dquantifynormals.m

利用蚁群算法和PSO算法实现MALDI_TOF数据分析

%%
close all; clear all;clc
load maldi3class_binned
load casecirr_3030_prepro
load casecirr_249wind

% 1-78    : class 1
% 79-150  : class 2
% 151-201 : class 3

normals = Y(:,79:150) ;

%% BASELINE CORRECT and NORMALIZE NORMALS (CONTROLS)
Y = normals;

YB1 = msbackadj(MZ,Y,'QUANTILEVALUE',0,'WINDOWSIZE',50,'STEPSIZE',50,...
    'REGRESSIONMETHOD','spline','SMOOTHMETHOD','rlowess');
YB2 = msbackadj(MZ,YB1,'QUANTILEVALUE',0,'WINDOWSIZE',50,'STEPSIZE',50,...
    'REGRESSIONMETHOD','spline','SMOOTHMETHOD','rlowess');
YB = msbackadj(MZ,YB2,'QUANTILEVALUE',0,'WINDOWSIZE',50,'STEPSIZE',50,...
    'REGRESSIONMETHOD','spline','SMOOTHMETHOD','rlowess');

% Normalization of Normals using parameters used to normalize case and
% cirrhosis
YN_Norm  = msnorm(MZ,YB,P);

%% QUANTIFY PEAKS IN CONTROLS

m  = size(YN_Norm,2);

% TAKE THE MAXIMUM OF THE INTENSITIES IN THE IDENTIFIED WINDOW

sample = YN_Norm;
max_win = [];
for i = 1: length(win_tick)
    if win_tick(i,1) == win_tick(i,2)
        a=sample(win_tick(i,1),:);
    else
        a = max(sample(win_tick(i,1):win_tick(i,2),: )); % sum/max
    end
    max_win = [max_win;  a];
end

YNorm = max_win;

save  Controls72 YNorm windows;