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OmicsMbqnMatlab.m
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OmicsMbqnMatlab.m
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% The follwing steps are done by MBQN
%
% 1) calculation of rowmeans (via meanfun)
% 2) subtraction of the rowmeans
% 3) ordinary quantile normalization
% 4) adding the previously subtracted row means
%
% whereImpute 1: means imputation is exclusively done before step 1)
% and withdraw after step 1)
%
% 2 means imputation is exclusively done before step 1)
% and withdraw after step 2)
%
% 0 without imputation [Default]
%
% qnMean Sollen die means (offsets) selber auch quantlienormalisiert
% werden? Dann sehen boxplots später eher wie qn aus.
% [false] Default (wegen kompatibiltität)
function O = OmicsMbqnMatlab(O,whereImpute,meanfun,qnMean)
if ~exist('meanfun','var') || isempty(meanfun)
meanfun = 'nanmedian';
end
if ~exist('qnMean','var') || isempty(qnMean)
qnMean = false;
end
if ~exist('whereImpute','var') || isempty(whereImpute)
whereImpute = 0; % no imputation
end
dat = get(O,'data');
if whereImpute>0
isna = isnan(dat);
dat = mice(dat,'cart');
end
m = NaN(size(dat,1),1);
for i=1:size(dat,1)
m(i) = feval(meanfun,dat(i,:));
end%
if whereImpute==1
dat(isna) = NaN;
end
if qnMean
mmatrix = m*ones(1,size(dat,2));
mmatrix(isnan(get(O,'data'))) = NaN; % deswegen kann MBQN boxplot erzeugen, der nicht nach QN aussieht
mmQn = quantilenorm(mmatrix);
disp('Balancing is done under quantilenorm-contraint.')
offsets = mmQn;
else
offsets = m*ones(1,size(dat,2));
end
dat = dat-offsets;
dat = quantilenorm(dat);
if whereImpute==2
dat(isna) = NaN;
end
dat = dat+offsets;
O = set(O,'data',dat,['Custom MBQN Normalization implemented in Matlab, imputation option ',num2str(whereImpute)]);