Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays
Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays
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DOI：
10.1109/jstsp.2008.924384
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发表时间：
2008-06-01
影响因子：
7.5
通讯作者：
Hassibi, Babak
中科院分区：
文献类型：
--
作者：
Parvaresh, Farzad;Vikalo, Haris;Hassibi, Babak
微阵列（DNA、蛋白质等）是大规模并行的基于亲和性的生物传感器，能够同时检测和定量大量不同的基因组颗粒。其中，DNA微阵列包括数以万计的探针点，目前被用来测试在一个单一的实验中的多个目标。在传统的微阵列中，每个点包含设计用于捕获单个靶标的单个探针的大量拷贝，因此仅收集单个数据点。这是比较DNA微阵列实验中传感资源的浪费使用，其中测试样品相对于参考样品进行测量。通常，两个样品所代表的基因总数中只有一小部分差异表达，因此，大量的探针点可能无法提供任何有用的信息。为此，我们提出了一种替代设计，即所谓的压缩微阵列，其中每个点包含几个不同探针的副本，并且点的总数可能远小于所测试的目标数量。更少的斑点直接转化为显著更低的成本，这是由于更便宜的阵列制造、更简单的图像获取和处理以及实验所需的更少量的基因组材料。为了从压缩的微阵列测量中恢复信号，我们利用压缩采样的想法。对于稀疏测量矩阵，我们提出了一种算法，具有显着降低计算复杂度比广泛使用的线性规划为基础的方法，也可以恢复信号的稀疏性较低。
Microarrays (DNA, protein, etc.) are massively parallel affinity-based biosensors capable of detecting and quantifying a large number of different genomic particles simultaneously. Among them, DNA microarrays comprising tens of thousands of probe spots are currently being employed to test multitude of targets in a single experiment. In conventional microarrays, each spot contains a large number of copies of a single probe designed to capture a single target, and, hence, collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Typically, only a fraction of the total number of genes represented by the two samples is differentially expressed, and, thus, a vast number of probe spots may not provide any useful information. To this end, we propose an alternative design, the so-called compressed microarrays, wherein each spot contains copies of several different probes and the total number of spots is potentially much smaller than the number of targets being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. For sparse measurement matrices, we propose an algorithm that has significantly lower computational complexity than the widely used linear-programming-based methods, and can also recover signals with less sparsity.