DNA Array Decoding from Nonlinear Measurements by Belief Propagation

Sheikh, Mona; Sarvotham, Shriram; Milenković, Olgica; Baraniuk, Richard G.

doi:10.1109/ssp.2007.4301250

Cited by 29 publications

(22 citation statements)

References 9 publications

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“…These include traditional randomly matrices (Yok and Rosen, 2008;Tan et al, 2012), Hamming matrices (Dai et al, 2009), and low-density parity check (LDPC) matrices (Sheikh et al, 2007). As a novelty, we have considered projective geometry (PG)-based matrices and compared these against the performance of the aforementioned matrices (Dai et al, 2009;Yok and Rosen, 2008;Tan et al, 2012), to evaluate their performance when different recovery methods are used.…”

Section: Measurement Matricesmentioning

confidence: 99%

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Compressed Sensing Methods for DNA Microarrays, RNA Interference, and Metagenomics

Rao

Deepthi²,

Renumadhavi³

et al. 2015

Journal of Computational Biology

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Compressed sensing (CS) is a sparse signal sampling methodology for efficiently acquiring and reconstructing a signal from relatively few measurements. Recent work shows that CS is well-suited to be applied to problems in genomics, including probe design in microarrays, RNA interference (RNAi), and taxonomic assignment in metagenomics. The principle of using different CS recovery methods in these applications has thus been established, but a comprehensive study of using a wide range of CS methods has not been done. For each of these applications, we apply three hitherto unused CS methods, namely, l1-magic, CoSaMP, and l1-homotopy, in conjunction with CS measurement matrices such as randomly generated CS m matrix, Hamming matrix, and projective geometry-based matrix. We find that, in RNAi, the l1-magic (the standard package for l1 minimization) and l1-homotopy methods show significant reduction in reconstruction error compared to the baseline. In metagenomics, we find that l1-homotopy as well as CoSaMP estimate concentration with significantly reduced time when compared to the GPSR and WGSQuikr methods.

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Section: Measurement Matricesmentioning

confidence: 99%

“…Furthermore, it has been reported that the signal acquisition model becomes nonlinear due to nonlinearity in measurement intensities for a single spot-single target situation (Sheikh et al, 2007). Steps to solve nonlinearity were taken from Dai et al (2009). …”

Section: Compressed Sensing Microarraymentioning

confidence: 99%

Compressed Sensing Methods for DNA Microarrays, RNA Interference, and Metagenomics

Rao

Deepthi²,

Renumadhavi³

et al. 2015

Journal of Computational Biology

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“…In CDMA systems [4] the Ψ and P Y |X corresponding to the channel model and noise may only be known to be in a certain class, or the true system parameters may change over time. In applications such as group testing [11] and DNA micro-arrays [14], similar problems of uncertainty and mismatch can arise. Motivated by these examples, we analyze the effect of particular fixed measurement functions, noise and especially uncertainty and mismatch in the noise model P Y |X and measurement function Ψ.…”

Section: Focus Of This Papermentioning

confidence: 99%

Robust detection of random variables using sparse measurements

Balakrishnan

Negi

Khosla

2009

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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We look at the problem of estimating k discrete random variables from n noisy and sparse measurements where k = nR, with a 'rate' R. The model is motivated by problems studied in diverse areas including compressed sensing, group testing, multiple access channels and sensor networks. In particular, we study uncertainty and mismatch in the measurement functions and the noise model and quantify the effect of these faults on detection performance, in the large system limit as n → ∞, while R remains constant. We characterize the performance of mismatched and uncertain detectors, design and analyze robust detectors and present an illustrative example where the analysis presented can be used to guide the design of robust measurement ensembles.

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“…In this paper we focus on the former, i.e., we propose and study compressed microarrays manufactured by probe spotting, and -optimization techniques for a sparse signal recovery therein. On the other hand, recent work [38] proposes the design of probes, each of which can potentially capture several different targets, and employs the belief propagation approach to facilitate sparse signal recovery. The design of probes in [38] can be quite challenging; in particular, balancing probes selectivity, specificity, as well as performing array calibration (i.e., determining the strength of binding of each target analyte to its corresponding probe) can be problematic.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, recent work [38] proposes the design of probes, each of which can potentially capture several different targets, and employs the belief propagation approach to facilitate sparse signal recovery. The design of probes in [38] can be quite challenging; in particular, balancing probes selectivity, specificity, as well as performing array calibration (i.e., determining the strength of binding of each target analyte to its corresponding probe) can be problematic. Our approach, however, employs already-designed probe sets and simply requires mixing a number of different probes prior to spotting them on an array-a procedure which is readily feasible.…”

Section: Introductionmentioning

confidence: 99%

Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays

Parvaresh

Vikalo

Misra

et al. 2008

IEEE J. Sel. Top. Signal Process.

190

129

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Abstract-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.

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DNA Array Decoding from Nonlinear Measurements by Belief Propagation

Cited by 29 publications

References 9 publications

Compressed Sensing Methods for DNA Microarrays, RNA Interference, and Metagenomics

Compressed Sensing Methods for DNA Microarrays, RNA Interference, and Metagenomics

Robust detection of random variables using sparse measurements

Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays

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