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Compressive sensing DNA microarrays.

by Richard G Baraniuk
EURASIP journal on bioinformatics systems biology ()

Abstract

Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints from CS theory and the biochemistry of probe-target DNA hybridization. An appropriate cross-hybridization model is proposed for CSMs, and several methods are developed for probe design and CS signal recovery based on the new model. Lab experiments suggest that in order to achieve accurate hybridization profiling, consensus probe sequences are required to have sequence homology of at least 80% with all targets to be detected. Furthermore, out-of-equilibrium datasets are usually as accurate as those obtained from equilibrium conditions. Consequently, one can use CSMs in applications in which only short hybridization times are allowed.

Cite this document (BETA)

Available from www.pubmedcentral.nih.gov
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Compressive sensing DNA microarra...

Richard Baraniuk Rice University Compressive Sensing
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Acknowledgements ��� For assistance preparing this presentation ��� Rice DSP group ��� Petros Boufounos, Volkan Cevher ��� Mark Davenport, Marco Duarte, Chinmay Hegde, Jason Laska, Shri Sarvotham, ��� ��� Mike Wakin, University of Michigan ��� geometry of CS, embeddings ��� Justin Romberg, Georgia Tech ��� optimization algorithms
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Agenda ��� Introduction to Compressive Sensing (CS) ��� motivation ��� basic concepts ��� CS Theoretical Foundation ��� geometry of sparse and compressible signals ��� coded acquisition ��� restricted isometry property (RIP) ��� signal recovery ��� CS Applications ��� Related concepts and work
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Digital Revolution
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Pressure is on Digital Sensors ��� Success of digital data acquisition is placing increasing pressure on signal/image processing hardware and software to support higher resolution/ denser sampling �� ADCs, cameras, imaging systems, microarrays, ��� x large numbers of sensors �� image data bases, camera arrays, distributed wireless sensor networks, ��� x increasing numbers of modalities �� acoustic, RF, visual, IR, UV, x-ray, gamma ray, ���
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Pressure is on Digital Sensors ��� Success of digital data acquisition is placing increasing pressure on signal/image processing hardware and software to support higher resolution/ denser sampling �� ADCs, cameras, imaging systems, microarrays, ��� x large numbers of sensors �� image data bases, camera arrays, distributed wireless sensor networks, ��� x increasing numbers of modalities �� acoustic, RF, visual, IR, UV deluge of data deluge of data �� how to acquire, store, fuse, processefficiently?
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Digital Data Acquisition ��� Foundation: Shannon sampling theorem ���if you sample densely enough (at the Nyquist rate), you can perfectly reconstruct the original analog data��� time space
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Sensing by Sampling ��� Long-established paradigm for digital data acquisition ��� uniformly sampledata at Nyquist rate (2x Fourier bandwidth) sample too much data!
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Sensing by Sampling ��� Long-established paradigm for digital data acquisition ��� uniformly sampledata at Nyquist rate (2x Fourier bandwidth) ���compressdata compress transmit/store receive decompress sample JPEG JPEG2000 ���
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Sparsity / Compressibility pixels large wavelet coefficients (blue = 0) wideband signal samples large Gabor (TF) coefficients time
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Sample / Compress compress transmit/store receive decompress sample sparse / compressible wavelet transform ��� Long-established paradigm for digital data acquisition ��� uniformly sampledata at Nyquist rate ���compressdata
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What���s Wrong with this Picture? ���Why go to all the work to acquire Nsamples only to discard all but Kpieces of data? compress transmit/store receive decompress sample sparse / compressible wavelet transform
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What���s Wrong with this Picture? linearprocessing linearsignal model (bandlimited subspace) compress transmit/store receive decompress sample sparse / compressible wavelet transform nonlinearprocessing nonlinearsignal model (union of subspaces)
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Compressive Sensing ��� Directly acquire ��� compressed ��� data ��� Replace samples by more general ���measurements��� compressive sensing transmit/store receive reconstruct
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Compressive Sensing Theory I Geometrical Perspective

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41% Ph.D. Student
 
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9% Researcher (at a non-Academic Institution)
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31% United States
 
14% China
 
5% Germany

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