Description Usage Arguments Value Author(s)
Evaluate feature consistency based on coefficient of variation, where cv=sd/mean. Only calculate CV for samples for which a signal is detected in at least 60 If signal is detected in 60 which is calculated using non-missing replicates.
1 2 | getCVreplicates(curdata, alignment.tool, numreplicates, min.samp.percent = 0.6,
impute.bool = TRUE, missingvalue)
|
curdata |
Feature alignment output matrix from apLCMS or XCMS with sample intensities |
alignment.tool |
Name of the feature alignment tool eg: "apLCMS" or "XCMS" |
numreplicates |
Number of replicates per sample |
min.samp.percent |
If signal is detected in x proportion of technical replicates, then the missing values are replaced by mean intensity which is calculated using non-missing replicates. eg: 0.7 |
impute.bool |
Should the missingvalues be replaced by mean of the other replicates? eg: TRUE or FALSE |
missingvalue |
How are the missing values represented? eg: 0 or NA |
Matrix of feature consistency based on CV with columns: mz: m/z of the feature minCV: minimum CV between technical replicates across all samples first_quartileCV: 25th percentile CV medianCV: 50th percentile CV meanCV: average of cofficient of variations between technical replicates per sample across all samples third_quartileCV: 75th percentile CV maxCV: maximum CV between technical replicates across all samples
Karan Uppal <kuppal2@emory.edu>
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