This function calculates the column-wise coefficient of variation
(CV), i.e. the ration between the standard deviation and the
mean, for the features in an MSnSet. The CVs are calculated
for the groups of features defined by groupBy. For groups
defined by single features, NA is returned.
featureCV(x, groupBy, na.rm = TRUE, norm = "none", suffix = NULL)An instance of class MSnSet.
An object of class factor defining how to
summarise the features.
A logical(1) defining whether missing values should
be removed.
One of normalisation methods applied prior to CV
calculation. See normalise() for more details. Here, the
default is 'none', i.e. no normalisation.
A character(1) to be used to name the new CV
columns. Default is NULL to ignore this. This argument
should be set when CV values are already present in the
MSnSet feature variables.
A matrix of dimensions length(levels(groupBy)) by
ncol(x) with the respecive CVs. The column names are formed
by pasting CV. and the sample names of object x, possibly
suffixed by .suffix.
data(msnset)
msnset <- msnset[1:4]
gb <- factor(rep(1:2, each = 2))
featureCV(msnset, gb)
#> CV.iTRAQ4.114 CV.iTRAQ4.115 CV.iTRAQ4.116 CV.iTRAQ4.117
#> 1 0.4116294 0.672121019 0.9798589 1.1049021
#> 2 0.1010699 0.005077863 0.1128963 0.2347332
featureCV(msnset, gb, suffix = "2")
#> CV.iTRAQ4.114.2 CV.iTRAQ4.115.2 CV.iTRAQ4.116.2 CV.iTRAQ4.117.2
#> 1 0.4116294 0.672121019 0.9798589 1.1049021
#> 2 0.1010699 0.005077863 0.1128963 0.2347332