metabnet: metabnet

In kuppal2/xmsPANDA: R Package for Biomarker Discovery, Network, and Data Exploratory Analysis

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

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 <[e-mail]>

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