Orthogonal projections to latent structures as a strategy for microarray data normalization
 Max Bylesjö^{1}Email author,
 Daniel Eriksson^{2},
 Andreas Sjödin^{3},
 Stefan Jansson^{3},
 Thomas Moritz^{2} and
 Johan Trygg^{1}
DOI: 10.1186/147121058207
© Bylesjö et al; licensee BioMed Central Ltd. 2007
Received: 12 February 2007
Accepted: 18 June 2007
Published: 18 June 2007
Abstract
Background
During generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded.
Results
We introduce a normalization strategy for multichannel microarray data based on orthogonal projections to latent structures (OPLS); a multivariate regression method. The effect of applying the normalization methodology on singlechannel Affymetrix data as well as dualchannel cDNA data is illustrated. We provide a parallel comparison to a wide range of commonly employed normalization methods with diverse properties and strengths based on sensitivity and specificity from external (spikein) controls. On the illustrated data sets, the OPLS normalization strategy exhibits leading average true negative and true positive rates in comparison to other evaluated methods.
Conclusion
The OPLS methodology identifies joint variation within biological samples to enable the removal of sources of variation that are noncorrelated (orthogonal) to the withinsample variation. This ensures that structured variation related to the underlying biological samples is separated from the remaining, biasrelated sources of systematic variation. As a consequence, the methodology does not require any explicit knowledge regarding the presence or characteristics of certain biases. Furthermore, there is no underlying assumption that the majority of elements should be nondifferentially expressed, making it applicable to specialized boutique arrays.
Background
The microarray technology [1] is now a standard technique in many genomics laboratories due to the highthroughput capacities and relatively low cost in detecting gene expression levels en masse. Since the introduction, a vast number of biological studies have utilized the technology to identify regulatory patterns in various organisms [2–4].
In the commonly used spotted microarray platform, probes are attached to a solid surface on predefined positions. Sample RNA is reverse transcribed to cDNA, labeled with fluorescent dyes and allowed to hybridize to the probes. After washing away superfluous material, the remaining fluorescence signal from the probes is assumed to reflect the relative expression levels of the sample RNA. Typically, two RNA samples, labeled with different fluorophores (for instance Cy5 and Cy3), are measured in parallel on the same surface to partially compensate for variability in probe dispersion and concentration. Extensions from the current twochannel standard into a multichannel platform have recently been gaining in popularity [5–7].
During data generation, numerous factors alter the outcome through the introduction of systematic biases. Different properties of the dyes (such as degree of dye incorporation and sensitivity to dye bleaching), irregular or overall disparities of the slide surfaces, variation in printing as well as scannerintroduced bias influence the RNA quantification process. We will generally refer to the main effects as dye, spatial and array bias in the following sections, which have been shown to be the most influential forms of systematic biases present in data from the spotted cDNA microarray platform [8, 9].
As a means to identify and remove systematic biases, data normalization is typically performed. A considerable amount of published studies concern the subject of microarray normalization, see for instance [10] for a comprehensive comparison of existing methods or [11] for a review.
The most widespread normalization methods aim to address the dye and possibly also spatial effects within each array. We will refer to these methods as withinarray normalization methods in the following text. Global median normalization is a straightforward normalization method that addresses labeling issues by adjusting the median intensity value within each array. Global loess normalization [12] appeared early on as a means to address intensitydependent dye bias. Subsequently, the loess method was applied locally within each printtip group to additionally assess fixed spatial effects [13]. The methodology of local regression normalization has recently been generalized to handle nonfixed dye and spatial effects in the OLIN normalization method [14]. All of the mentioned methods explicitly or implicitly assume that the majority of the genes on the array (or in localized regions) are unaffected, i.e. that the logtransformed ratios M = log_{2}(R/G) are centered at zero.
As typical microarray experiments involve multiple arrays to characterize multiple samples, systematic differences between the arrays (array bias) are frequently introduced. Several normalization methods for independently addressing this bias have been suggested in the literature [15–17]. We will refer to these methods as betweenarray normalization methods. Betweenarray normalization methods are typically applied subsequent to withinslide normalization methods. The general strategy has been to normalize the empirical distributions of intensities across arrays, such as the Aquantile normalization [15, 17] that ensures that distributions of A = log_{2} ($\sqrt{RG}$) values are maintained across the slides without altering the dye ratios. Another, closely related approach is the Tquantile normalization methodology [15, 17] that performs quantile normalization separately per group, where a group is defined as an arbitrary collection of quantified RNA samples (such as technical replicates of the same biological sample).
Different approaches to microarray normalization have emerged that do not easily fall into any of these groups. For instance, the VSN normalization method [18, 19] performs channelwise linear and nonlinear transformations to reduce the mean value and variance dependence. Potentially powerful is the analysis of variance (ANOVA) approach [8, 9] where all effects are assessed simultaneously in one global model. Wolfinger et al. explicitly used two interconnected models; one for normalization purposes and the later for identification of differential expression (DE). The ANOVA approach is conceptually related to the presented methodology. Consequently, similarities and discrepancies will be elaborated further in a suitable context.
