Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596

Mathematical Citation Quotient (MCQ) 2016: 0.06

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 4, Issue 1 (Apr 2005)

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Pixel-level Signal Modelling with Spatial Correlation for Two-Colour Microarrays

Claus T Ekstrøm / Søren Bak
  • Dept. of Plant Biology and Center of Molecular Plant Physiology (PlaCe), Royal Veterinary and Agricultural University
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Mats Rudemo
Published Online: 2005-04-06 | DOI: https://doi.org/10.2202/1544-6115.1112

Statistical models for spot shapes and signal intensities are used in image analysis of laser scans of microarrays. Most models have essentially been based on the assumption of independent pixel intensity values, but models that allow for spatial correlation among neighbouring pixels can accommodate errors in the microarray slide and should improve the model fit. Five spatial correlation structures, exponential, Gaussian, linear, rational quadratic and spherical, are compared for a dataset with 50-mer two-colour oligonucleotide microarrays and 452 probes for selected Arabidopsis genes. Substantial improvement in model fit is obtained for all five correlation structures compared to the model with independent pixel values, and the Gaussian and the spherical models seem to be slightly better than the other three models. We also conclude that for the data set analysed the correlation seems negligible for non-neighbouring pixels.

Keywords: spotted array; spatial correlation; censored data; polynomial-hyperbolic model

About the article

Published Online: 2005-04-06


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1112.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Marc Morant, Claus Ekstrøm, Peter Ulvskov, Charlotte Kristensen, Mats Rudemo, Carl Erik Olsen, Jørgen Hansen, Kirsten Jørgensen, Bodil Jørgensen, Birger Lindberg Møller, and Søren Bak
Molecular Plant, 2010, Volume 3, Number 1, Page 192
[2]
Gerard R. Ridgway and Simon J. Godsill
IEEE Signal Processing Letters, 2007, Volume 14, Number 10, Page 653

Comments (0)

Please log in or register to comment.
Log in