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% Simulate a single case of carriers reconstruction
% out of 1500 individuals.
% We use 200 pools, and assume that the carrier rate is 1%
%=============================
clear
% simulation parameters:
n_samples = 1500; %number of individual to be screened.
k_pools = 200; % number of pools we use.
s = 0.01*1500; % number of carriers within n_samples.
sigma_pipette = 0.1; % The actual amount of DNA taken from a
% certain individual is a Gaussian random variable.
read_error_prob = 0.01; % probability for an incorrect read by the machine
mean_reads = 4*10^6/100;
% Interpretation #1 : mean number of reads per SNP in a lane.
% In this case we simulate the case in which we
% sequence 100 different loci,
% hence the total number of reads (4*10^6) is divided by 100.
% Interpretation #2 :
% We may also sequence a single SNP,
% but mark each pool by a different barcode.
% Hence, this simulation corresponds to sequencing,
% on the same lane, 100 different pools,
% each marked by a different barcode.
% Sequencing in this case is targeted to a single SNP.
% simulate a Bernoulli(0.5) sensing matrix
[x,fractionalOutput,discreteOutput] = simulateCSseq(n_samples, k_pools, s, mean_reads, sigma_pipette, read_error_prob);
displaySimulationParameters(n_samples,k_pools,s,mean_reads,sigma_pipette,read_error_prob,'Simulating the Bernoulli 0.5 case.')
disp(['Hamming distance between the correct and reconstructed genotypes: ',num2str(length(find(x-discreteOutput)))])
disp('==============')
% simulates a square-root (N) sensing matrix
sqrtFlag = 1;
[x,fractionalOutput,discreteOutput] = simulateCSseq(n_samples, k_pools, s, mean_reads, sigma_pipette, read_error_prob,sqrtFlag);
displaySimulationParameters(n_samples,k_pools,s,mean_reads,sigma_pipette,read_error_prob,'Simulating the sqrt(N) case.')
disp(['Hamming distance between the correct and reconstructed genotypes: ',num2str(length(find(x-discreteOutput)))])
disp('==============')
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