Operational criteria for selecting a cDNA microarray data normalization algorithm

Argyropoulos, Christos; Chatziioannou, Aristotelis; Nikiforidis, George; Moustakas, Aristidis; Kollias, George; Aidinis, Vassilis

doi:10.3892/or.15.4.983

Cited by 7 publications

(12 citation statements)

References 45 publications

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“…The calculation of the bias and variance of the expression values for these probes, which is described in detail in Materials and methods (equations [1], [2], [5] and [6]), appears to be the most obvious measure to differentiate among normalization methods. Following the procedure described in the study by Argyropoulos et al (67), we also assumed that the normalization method that gives the lowest bias and variance values for the control spots should be selected as the most appropriate method. Using equations [1] and [2] for double-channel normalization and equations [5] and [6] for single-channel normalization, we calculated the bias and variance values for the control probes present in each of the four datasets.…”

Section: Resultsmentioning

confidence: 99%

“…However, the use of graphical tools is intuitive and often based on the subjective perception of the researcher. In our opinion, a much more elegant and objective solution was proposed in the study by Argyropoulos et al (67), who indicated that the most important aspects of the selection of a normalization algorithm are accuracy, precision and over-fitting. These three aspects can be verified using the bias, variance and relative entropy, respectively, which can be calculated by mathematical formulae.…”

Section: Discussionmentioning

confidence: 99%

“…Taking into account all the above parameters, the spike-based normalization method was selected as the most appropriate for the tested dataset. However, Argyropoulos et al (67) admitted that there is no universally optimal normalization strategy.…”

Section: Discussionmentioning

confidence: 99%

“…To evaluate the normalization methods that are used for the normalization of double-channel microarray data, we used the formal approach proposed in the study by Argyropoulos et al (67). These authors determined the bias and variance values for spike-in control spots using the following formulae:…”

Section: Comparison Of the Normalization Methodsmentioning

confidence: 99%

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Analysis of boutique arrays: A universal method for the selection of the optimal data normalization procedure

Uszczyńska

Zyprych-Walczak

Handschuh

et al. 2013

International Journal of Molecular Medicine

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Abstract. DNA microarrays, which are among the most popular genomic tools, are widely applied in biology and medicine. Boutique arrays, which are small, spotted, dedicated microarrays, constitute an inexpensive alternative to whole-genome screening methods. The data extracted from each microarray-based experiment must be transformed and processed prior to further analysis to eliminate any technical bias. The normalization of the data is the most crucial step of microarray data pre-processing and this process must be carefully considered as it has a profound effect on the results of the analysis. Several normalization algorithms have been developed and implemented in data analysis software packages. However, most of these methods were designed for whole-genome analysis. In this study, we tested 13 normalization strategies (ten for double-channel data and three for single-channel data) available on R Bioconductor and compared their effectiveness in the normalization of four boutique array datasets. The results revealed that boutique arrays can be successfully normalized using standard methods, but not every method is suitable for each dataset. We also suggest a universal seven-step workflow that can be applied for the selection of the optimal normalization procedure for any boutique array dataset. The described workflow enables the evaluation of the investigated normalization methods based on the bias and variance values for the control probes, a differential expression analysis and a receiver operating characteristic curve analysis. The analysis of each component results in a separate ranking of the normalization methods. A combination of the ranks obtained from all the normalization procedures facilitates the selection of the most appropriate normalization method for the studied dataset and determines which methods can be used interchangeably. IntroductionDespite the dynamic development of deep sequencing technologies, microarrays are still commonly used in genomic research (1-5). Currently, DNA microarrays are mainly used for genotyping (6-9), gene expression profiling (10-12) and microRNA screening (13-15). In medicine, microarrays are used to determine the complexity and heterogeneity of diseases, to facilitate disease classification and to predict therapeutic outcomes (8,(16)(17)(18)(19)(20)(21)(22).Microarrays provide a large amount of useful information, but are accompanied by inherent noise and systematic errors (23)(24)(25)(26). No microarray experiment is free from variation introduced during sample preparation, hybridization, washing and scanning (24,27,28). Spotted arrays are burdened with technical defects that occur during their printing; these defects manifest as differences in spot size and shape and/or shifts of spots, rows or whole print-tips (24,28). In two-color assays, additional bias is introduced by uneven dye incorporation and by differences in the signal dynamic range and the sensitivity of dyes to photobleaching (23,24,29). Therefore, the major challenge in microarray analysis is data pr...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Comparison Of the Normalization Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of boutique arrays: A universal method for the selection of the optimal data normalization procedure

Uszczyńska

Zyprych-Walczak

Handschuh

et al. 2013

International Journal of Molecular Medicine

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show abstract

“…The values of the factors were estimated in the development subset and were used to correct the validation subset. We used empirical measures of variance (58,59) for the assessment of the effects of bias correction using the methods proposed in this paper. The Root Mean Square Error (RMSE), i.e.…”

Section: Methodsmentioning

confidence: 99%

Modeling bias and variation in the stochastic processes of small RNA sequencing

Argyropoulos

Etheridge

Sakhanenko

et al. 2017

Nucleic Acids Research

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The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data.

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miREV: An Online Database and Tool to Uncover Potential Reference RNAs and Biomarkers in Small-RNA Sequencing Data Sets from Extracellular Vesicles Enriched Samples

Hildebrandt

Kirchner

Hoen

et al. 2021

Journal of Molecular Biology

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Operational criteria for selecting a cDNA microarray data normalization algorithm

Cited by 7 publications

References 45 publications

Analysis of boutique arrays: A universal method for the selection of the optimal data normalization procedure

Analysis of boutique arrays: A universal method for the selection of the optimal data normalization procedure

Modeling bias and variation in the stochastic processes of small RNA sequencing

miREV: An Online Database and Tool to Uncover Potential Reference RNAs and Biomarkers in Small-RNA Sequencing Data Sets from Extracellular Vesicles Enriched Samples

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