xMSwrapper.apLCMS: xMSwrapper.apLCMS

In BowenNCSU/xMSanalyzer: xMSanalyzer: R package for data analysis, comparison, and annotation of metabolomics data.

Description

Wrapper function based on apLCMS.align,evaluate.Features, evaluate.Samples,merge.Results, and feat.batch.annotation.

Usage

xMSwrapper.apLCMS(cdfloc, apLCMS.outloc, xMSanalyzer.outloc, min.run.list = c(4, 
    3), min.pres.list = c(0.5, 0.8), minexp.pct = 0.1, mztol = NA, alignmztol = NA, 
    alignchrtol = NA, numnodes = NA, run.order.file = NA, apLCMSmode = "untargeted", 
    known_table = NA, match_tol_ppm = 5, max.mz.diff = 15, max.rt.diff = 300, 
    merge.eval.pvalue = 0.2, mergecorthresh = 0.7, deltamzminmax.tol = 10, 
    subs = NA, num_replicates = 3, mz.tolerance.dbmatch = 10, adduct.list = c("M+H"), 
    samp.filt.thresh = 0.7, feat.filt.thresh = 50, cormethod = "pearson", 
    mult.test.cor = TRUE, missingvalue = 0, ignore.missing = TRUE, filepattern = ".cdf", 
    sample_info_file = NA, refMZ = NA, refMZ.mz.diff = 10, refMZ.time.diff = NA, 
    void.vol.timethresh = 30, replacezeroswithNA = TRUE, scoreweight = 30, 
    charge_type = "pos", syssleep = 0.5)

Arguments

cdfloc	The folder where all NetCDF/mzML/mzXML/mzData) files to be processed are located. For example "C:/CDF/"
apLCMS.outloc	The folder where apLCMS feature alignment output will be written. For example "C:/apLCMSoutput/"
xMSanalyzer.outloc	The folder where xMSanalyzer output will be written. For example "C:/xMSanalyzeroutput/"
min.run.list	List of values for min.run parameter, eg: c(3,6) would run the cdf.to.ftr function at min.run=3 and min.run=6
min.pres.list	List of values min.pres, eg: c(0.3,0.8) would run the cdf.to.ftr function at min.run=3 and min.run=6
minexp.pct	If a feature is to be included in the final feature table, it must be present in at least this fraction of spectra. eg: 0.2
mztol	The user can provide the m/z tolerance level for peak identification to override the programs selection of the tolerance level. This value is expressed as the percentage of the m/z value. This value, multiplied by the m/z value, becomes the cutoff level. Please see the help for proc.cdf() for details.
alignmztol	The user can provide the m/z tolerance level for peak alignment to override the programs selection. This value is expressed as the percentage of the m/z value. This value, multiplied by the m/z value, becomes the cutoff level.Please see the help for feature.align() for details.
numnodes	Number of computational nodes to use for sample processing. eg: 2
run.order.file	Name of a tab-delimited file that includes sample names sorted by the order in which they were run (sample names must match the CDF file names)
max.mz.diff	+/- mz tolerance in ppm for feature matching
max.rt.diff	retention time tolerance for feature matching
merge.eval.pvalue	Threshold for defining signifcance level of the paired t-test or the Pearson correlation during the merge stage in xMSanalyzer. The p-value is used to determine whether two features with same m/z and retention time have identical intensity profiles.
mergecorthresh	Correlation threshold to be used during the merge stage in xMSanalyzer to determine whether two features with same m/z and retention time have identical intensity profiles.
subs	If not all the CDF files in the folder are to be processed, the user can define a subset using this parameter. For example, subs=15:30, or subs=c(2,4,6,8)
num_replicates	Number of replicates per sample
mz.tolerance.dbmatch	m/z threshold for database matching
adduct.list	List of adducts for matching m/zs in KEGG. eg: c("M+H", "M+Na")
samp.filt.thresh	Threshold for filtering samples based on Pearson correlation between technical replicates. eg: 0.7
feat.filt.thresh	Threshold for filtering samples based on percent intensity difference or coefficient of variation. eg: 50
cormethod	Method for determing correlation between technical replicates. eg: "pearson" or "spearman
mult.test.cor	Should Bonferroni multiple testing correction method be applied for comparing intensities of overlapping m/z? Default: FALSE
missingvalue	How are missing values represented? eg: 0 or NA
ignore.missing	Should the missing values be ignored while computing pearson correlation. eg: TRUE or FALSE
filepattern	File format of spectral data files. eg: ".cdf", ".mzXML"
alignchrtol	The retention time tolerance level for peak alignment. The default is NA, which allows the program to search for the tolerance level based on the data. Default: 10
apLCMSmode	"untargeted" or "hybrid"; Default: "untargeted"
known_table	A data frame containing the known metabolite ions and previously found features. Please see documentation of semi.sup() function in apLCMS for more details.
match_tol_ppm	The ppm tolerance to match identified features to known metabolites/features. Used by the semi.sup() function in apLCMS. Default: 5
deltamzminmax.tol	Maximum allowed delta ppm between mz.min and mz.max. Eg: 10
refMZ	Full path of the file with m/z of the targeted chemicals to search for. If the value is "NA", then the list of metabolites in the data(example_target_list) is used. The input file should be formatted as data(example_target_list): Column A: m/z of the targeted metabolite (required) Column B: retention time (Optional) Column C: Name of the metabolite (required)
refMZ.mz.diff	The ppm tolerance to search for targeted metabolites/features in xMSanalyzer. Default: 10
refMZ.time.diff	The time tolerance to search for targeted metabolites/features in xMSanalyzer. Default: NA
void.vol.timethresh	Threshold for void volume. The program searches for the void volume cutoff within the defined time limit. Default: 30
replacezeroswithNA	Should 0s be treated as missing values during ComBat (TRUE or FALSE). Default: TRUE
scoreweight	The w parameter in the scoring function defined in the xMSanalyzer manuscript. Uppal 2013, BMC Bioinformatiocs. Default: 30
sample_info_file	File listing the order in which the samples were run. The format of the file should be as follows: Column A:File names matching the CDF/mzXML files Column B: Sample ID Column C: Batch number (This column should be labeled "Batch") Column D: Additional covariates to adjust for (Optional)
charge_type	Ionization mode. "pos" or "neg" Default: "pos"
syssleep	Sleep time in between KEGG REST queries. Default: "0.5"

