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PPISR_linux.m
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PPISR_linux.m
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function []=PPISR_linux(varargin)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ISR methods for GWAS @AUTHOR MENG LUO %
% contact: [email protected] %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%seting the env path
% alias matlab='/mnt/d/linux/MATLAB2016b/bin/matlab -nodesktop -nosplash -singleCompThread -logfile `date +%Y_%m_%d-%H_%M_%S`.log -r'
addpath([pwd,'/src/']);
%prediction=ft_getopt(varargin, 'prediction', 'no');
method=ft_getopt(varargin, 'method', 'randomly');%cross validation or 10 fold
matfile=ft_getopt(varargin, 'matfile', []);
phefile = ft_getopt(varargin, 'phefile', 'phe.fam');
genofile = ft_getopt(varargin, 'genofile', 'pop.traw');
outfile = ft_getopt(varargin, 'outfile', 'pop.traw.mat');
sample = ft_getopt(varargin, 'sample',[]);
nSNP = ft_getopt(varargin, 'nSNP', []);
ntrait = ft_getopt(varargin, 'ntrait', 1);
chr = ft_getopt(varargin, 'nchr', []);
opt_outresult = ft_getopt(varargin, 'opt_outresult', 'ISR.opt.outresult.txt');
all_outresult = ft_getopt(varargin, 'all_outresult', 'ISR.outresult.txt');
vcf = ft_getopt(varargin, 'vcf', []);
bed = ft_getopt(varargin, 'bed', []);
ncov = ft_getopt(varargin, 'ncov', []);
IM = ft_getopt(varargin, 'IM', 1);
sgv = ft_getopt(varargin, 'sgv', 0.05); % default bonferroni correction
mdl = ft_getopt(varargin, 'model',1);
%matfile = string, the matlab data format if you already prepare you data
%phefile = string, can be any of file format split with "\t"(default = 'phe.fam')
%genofile = string, .traw file format from plink (default = 'pop.traw')
%outfile = string, save covert genotypes file name with any name you defined and save matlab format (default = 'pop.traw.mat')
%sample = number, the number of individuals you want to analysis
%nSNP = number, the number of SNPs.
%ntrait = number, the number of traits.
%chr = number, the number of chromosome.
%opt_outresult = string, write the result to text file (default = 'ISR.opt.outresult.txt')
%all_outresult = string, write the result to text file (default = 'ISR.outresult.txt')
%vcf = string, the VCF file name.
%bed = string, the bed file name.
%ncov = number, the number of PCs covariates.
%IM = impute missing genotype with mean and median value, '1' was the default method means and others was median.
%sgv = number, the bonferroni correction for association tests results.
%mdl = number,1 for linear model and 2 or 3 for nolinear model ; input('Using Model II(without square term 2) or Model III(with square term 3) 2/3? ');
% Usage:
% matlab "PPISR_linux('phefile','../data/pop.fam','genofile','../data/pop.traw','sample',87,'nSNP',28228,'ntrait',1,'ncov',5),exit;"
% matlab "PPISR_linux('matfile','demo.mat','sample',798,'nSNP',92641),exit;"
%default parameter
%if ~exist('outfile')
% outfile = 'pop.traw.mat';
%end
%if ~exist('opt_outresult')
% opt_outresult = 'opt_outresult.txt';
%end
%if ~exist('all_outresult')
% all_outresult = 'all_outresult.txt';
%end
%maxNumCompThreads=1;
% write a shell script for ISR getopt....
%clear;clc;
%maxNumCompThreads(1)=1;
if ~isempty(vcf)
system(['plink ',' --vcf ',vcf,' --recode A-transpose --out pop']);
traw2mat(phefile,genofile,outfile,sample,nSNP,ntrait,IM);
load(outfile);
elseif ~isempty(bed)
system(['plink ',' --bfile ',bed,' --recode A-transpose --out pop ']);
traw2mat(phefile,genofile,outfile,sample,nSNP,ntrait,IM);
load(outfile);
elseif ~isempty(matfile)
load(matfile)
else
traw2mat(phefile,genofile,outfile,sample,nSNP,ntrait,IM);
load(outfile);
end
%traw2mat('../../pop.fam','../../poptraw.traw','popnew.mat',175,407045,3)
Y=y;X=x;
[~,p1]=size(Y);
if method=='randomly'
%clear,clc
warning('off','all')
diary('ISR.log'); % notes the command window diary
yname=2;%no change the name of phenotype
impute=2;
alfa=0.001;
glm=3;%the number of running default 3
ept0=25;%initially random chosed the number of multi-locus (probable result) default 5
gr=2;%export the result of significant association SNP genotype (with gr=1)
nchr=chr;%the number of Chromosome
sgt=sgv;% bonferroni correction
%y=Y(:,p1);
N = size(x,1);
sq=randperm(N);testing=sq(1:round(N*0.2));trainning=setdiff(sq,testing);
%for i=1:nfolds
%which=foldid==i;
%if verbous, disp(['Fitting fold # ' num2str(i) ' of ' num2str(nfolds)]);end
% x=reperma
x=X(trainning,:);y=Y(trainning,1);xte=X(testing,:);xtr=X(trainning,:);
%find the MISSING DATA add to the program
% [n,p]=size(x);
% for i=1:n
% for j=1:p
% if isnan(x(i,j))
% disp([i,j,x(i,j)])
% end
% end
% end
%stop
% ISR method
if mdl==2
if ~isempty(ncov)
ISR_Epi_COV
else
ISR_Epi
end
else
if ~isempty(ncov)
ISR_COV
else
ISR_REG
end
end
%changing possible the last association results
%writetable(opt_result,opt_outresult,'Delimiter','\t');writetable(all_result,all_outresult,'Delimiter','\t');
diary off
elseif method=='fold'
impute=2;yname=2;
diary(['isr',num2str(i),'.log']); % notes the command window diary
alfa=0.001;
glm=3;%the number of running
ept0=25;%initially random chosed the number of multi-locus (probable result)
gr=2;%export the result of significant association SNP genotype (with gr=1)
nchr=chr;%the number of Chromosome
sgt=sgv;
y=Y(:,p1);
%find the MISSING DATA add to the program
% [n,p]=size(x);
% for i=1:n
% for j=1:p
% if isnan(x(i,j))
% disp([i,j,x(i,j)])
% end
% end
% end
%stop
% ISR method
if mdl==2
if ~isempty(ncov)
ISR_Epi_COV
else
ISR_Epi
end
else
if ~isempty(ncov)
ISR_COV
else
ISR_REG
end
end
%changing possible the last association results
writetable(opt_result,opt_outresult,'Delimiter','\t');writetable(all_result,all_outresult,'Delimiter','\t');
diary off
end
end