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RAMClustR: Mass Spectrometry Metabolomics Feature Clustering and Interpretation

A feature clustering algorithm for non-targeted mass spectrometric metabolomics data. This method is compatible with gas and liquid chromatography coupled mass spectrometry, including indiscriminant tandem mass spectrometry data.

Documentation for users

Installation

The newest version of the package can be installed through conda from the bioconda channel:

conda install -c bioconda r-ramclustr

Or you can alternatively Install from R console:

install.packages("devtools", repos="http://cran.us.r-project.org", dependencies=TRUE)

library(devtools)

install_github("cbroeckl/RAMClustR", build_vignettes = TRUE, dependencies = TRUE)

library(RAMClustR)

vignette("RAMClustR")

Introduction

Main clustering function output - see citation for algorithm description or vignette('RAMClustR') for a walk through. batch.qc. normalization requires input of three vectors (1) batch (2) order (3) qc. This is a feature centric normalization approach which adjusts signal intensities first by comparing batch median intensity of each feature (one feature at a time) QC signal intensity to full dataset median to correct for systematic batch effects and then secondly to apply a local QC median vs global median sample correction to correct for run order effects.

There are two pathways for using RAMClustR; You can use either use the main ramclustR function or the individual stepwise workflow.

Below is a small example of using main ramclustR function.

## Choose input file with feature column names `mz_rt` (expected by default).
## Column with sample name is expected to be first (by default).
## These can be adjusted with the `featdelim` and `sampNameCol` parameters.
wd <- getwd()
filename <- file.path(wd, "testdata/peaks.csv")
pheno <- file.path(wd, "testdata/phenoData.csv") 
print(filename)
head(data.frame(read.csv(filename)), c(6L, 5L))

## If the file contains features from MS1, assign those to the `ms` parameter.
## If the file contains features from MS2, assign those to the `idmsms` parameter.
## If you ran `xcms` for the feature detection, the assign the output to the `xcmsObj` parameter.
## In this example we use a MS1 feature table stored in a `csv` file.
setwd(tempdir())
ramclustobj <- ramclustR(
    ms = filename,
    pheno_csv = pheno,
    st = 5,
    maxt = 1,
    blocksize = 1000
  )

## Investigate the deconvoluted features in the `spectra` folder in MSP format
## or inspect the `ramclustobj` for feature retention times, annotations etc.
print(ramclustobj$ann)
print(ramclustobj$nfeat)
print(ramclustobj$SpecAbund[,1:6])
setwd(wd)

Individual stepwise workflow

alt text

Below is a small example of using Individual stepwise workflow.

set.seed(123) # to get reproducible results with jitters
wd <- getwd()
tmp <- tempdir()
load(file.path("testdata", "test.rc.ramclustr.fillpeaks"))

setwd(tmp)

ramclustObj <- rc.get.xcms.data(xcmsObj = xdata)
ramclustObj <- rc.expand.sample.names(ramclustObj = ramclustObj)
ramclustObj <- rc.feature.replace.na(ramclustObj = ramclustObj)
ramclustObj <- rc.feature.filter.blanks(ramclustObj = ramclustObj, blank.tag = "Blanc")
ramclustObj <- rc.feature.normalize.qc(ramclustObj = ramclustObj, qc.tag = "QC")
ramclustObj <- rc.feature.filter.cv(ramclustObj = ramclustObj)
ramclustObj <- rc.ramclustr(ramclustObj = ramclustObj)
ramclustObj <- rc.qc(ramclustObj = ramclustObj)
ramclustObj <- do.findmain(ramclustObj = ramclustObj)

## Investigate the deconvoluted features in the `spectra` folder in MSP format
## or inspect the `ramclustobj` for feature retention times, annotations etc.
print(ramclustobj$ann)
print(ramclustobj$nfeat)
print(ramclustobj$SpecAbund[,1:6])
setwd(wd)

Documentation for developers

Installation

git clone https://github.com/cbroeckl/RAMClustR.git
cd RAMClustR
conda env create -n ramclustr-dev -f=conda/environment-dev.yaml
conda activate ramclustr-dev

Testing

# Activate the ramclustr-dev environment
# Run the below command on R console
devtools::test()

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.