Faster cyclic loess: Normalizing RNA arrays via linear models

Mayo Clinic - Rochester, Рочестер, Minnesota, United States
Bioinformatics (Impact Factor: 4.98). 12/2004; 20(16):2778-86. DOI: 10.1093/bioinformatics/bth327
Source: PubMed


Our goal was to develop a normalization technique that yields results similar to cyclic loess normalization and with speed comparable to quantile normalization.
Fastlo yields normalized values similar to cyclic loess and quantile normalization and is fast; it is at least an order of magnitude faster than cyclic loess and approaches the speed of quantile normalization. Furthermore, fastlo is more versatile than both cyclic loess and quantile normalization because it is model-based.
The Splus/R function for fastlo normalization is available from the authors.

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    • "Taking into account that ComBat does not eliminate batch effect with the conditions of our dataset, we decided to partially solve this issue as follows: After preprocessing all arrays with frma (McCall et al., 2010), and using summarization with robust weighted average with no background correction, we split the datasets into cases/controls, and then applied ComBat to both datasets separately. After that, we re-joined the two resulting datasets and re-normalized them together with the cyclic loess algorithm (Ballman et al., 2004), in such way that both conditions belong now to the same dynamic range We needed to have a measure of the batch effect within the samples so that we could remove the corresponding bias as accurately as possible. To this end we resort to Principal Variance Component Analysis (PVCA) that is an algorithm that combines the advantages of the principal component analysis (reduction of dimensionality) with the components of the analysis of variance (Grass, 2009). "
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    Computational biology and chemistry 09/2015; DOI:10.1016/j.compbiolchem.2015.08.007 · 1.12 Impact Factor
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    • "Analyses for both data types were performed on the log base 2 scale. For each data type, all samples were normalized together as study groups should not have wholesale changes in RNA concentration using appropriate model-based algorithms (Ballman et al. 2004; Oberg et al. 2008). Data were analyzed using linear models (ANOVA models) (Kerr et al. 2000; Wolfinger et al. 2001; Hill et al. 2008; Oberg et al. 2008; Oberg and Vitek 2009) together with empirical Bayes methods (Smyth 2004), which are appropriate for mitigating the risk of false discovery in small sample sizes. "
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    Brain and Behavior 11/2014; 4(6). DOI:10.1002/brb3.283 · 2.24 Impact Factor
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    • "Data quality was assessed via box and whisker plots along with residual and pair-wise MVA plots before and after normalization [29,30]. All arrays were normalized together using fastlo, a non-linear normalization similar to cyclic loess which runs in a fraction of the time [31]. Both supervised and unsupervised analyses were performed. "
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