Recovery of Compressed Sensing Microarray Using Sparse Random Matrices

Due to the uncertainty of elements in the random matrix, the design of composite probes on compressed sensing microarray (CSM) becomes more complexity. In this paper, we proposed a sparse random measurement matrix with ‘0/1’ binary element, and fixed the same amount of elements ‘1’ on each row, to construct the CSM composite probe. There is the same dilution for the mixed solution of target segments to ensure the consistency of gene concentration, so the composite probes which made up of the linear combination of target segments are very simple. Simulation experiment results show that the variation characteristics of the target segment can be accurately recovered by OMP algorithm under N = 96 sequence segments and variation sparsity level K ≤ 12, when M = 48 composite probes are constructed with a sparse random matrix fixed amount of non-zero elements each row.