You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: DESeq2ori.m
    path of file: ./repos/scGEAToolbox/+run/external/mt_DESeq2
    
    the code of the file until where you have to start completion: function [log2FC, FDR, meanExp, pValues] = DESeq2ori(matrix1, matrix2)
% matrix1: matrix where each line represent a sample in the condition 1 in the
% format samples x genes
% matrix2: matrix where each line represent a sample in the condition 2 in the
% format samples x genes
%
% The overexpressed genes are those with a FDR<0.1 and log2FC>1 and under-expressed
%  with a FDR<0.1 and log2FC<-1
%


    
function [log2FC, FDR, meanExp, pValues] = DESeq2ori(matrix1, matrix2)
% matrix1: matrix where each line represent a sample in the condition 1 in the
% format samples x genes
% matrix2: matrix where each line represent a sample in the condition 2 in the
% format samples x genes
%
% The overexpressed genes are those with a FDR<0.1 and log2FC>1 and under-expressed
%  with a FDR<0.1 and log2FC<-1
%

disp('Normalization ...')
%[M1,N1] = size(matrix1);
geom1 = geomean(matrix1);
geom1(geom1 == 0) = 1;
norm1 = mean(bsxfun(@rdivide, matrix1, geom1), 2);
matrixN1 = bsxfun(@rdivide, matrix1', norm1')';

%[M2,N2] = size(matrix2);
geom2 = geomean(matrix2);
geom2(geom2 == 0) = 1;
norm2 = mean(bsxfun(@rdivide, matrix2, geom2), 2);
matrixN2 = bsxfun(@rdivide, matrix2', norm2')';

disp('Log2FC(mean)...')
mean1 = mean(matrixN1);
mean2 = mean(matrixN2);
meanExp = mean(cat(1, matrix1, matrix2))';

log2FC = log2(mean1./mean2)';

disp('Differential Analysis ...')

tLocal = nbintest(matrix1', matrix2', 'VarianceLink', 'LocalRegression', ...
    'SizeFactor', {norm1', norm2'});
pValues = tLocal.pValue;

if exist('mafdr.m', 'file')
    FDR = mafdr(pValues, 'BHFDR', true);
else
    [~, ~, ~, FDR] = pkg.fdr_bh(pValues);
end