evaluate.Samples: evaluate.Samples

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

Description

Evaluate sample consistency based on Pearson or Spearman Correlation.

Usage

evaluate.Samples(curdata, numreplicates, alignment.tool, cormethod = "pearson", 
    missingvalue = 0, ignore.missing = TRUE, replace.bad.replicates = TRUE)

Arguments

curdata	feature alignment output matrix from apLCMS or XCMS with intensities
numreplicates	number of technical replicates per sample
alignment.tool	name of the feature alignment tool eg: "apLCMS" or "XCMS"
cormethod	Pearson or Spearman correlation.
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
replace.bad.replicates	Should the bad replicates be replaced by the average of the good ones? For example, if the number of technical replicates is more than two, and one of the replicates is poorly correlated with the other two but the other replicates have correlation greater than the defined threshold, then the bad replicate is replaced by the average of the good ones.

Details

If at least two analytical replicates are present for each biological sample, this function calculates the mean pairwise Pearson correlation coefficient between sample replicates using the built-in cor() function in R. Only the features with no missing values are used to evaluate correlation. Analytical replicates refer to multiple injections from the same biological sample; whereas, samples refer to different biological samples.

Value

returns a matrix of Pearson or Spearman Correlation Coefficients within technical replicates per sample.

Author(s)

Karan Uppal <[e-mail]>

yufree/xMSanalyzer documentation built on May 4, 2019, 6:35 p.m.