,
  ExpDes = NULL,
  sampNameCol = 1,
  st = NULL,
  timepos = 2,
  featdelim = "_",
  ensure.no.na = TRUE
)

Arguments

csv	filepath: csv input. Features as columns, rows as samples. Column header mz_rt
phenoData	character: character string in 'taglocation' to designate files as either MS / DIA(MSe, MSall, AIF, etc) e.g. "01.mzML"
idmsms	filepath: optional idMSMS / MSe csv data. same dim and names as ms required
ExpDes	either an R object created by R ExpDes object: data used for record keeping and labelling msp spectral output
sampNameCol	integer: which column from the csv file contains sample names?
st	numeric: sigma t - time similarity decay value
timepos	integer: which position in delimited column header represents the retention time
featdelim	character: how feature mz and rt are delimited in csv import column header e.g. ="-"
ensure.no.na	logical: if TRUE, any 'NA' values in msint and/or msmsint are replaced with numerical values based on 10 percent of feature min plus noise. Used to ensure that spectra are not written with NA values.

Details

This function creates a ramclustObj which will be used as input for clustering.

Value

an empty ramclustR object. this object is formatted as an hclust object with additional slots for holding feature and compound data. details on these found below.

$frt: feature retention time, in whatever units were fed in

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

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

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

$MSMSdata: the (optional) DIA(MSe, MSall, AIF etc) dataset

$xcmsOrd: original xcms order of features, for back-referencing when necessary

$msint: weighted.mean intensity of feature in ms level data

$msmsint:weighted.mean intensity of feature in msms level data

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.

Examples

## Choose csv input file. Features as columns, rows as samples
## Choose csv input file phenoData 
filename <- system.file("extdata", "peaks.csv", package = "RAMClustR", mustWork = TRUE)
phenoData <- system.file("extdata", "phenoData.csv", package = "RAMClustR", mustWork = TRUE)

ramclustobj <- rc.get.csv.data(csv = filename, phenoData = phenoData, st = 5)

RAMClustR documentation built on Oct. 20, 2023, 5:08 p.m.