A new non-linear normalization method for reducing variability in DNA microarray experiments

By C. Workman, Lars Juhl Jensen, H. Jarmer, R. Berka, Laurent Gautier, H. B. Nielser, H. Saxild, C. Nielsen, Søren Brunak, S. Knudsen. Genome Biology, 2002

580 citations recorded by the source corpus.

BackgroundMicroarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy5 and Cy3 as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneChips) and cDNA microarray data. Current normalization techniques are most often linear and therefore not capable of fully correcting for these effects.ResultsWe present here a simple and robust non-linear method for normalization using array signal distribution analysis and cubic splines. These methods compared favorably to normalization using robust local-linear regression (lowess). The application of these methods to oligonucleotide arrays reduced the relative error between replicates by 5-10% compared with a standard global normalization method. Application to cDNA arrays showed improvements over the standard method and over Cy3-Cy5 normalization based on dye-swap replication. In addition, a set of known differentially regulated genes was ranked higher by the t-test. In either cDNA or Affymetrix technology, signal-dependent bias was more than ten times greater than the observed print-tip or spatial effects.ConclusionsIntensity-dependent normalization is important for both high-density oligonucleotide array and cDNA array data. Both the regression and spline-based methods described here performed better than existing linear methods when assessed on the variability of replicate arrays. Dye-swap normalization was less effective at Cy3-Cy5 normalization than either regression or spline-based methods alone.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bbf432233475ba1fa6f19cb224c2e93279fc6519

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Citations in the corpus

NOREVA: normalization and evaluation of MS-based metabolomics data (2017, methodology): “The advantages of the RUV-2 model include 18 : (1) the biological factors of interest are not removed along with the unwanted variation; (2) the method is applied to datasets without internal standards; (3) all unwanted biological variation can be accommodated; (4) it allows for the systematic integ”
Whole-genome analysis of histone H3 lysine 4 and lysine 27 methylation in human embryonic stem cells. (2007, methodology): “Briefly, after scanning and image extraction, IP and Input signal values for each of the 38 arrays were normalized according to Qspline (Workman et al., 2002).”
A novel and universal method for microRNA RT-qPCR data normalization (2009, unknown): “ant normalization [13].”
A benchmark for Affymetrix GeneChip expression measures (2004, result): “Similar or even identical descriptive methods have been used by a variety of researchers to evaluate measures of expression (Holder et al., 2001; Workman et al., 2002; Irizarry et al., 2003a; Naef et al., 2001; Li and Wong, 2001).”
Stochastic Models Inspired by Hybridization Theory for Short Oligonucleotide Arrays (2004, unknown): “Various other similar algorithms have been proposed [10, 21 , 17, 4, 24].”
Archetypal analysis of diverse Pseudomonas aeruginosa transcriptomes reveals adaptation in cystic fibrosis airways (2013, methodology): “Then, data is normalized using the qspline method [14] and gene expression index values are calculated using robust multiarray average expression measure [15].”
affy - analysis of Affymetrix GeneChip data at the probe level (2004, methodology): “Details concerning the normalization methods included in the package can be found in their respective references: loess (Åstrand, 2003), invariantset (Li and Wong, 2001b), qspline (Workman et al. , 2002) and quantiles (Bolstad, 2001).”
A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (2012, methodology): “The CEL files were pre-processed and normalized together using Probe Logarithmic Intensity Error (PLIER) (37) and cubic spline method (38), respectively.”
Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment (2004, unknown): “For instance, where only a limited number of gene expression levels are changed and they are well-behaved [25], stability of signal versus log-ratio [1-4] and intensity independence of measured log-ratio [1,5-7] can be expected.”