table.to.rc: table.to.rc

View source: R/table.to.rc.R View source: R/csv.to.rc.R

table.to.rcR Documentation

table.to.rc

Description

import preprocessed protein/metabolite data from csv file to convert to ramclustR object.

import preprocessed protein/metabolite data from csv file to convert to ramclustR object.

Usage

table.to.rc(
  table.file = NULL,
  factor.columns = NULL,
  sample.name.column = NULL
)

table.to.rc(
  table.file = NULL,
  factor.columns = NULL,
  sample.name.column = NULL
)

Arguments

factor.columns

integer: which column from the csv file contains sample names. i.e. 1, or 1:3

import.table

filepath: csv, xls, or xlsx input. Molecules as columns, rows as samples. Column header is molecule name.

Details

This function creates a ramclustObj which can be used to enable downstream statistical analysis.

This function creates a ramclustObj which can be used to enable downstream statistical analysis.

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

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.

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)

## 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)


cbroeckl/csu.pmf.tools documentation built on May 21, 2024, 1:26 a.m.