A Novel and Fast Normalization Method for High-Density Arrays
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Maarten van Iterson
Abstract
Background: Among the most commonly applied microarray normalization methods are intensity-dependent normalization methods such as lowess or loess algorithms. Their computational complexity makes them slow and thus less suitable for normalization of large datasets. Current implementations try to circumvent this problem by using a random subset of the data for normalization, but the impact of this modification has not been previously assessed. We developed a novel intensity-dependent normalization method for microarrays that is fast, simple and can include weighing of observations.
Results: Our normalization method is based on the P-spline scatterplot smoother using all data points for normalization. We show that using a random subset of the data for normalization should be avoided as unstable results can be produced. However, in certain cases normalization based on an invariant subset is desirable, for example, when groups of samples before and after intervention are compared. We show in the context of DNA methylation arrays that a constant weighted P-spline normalization yields a more reliable normalization curve than the one obtained by normalization on the invariant subset only.
Conclusions: Our novel intensity-dependent normalization method is simpler and faster than current loess algorithms, and can be applied to one- and two-colour array data, similar to normalization based on loess.
Availability: An implementation of the method is currently available as an R package called TurboNorm from www.bioconductor.org .
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- A New Explained-Variance Based Genetic Risk Score for Predictive Modeling of Disease Risk
- Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications
- Cluster-Localized Sparse Logistic Regression for SNP Data
- How to analyze many contingency tables simultaneously in genetic association studies
- Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure
- Estimating the Number of One-step Beneficial Mutations
- Testing clonality of three and more tumors using their loss of heterozygosity profiles
- Correction for Founder Effects in Host-Viral Association Studies via Principal Components
- A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
- An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping
- A Novel and Fast Normalization Method for High-Density Arrays
- Performance of MAX Test and Degree of Dominance Index in Predicting the Mode of Inheritance
- A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data
- QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function
- Computing Posterior Probabilities for Score-based Alignments Using ppALIGN