Orthogonal signal correction (OSC) [20] is a technique originally developed and used for spectral data. The general concept of OSC is straightforward: structured variation that is orthogonal (noncorrelated) to a given problem is identified and can subsequently be studied and discarded. Formally rephrased, systematic variation in the descriptor matrix X (containing, for instance, spectral measurements or microarray signal intensities) is recognized by utilizing information in the response matrix Y (containing, for instance, toxicity measurements or replicate sample information). Orthogonal projections to latent structures (OPLS) [21] was later introduced as an extension to the supervised multivariate regression method partial least squares (PLS) [22] featuring an integrated OSCfilter. OPLS employs information in the Y matrix to decompose the X matrix into correlated, orthogonal and residual structures of information, respectively. Further details of the OPLS method and related methods are described in the upcoming paragraphs.
The following notation will be used throughout. Vectors are denoted by bold, lowercase letters and are assumed to be column vectors unless indicated by a transposition, e.g. p^{T}. Matrices are denoted by bold uppercase letters, for instance X, with optional dimensionality information, e.g. (N × K). Matrix inverses are denoted by X^{1}. All matrices are assumed to be columnwise meancentered unless explicitly stated.
Linear regression methods
Linear regression relate two data matrices X (N × K) and Y (N × M) on the general form in Equation 1.
Y = XB + F (1)
The difficulty in linear regression lies in identifying B (K × M) while maintaining certain objectives, such as minimization of the residual F (N × K), highquality predictions of future (unknown) Y_{pred} as well as high interpretability of B. One of the most frequently employed methods for estimating B is the multiple linear regression (MLR) method. As MLR is a leastsquares solution, B is resolved so that the sum of squares of the residual matrix F is minimized (Equation 2A). The X^{+} matrix denotes the generalized (MoorePenrose) inverse (Equation 2B).
B = X^{+}Y (2A)
X^{+} = (X^{T}X)^{1}X^{T} (2B)
If X is rankdeficient, (X^{T}X)^{1} will be undefined and, consequently, the method inapplicable. This generally happens when there is strong multicollinearity between the columns (variables) in X. This scenario is typical for data matrices in the areas of biology and bioinformatics as biological systems are inherently full of covariance patterns stemming from pathway regulations.
One alternative to traditional MLR is employing latent variable regression (LVR) methods. The general assumption behind LVR methods is that a system can be described in terms of a small number of latent variables that characterize the main properties of the system. Multicollinearity is, in such a system, both expected and handled appropriately. This is, for instance, employed in the PLS regression method [22] where X is decomposed into latent variable structures T and thereby circumvents the problems with potential rankdeficiency in X (Equation 3).
X = TP^{T} + E (3)
The definition and calculation of B is distinctly different (Equations 4 and 5) by utilizing the latent variable structures in T.
B = W(P^{T}W)^{1}C^{T} (4)
C = (T^{T}T)^{1}T^{T}Y (5)
In Equations 3, 4 and 5, T (N × A) is the score matrix, describing properties at a sample (observational) level, P^{T} (A × K) is the loading matrix, describing properties at a variable (descriptor) level, W (K × A) is a weight matrix describing covariance between X and Y, E (N × K) is the residual matrix of X. N denotes the number of observations (microarray channels) and K the number of variables (microarray elements). A is the number of latent variables and thus determines the latent variable rank of the solution, which is typically far less than the algebraic rank. A suitable value of A is determined using resampling methods such as crossvalidation [23] or similar. See the supplied reference for further details.
The OPLS method
OPLS is a multivariate LVR method where the objective function is to find predictive components that simultaneously maximize the covariance and correlation between X and Y as in Equation 1. Compared to the PLS representation of X (Equation 3), OPLS utilizes information in the response matrix Y to further decompose the X matrix into three distinct structures as described in Equation 6. Here, T_{p} (N × A_{p}) denotes the predictive score matrix for X, P_{p}^{T} (A_{p} × K) denotes the predictive loading matrix for X, T_{o} (N × A_{o}) denotes the corresponding Yorthogonal score matrix, P_{o}^{T} (A_{o} × K) denotes the loading matrix of Yorthogonal components and E denotes the residual matrix of X.
X = T_{p}P_{p}^{T} + T_{o}P_{o}^{T} + E (6)
Important to note from Equation 6 is that T_{p}P_{p}^{T} contains systematic covariance and correlation structures in relation to Y, T_{o}P_{o}^{T} contains systematic Yorthogonal (biasrelated) variation and the residual matrix E contains the remaining unmodeled variation. The A_{p} and A_{o} parameters define the rank of the solution and will be discussed in more detail at a later point. More explicit information regarding the algorithm for identifying T_{p}, P_{p}^{T}, T_{o} and P_{o}^{T}, respectively, are described in [21, 24].
