Estimating gene signals from noisy microarray images

Pinaki Sarder, Arye Nehorai, Paul H Davis, Samuel L. Stanley

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

In oligonucleotide microarray experiments, noise is a challenging problem, as biologists now are studying their organisms not in isolation but in the context of a natural environment. In low photomultiplier tube (PMT) voltage images, weak gene signals and their interactions with the background fluorescence noise are most problematic. In addition, nonspecific sequences bind to array spots intermittently causing inaccurate measurements. Conventional techniques cannot precisely separate the foreground and the background signals. In this paper, we propose analytically based estimation technique. We assume a priori spot-shape information using a circular outer periphery with an elliptical center hole. We assume Gaussian statistics for modeling both the foreground and background signals. The mean of the foreground signal quantifies the weak gene signal corresponding to the spot, and the variance gives the measure of the undesired binding that causes fluctuation in the measurement. We propose a foreground-signal and shape-estimation algorithm using the Gibbs sampling method. We compare our developed algorithm with the existing Mann-Whitney (MW)- and expectation maximization (EM)/iterated conditional modes (ICM)-based methods. Our method outperforms the existing methods with considerably smaller mean-square error (MSE) for all signal-to-noise ratios (SNRs) in computer-generated images and gives better qualitative results in low-SNR real-data images. Our method is computationally relatively slow because of its inherent sampling operation and hence only applicable to very noisy-spot images. In a realistic example using our method, we show that the gene-signal fluctuations on the estimated foreground are better observed for the input noisy images with relatively higher undesired bindings.

Original languageEnglish (US)
Article number7
Pages (from-to)142-153
Number of pages12
JournalIEEE Transactions on Nanobioscience
Volume7
Issue number2
DOIs
StatePublished - Jun 1 2008

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Microarrays
Genes
Signal to noise ratio
Sampling
Oligonucleotides
Photomultipliers
Signal-To-Noise Ratio
Mean square error
Noise
Fluorescence
Statistics
Electric potential
Oligonucleotide Array Sequence Analysis
Experiments

Keywords

  • Gibbs sampling
  • Low PMT voltage image
  • Spot segmentation
  • cDNA microarray

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Pharmaceutical Science
  • Medicine (miscellaneous)
  • Biotechnology
  • Biomedical Engineering
  • Bioengineering

Cite this

Estimating gene signals from noisy microarray images. / Sarder, Pinaki; Nehorai, Arye; Davis, Paul H; Stanley, Samuel L.

In: IEEE Transactions on Nanobioscience, Vol. 7, No. 2, 7, 01.06.2008, p. 142-153.

Research output: Contribution to journalArticle

Sarder, Pinaki ; Nehorai, Arye ; Davis, Paul H ; Stanley, Samuel L. / Estimating gene signals from noisy microarray images. In: IEEE Transactions on Nanobioscience. 2008 ; Vol. 7, No. 2. pp. 142-153.
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