Description Usage Arguments Value Author(s) References Examples
Normalize data to minimize the difference among the subgroups of the samples generated by experimental factor such as multiple plates (batch effects)
 the primary method is MultiMA, but other fitting function, f in manuscript (e.g. loess) is available, too.
This method is based on the assumptions stated below
The geometric mean value of the samples in each subgroup (or plate) for a single target is ideally same as those from the other subgroups.
The subgroup (or plate) effects that influence those mean values for multiple observed targets are dependent on the values themselves. (intensity dependent effects)
1 2 
mD 
a 
expGroup 
a 
represent_FUN 
a 
fitting_FUN 

isLog 
TRUE or FALSE, if the normalization should be conducted after logtransformation. The affinity proteomics data from suspension bead arrays is recommended to be normalized using the default, 
The data after normalization in a matrix
MunGwan Hong <mungwan.hong@scilifelab.se>
Hong MG, Lee W, Pawitan Y, Schwenk JM (201?) Multidimensional normalization of plate effects for multiplexed applications unpublished
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  data(sba)
B < normn_MA(sba$X, sba$plate) # MultiMA normalization
# MAloess normalization
B < normn_MA(sba$X, sba$plate, fitting_FUN= function(m_j, A) loess(m_j ~ A)$fitted)
# weighted linear regression normalization
B < normn_MA(sba$X, sba$plate, fitting_FUN= function(m_j, A) {
beta < lm(m_j ~ A, weights= 1/A)$coefficients
beta[1] + beta[2] * A
})
# robust linear regression normalization
if(any(search() == "package:MASS")) { # excutable only when MASS package was loaded.
B < normn_MA(sba$X, sba$plate, fitting_FUN= function(m_j, A) {
beta < rlm(m_j ~ A, maxit= 100)$coefficients
beta[1] + beta[2] * A
})
}

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