epeakscreening.m

利用蚁群算法和PSO算法实现MALDI_TOF数据分析

clear all;clc

% Covariates
% 1.gender 2.age 3.residency 4.anti_HCV	5.HCV_RNA 6.anti_HBv
% 7.ever smoked cigarettes	

load Controls72 
load normalCovariates

% The 72 controls with windows and the covariates information

%--------------------------------------

X = covar;
n = size(windows,1);
controls = YNorm';

pvalue =[];
for i = 1: n
    y = controls(:,i);
    stats = regstats(y,X,'linear');
    pvalue =[pvalue stats.tstat.pval];
end
pvalue = pvalue(2:8,:);

%%

ind = size(covar,2);
pval = [];
for k =1:ind
    y = covar(:,k);
    pva = [];
    for i = 1: n
        X = controls(:,i);
        [b,dev,stats] = glmfit(X,y,'binomial');
        pva =[pva; stats.p(2)];
    end
    pval = [pval pva];
    
end
pval = pval';
         
%%

load casecirr_249wind

index = [];
for j= 1: size(covar,2)
    for i = 1: size(windows,1)
        if (pval(j,i) < 0.05 && pvalue(j,i) < 0.05)
            index = [index i];
        end
    end
end
Un =  unique(index);
Wn = setdiff((1:size(windows,1)),Un);
P = [ pval; ones(1,size(windows,1)); pvalue];
W = windows(Wn,:);
sample_tr = sample_tr(Wn,:);
sample_pr = sample_pr(Wn,:);
windows = W;

save CaseCirr sample_tr sample_pr label_tr label_pr windows