+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Normalization of microarray data using a spatial mixed model analysis which includes splines

Normalization of microarray data using a spatial mixed model analysis which includes splines

Bioinformatics 20(17): 3196-3205

Microarray experiments with thousands of genes on a slide and multiple slides used in any experimental set represent a large body of data with many sources of variation. The identification of such sources of variation within microarray experimental sets is critical for correct deciphering of desired gene expression differences. We describe new methods for the normalization using spatial mixed models which include splines and analysis of two-colour spotted microarrays for within slide variation and for a series of slides. The model typically explains 45-85% of the variation on a slide with only approximately 1% of the total degrees of freedom. The results from our methods compare favourably with those from intensity dependent normalization loess methods where we accounted for twice as much uncontrolled and unwanted variation on the slides. We have also developed an index for each EST that combines the various measures of the differential response into a single value that researchers can use to rapidly assess the genes of interest.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 012362156

Download citation: RISBibTeXText

PMID: 15231532

DOI: 10.1093/bioinformatics/bth384

Related references

Normalization of dye bias in microarray data using the mixture of splines model. Statistical Applications in Genetics and Molecular Biology 6: Article2, 2007

The Analysis of Longitudinal Data Using Mixed Model L-Splines. Biometrics 62(2): 392-401, 2006

The analysis of longitudinal data using mixed model L-splines. Biometrics 62(2): 392-401, 2006

Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data. Statistical Methods in Medical Research 13(1): 63-82, 2004

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data. Bioinformatics 21(11): 2674-2683, 2005

Microarray Data Analysis Toolbox (MDAT): for normalization, adjustment and analysis of gene expression data. Bioinformatics 20(18): 3687-3690, 2004

Variance component estimation for mixed model analysis of cDNA microarray data. Biometrical Journal. Biometrische Zeitschrift 50(6): 927-939, 2009

Bayesian estimation of a surface to account for a spatial trend using penalized splines in an individual-tree mixed model. Canadian Journal of Forest Research 37(12): 2677-2688, 2007

SNOMAD (Standardization and NOrmalization of MicroArray Data): web-accessible gene expression data analysis. Bioinformatics 18(11): 1540-1541, 2002

Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics 19(4): 474-482, 2003

Linear mixed model selection for false discovery rate control in microarray data analysis. Biometrics 66(2): 621-629, 2010

The effect of normalization on microarray data analysis. Dna and Cell Biology 23(10): 635-642, 2004

A robust two-way semi-linear model for normalization of cDNA microarray data. Bmc Bioinformatics 6: 14-14, 2005

Model selection and efficiency testing for normalization of cDNA microarray data. Genome Biology 5(8): R60, 2004

SED, a normalization free method for DNA microarray data analysis. Bmc Bioinformatics 5: 121, 2004