Normalization of oligonucleotide arrays based on the least-variant set of genes

Calza, Stefano; Valentini, Davide; Pawitan, Yudi

doi:10.1186/1471-2105-9-140

Cited by 52 publications

(64 citation statements)

References 30 publications

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“…Probes with an E value of Ͼ0.7 (around 17% of all probes) were selected as the LOS. For each microarray, a LOWESS (locally weighted scatterplot smoothing) (40) polynome with a smoothing parameter of 0.2 was fitted to the raw data from LOS probes and their average expression over the whole time series, similar to a procedure described previously (41). All probe values were normalized regarding these polynomes.…”

Section: Methodsmentioning

confidence: 99%

Daily Expression Pattern of Protein-Encoding Genes and Small Noncoding RNAs in Synechocystis sp. Strain PCC 6803

Beck

Hertel

Rediger

et al. 2014

Appl Environ Microbiol

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Section: Methodsmentioning

confidence: 99%

Daily Expression Pattern of Protein-Encoding Genes and Small Noncoding RNAs in Synechocystis sp. Strain PCC 6803

Beck

Hertel

Rediger

et al. 2014

Appl Environ Microbiol

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“…The CONSTANd algorithm is also a data-driven normalization method and adopts the expected value (mean) as measure of the central tendency. This global normalization scheme is justified when three key assumptions are fulfilled (53). First, all normalization methods require a reference set of observations that do not vary between the samples.…”

Section: Discussionmentioning

confidence: 99%

“…This type of inaccuracies can be remediated by data normalization. Luckily, a plethora of data normalization methods exist that can be borrowed from micro-array, LC-MS or NMR data analysis (12)(13)(14)(15). Some of these normalization techniques are already implemented in software packages dedicated for mass spectral data, as the case for the DAPAR implemented in R Bioconductor.…”

mentioning

confidence: 99%

CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization

Maes

Hadiwikarta²,

Mertens

et al. 2016

Molecular & Cellular Proteomics

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In quantitative proteomics applications, the use of isobaric labels is a very popular concept as they allow for multiplexing, such that peptides from multiple biological samples are quantified simultaneously in one mass spectrometry experiment. Although this multiplexing allows that peptide intensities are affected by the same amount of instrument variability, systematic effects during sample preparation can also introduce a bias in the quantitation measurements. Therefore, normalization methods are required to remove this systematic error. At present, a few dedicated normalization methods for isobaric labeled data are at hand. Most of these normalization methods include a framework for statistical data analysis and rely on ANOVA or linear mixed models. However, for swift quality control of the samples or data visualization a simple normalization technique is sufficient. To this aim, we present a new and easy-to-use data-driven normalization method, named CONSTANd. The CONSTANd method employs constrained optimization and prior information about the labeling strategy to normalize the peptide intensities. Further, it allows maintaining the connection to any biological effect while reducing the systematic and technical errors. As a result, peptides can not only be compared directly within a multiplexed experiment, but are also comparable between other isobaric labeled datasets from multiple experimental designs that are normalized by the CONSTANd method, without the need to include a reference sample in every experimental setup. The latter property is especially useful when more than six, eight or ten (TMT/iTRAQ) biological samples are required to detect differential peptides with sufficient statistical power and to optimally make use of the multiplexing capacity of isobaric labels. Molecular & Cellular Proteomics 15:

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“…The proposed method is an adaptation of a similar algorithm developed for Affymetrix mRNA array (Calza et al 2007), to a modified one for the miRNA Agilent platform, with an improved modeling of the data. The main motivation of the joint GLM modeling stands in the heterogeneous variance of intensities across probes for a large proportion of miRNAs, so that a standard RLM would not efficiently estimate array and probe effects, and thus would likely result in suboptimal identification of the reference set for normalization.…”

Section: Discussionmentioning

confidence: 99%

Modified least-variant set normalization for miRNA microarray

Suo

Salim²,

Chia³

et al. 2010

RNA

Self Cite

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MicroRNAs (miRNAs) are short noncoding RNAs that are involved in post-transcriptional regulation of mRNAs. Microarrays have been employed to measure global miRNA expressions; however, because the number of miRNAs is much smaller than the number of mRNAs, it is not clear whether traditional normalization methods developed for mRNA arrays are suitable for miRNA. This is an important question, since normalization affects downstream analyses of the data. In this paper we develop a least-variant set (LVS) normalization method, which was previously shown to outperform other methods in mRNA analysis when standard assumptions are violated. The selection of the LVS miRNAs is based on a robust linear model fit of the probelevel data that takes into account the considerable differences in variances between probes. In a spike-in study, we show that the LVS has similar operating characteristics, in terms of sensitivity and specificity, compared with the ideal normalization, and it is better than no normalization, 75th percentile-shift, quantile, global median, VSN, and lowess normalization methods. We evaluate four expression-summary measures using a tissue data set; summarization from the robust model performs as well as the others. Finally, comparisons using expression data from two dissimilar tissues and two similar ones show that LVS normalization has better operating characteristics than other normalizations.

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Normalization of oligonucleotide arrays based on the least-variant set of genes

Cited by 52 publications

References 30 publications

Daily Expression Pattern of Protein-Encoding Genes and Small Noncoding RNAs in Synechocystis sp. Strain PCC 6803

Daily Expression Pattern of Protein-Encoding Genes and Small Noncoding RNAs in Synechocystis sp. Strain PCC 6803

CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization

Modified least-variant set normalization for miRNA microarray

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