metabnet: metabnet

Description Usage Arguments Details Value Author(s)

View source: R/metabnet.R

Description

Function for correlation (complete or partial) based metabolome-wide network analysis. Additionally, users have the option to provide a matrix of m/z features corresponding to chemicals of interest such as (phenylalanine, choline, etc) and/or a matrix of m/z features corresponding to discriminatory metabolites.

Usage

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metabnet(feature_table_file, target.metab.file, sig.metab.file,
 class_labels_file = NA, parentoutput_dir, num_replicates = 1,
 cor.method = "spearman", abs.cor.thresh = 0.4, cor.fdrthresh = 0.05,
 target.mzmatch.diff = 10, target.rtmatch.diff = NA, max.cor.num = 100, 
feat.filt.thresh = NA, summarize.replicates = TRUE, summary.method = "mean", all.missing.thresh = 0.5, group.missing.thresh = 0.7, log2transform = TRUE,
 medcenter = TRUE, znormtransform = FALSE, quantile_norm = TRUE,
 lowess_norm = FALSE, madscaling = FALSE, missing.val = 0, 
networktype = "complete", samplermindex = NA, rep.max.missing.thresh = 0.3, summary.na.replacement = "zeros", net_node_colors = c("pink", "skyblue"), 
net_legend = FALSE, netrandseed = 555, TIC_norm = FALSE, 
normalization.method = c("none"), input.intensity.scale = "raw")

Arguments

feature_table_file

Feature table that includes the mz, retention time, and measured intensity in each sample for each analyte. The first 2 columns should be the mz and time. The remaining columns should correspond to the samples in the class labels file with each column including the intensity profile of a sample. Full path required. Eg: C:/My Documents/test.txt The feature table should be in a tab-delimited format. An example of the input file is provided under the "example" folder.

target.metab.file

File that includes the mz and/or retention time of the targeted metabolites corresponding to pathways or chemicals of interest. See example.

sig.metab.file

File that includes the mz and/or retention time of the discriminatory metabolites. See example.

class_labels_file

File with class labels information for each sample. Samples should be in the same order as in the feature table. Please use the same format as in the example folder.

parentoutput_dir

Provide full path of the folder where you want the results to be written. Eg: C:/My Documents/ProjectA/results/

num_replicates

Number of technical replicates

cor.method

Correlation method. Options: "pearson" or "spearman". Default: "spearman"

abs.cor.thresh

Absolute Pearson correlation coefficient for network analysis. Eg: 0.5

cor.fdrthresh

False discovery rate threshold for correlation analysis. Eg: 0.05

target.mzmatch.diff

+/- ppm mass tolerance for searching the target m/z in the current feature table

target.rtmatch.diff

+/- retention time tolerance for searching the target m/z in the current feature table

max.cor.num

Maximum number of correlated metabolites to be included in the network figure. Default: 100

feat.filt.thresh

Percent Intensity Difference or Coefficient of variation threshold; feature filtering Use NA to skip this step.

summarize.replicates

Do the technical replicates per sample need to be averaged or median summarized?

summary.method

Method for summarizing the replicates. Options: "mean" or "median"

summary.na.replacement

How should the missing values be represented? Options: "zeros", "halfsamplemin","halfdatamin", "none" "zeros": replaces missing values by 0 "halfsamplemin": replaces missing value by one-half of the lowest signal intensity in the corresponding sample "halfdatamin": replaces missing value by one-half of the lowest signal intensity in the complete dataset "none": keeps missing values as NAs

Users are recommended to perform imputation prior to performing biomarker discovery.

all.missing.thresh

What propotion of total number of samples should have an intensity? Default: 0.5

group.missing.thresh

What propotion of samples in either of the two groups should have an intensity? If at least x for further analysis. Default: 0.7

log2transform

Data transformation: Please refer to http://www.biomedcentral.com/1471-2164/7/142 Try different combinations; such as log2transform=TRUE, znormtransfrom=FALSE or log2transform=FALSE, znormtransfrom=TRUE

medcenter

Median centering of metabolites

znormtransform

Auto scaling; each metabolite will have a mean of 0 and unit variance

quantile_norm

Performs quantile normalization. Normalization options: Please set only one of the options to be TRUE

lowess_norm

Performs lowess normalization. Normalization options: Please set only one of the options to be TRUE

madscaling

Performs median adjusted scale normalization. Normalization options: Please set only one of the options to be TRUE

missing.val

How are the missing values represented in the input data? Options: "0" or "NA"

networktype

Options: "complete" or "GGM" "complete": performs network analysis using ordinary Pearson or Spearman correlation statistic "GGM": generates network based on partial correlation analysis using the GeneNet package

samplermindex

Column index of any additional or irrelevant columns to be deleted. Options: "NA" or list of column numbers. eg: c(1,3,4) Default=NA

rep.max.missing.thresh

What propotion of replicates are allowed to have missing values during the averaging or median summarization step of each biological sample? If the number of replicates with missing values is greater than the defined threshold, then the summarized value is represented by the "missing.val" parameter. If the number of replicates with missing values is less than or equal to the defined threshold, then the summarized value is equal to the mean or the median of the non-missing values. Default: 0.5

net_node_colors

Colors of nodes in the correlation networks. Eg: c("pink", "skyblue"), or ("red","green")

net_legend

Should the network be displayed for the correlation network? eg: TRUE or FALSE

Details

Function for metabolomic network analysis

Value

Correlation matrix and network of metabolites.

Author(s)

Karan Uppal <kuppal2@emory.edu>


kuppal2/xmsPANDA documentation built on May 15, 2021, 5:48 a.m.