Details

The wrapper function includes five stages to utilize information from the technical replicates to optimize the data extraction process, enhance data quality, search for targeted features/metabollites, perform QA & QC evaluations including batch-effect evaluation & correction. : 1) features are extracted using different parameters 2) results from each parameter setting are evaluated for sample quality & feature consistency, 3) filtered results from individual settings are merged to obtain a combined feature table; the optimization score that takes into account the number of features and average CV (see Uppal 2013) is used to determine the most optimal set of parameters. 4) A targeted feature table using the list of m/z in the "refMZ" file is generated 5) Quality measures of each featue includes: number of samples including replicates with non-missing values (NumPres.All.Samples), number of biological samples for which more than 60 median coefficient of variation (CV) within technical replicates summarized across all samples; Qscore (Quality score which is calculated using NumPres.All.Samples, NumPres.Biol.Samples, median CV, and delta ppm between mz.min and mz.max; Higher is better) Users have the option to filter poor quality samples and features based on correlation between technical replicates and feature reproducibility measures such as PID or CV, respectively.

Value

A list is returned.

apLCMS.merged.res	Merged feature table, P1 U P2 where P1 and P2 are two sets of parameter settings. Please note that the four columns include the characteristics of the feature from apLCMS. The "npeaks" column includes the number of samples with a non-missing intensity. The "QRscore" is defined as the ratio of percentage of biological samples for which more than 50 percent of technical replicates have a signal and median PID or CV. QRscore=Percent good samples/median PID or CV
apLCMS.ind.res	List with results from individual parameter settings.
apLCMS.ind.res.filtered	List with results from individual parameter settings after filtering.
annot.res	List with annotation results after merging results.
feat.eval.ind	List with feature evaluation results based on CV or PID at each parameter setting.
sample.eval.ind	List with sample evaluation results at each parameter setting based on correlation between technical replicates.

Outputs the following to apLCMS.outloc: -apLCMS results at each parameter settings Outputs the following to xMSanalyzer.outloc: -feature consistency results -sample evaluation results -feature list after merging results from parameter settings A and B -merge summary

Author(s)

Karan Uppal <[e-mail]>

BowenNCSU/xMSanalyzer documentation built on May 24, 2019, 7:36 a.m.