Study summary
We will, in the upcoming sections, show how proper construction of the X and Y matrices and subsequent use of OPLS can be utilized as an efficient normalization step for multichannel microarray data. Dualchannel microarray data will primarily be used in direct comparison with a set of common normalization methods to highlight differences. Additional data sets, both dualchannel and singlechannel, have been evaluated and are presented in additional data file 1. The evaluation will primarily be based on differential expression for external controls where the true ratios are known a priori.
Results
A brief summary of the outlined strategy is provided in the next paragraphs; for a more comprehensive description, consult the Methods section.
The presented methodology identifies joint variation within biological samples to enable removal of sources of variation that are mathematically independent (orthogonal) to the withinsample variation. This ensures that systematic variation related to the underlying biological samples is separated from the remaining, biasrelated sources of structured variation. The raw microarray data is, in the following text, contained in the X matrix whereas information regarding the different biological samples is contained in the Y matrix. The systematic covariance and correlation structures associated to the biological samples are characterized by the predictive score matrix T_{p} (N × A_{p}) and predictive loading matrix P_{p}^{T} (A_{p} × K) from the OPLS model. Here, the T_{p} matrix describes relations at a sample level whereas the P_{p}^{T} matrix describes corresponding characteristics at a variable (gene) level. The biasrelated variation, henceforth referred to as the Yorthogonal variation, is captured in the Yorthogonal score matrix T_{o} (N × A_{o}) and the Yorthogonal loading matrix P_{o}^{T} (A_{o} × K). In a similar fashion, the T_{o} matrix describes relations at a sample level whereas the P_{o}^{T} matrix describes corresponding characteristics at a variable (gene) level. Dimensionality of the solution is primarily related to the data set specific parameter A_{o} that is estimated by means of Monte Carlo CrossValidation (MCCV) [25]. Please consult additional data file 1 for further information regarding the crossvalidation procedure.
In the presented study, we will explicitly illustrate the effects of the suggested normalization methodology primarily on a public dualchannel data set. This data set, which we will refer to as the H8k data set, contains 26 twochannel cDNA microarrays from a previously published study [26]. The experimental design is a traditional dyeswap design containing a treated sample and a reference sample measured using technical replication. Further details regarding the data set are available in the supplied reference.
We have further evaluated two different data sets using the presented methodology. The first is an inhouse produced dualchannel data set (referred to as the POP2.3 data set), whereas the second is a public singlechannel Affymetrix (HGU95) spikein data set, available at the Affymetrix web page [27]. Characteristics and results for these two data sets are mainly available in additional data file 1.
Overview of the evaluated normalization methods. The compared normalization methods and their corresponding properties. ^{a} The spatial effect is constrained to printtip based effects. ^{b} The method can be extended to support this feature.
Method name  Short name  Ratiobased  Spatial  Betweenarray 

Global median  Median  Yes  No  No 
Global loess  Loess  Yes  No  No 
Printtip loess  PTloess  Yes  Yes^{a}  No 
Printtip loess with Tquantile norm.  PTloess/Tq  Yes/Yes  Yes^{a}/No  No/Yes 
OLIN  OLIN  Yes  Yes  No 
VSN  VSN  No  No^{b}  No 
Global loess with ANOVA  ANOVA  Yes/No  No/Yes  No/Yes 
OPLS  OPLS  No  Yes  Yes 
The information in the Yorthogonal T_{o}P_{o}^{T} matrices is readily accessible for interpretational purposes. Recall that the T_{o} matrix describes relations at a sample level whereas the P_{o}^{T} matrix describes characteristics at a variable (gene) level. For this particular data set, T_{o} is composed of 10 score vectors that are orthogonal to Y and individually orthogonal to each other. We will explicitly interpret a selection of Yorthogonal vectors to justify the discarded variation as well as to demonstrate the powerful interpretational alternatives available when employing OPLS as a normalization method.
The encouraging results from the H8k data set are supported by results from the dualchannel, inhouse produced POP2.3 data set as well as the public singlechannel HGU95 data set (see additional data file 1 for details). For the POP2.3 data set, OPLSnormalized data exhibits leading average TP and TN rates. Furthermore, the first score vector t_{o,1} characterizes a distinct array bias, consistent with the behavior of the H8k data set. For the HGU95 data set, OPLSnormalized data displays leading average TP and TN rates; signifying that the method is applicable also for singlechannel data.
Discussion
Microarray measurements frequently host various forms of systematic and dataset specific experimental errors that limit the accuracy and precision of the results. We have outlined a strategy based on recent advances in multivariate regression for identification of such bias. Using the OPLS method [21], information across biological samples is employed to discard noncorrelated information. With a sound underlying experimental design, this Yorthogonal information will contain various forms of biases (such as array, dye, spatial and batchrelated biases), which can subsequently be discarded from the data.
The general form of the methodology arguably makes it likely to be of broad utility, which we discuss in more detail in the upcoming paragraphs.
First, the methodology is intensitybased and thus not restricted to twochannel data and the explicit formation of ratios (M values). The main rationale behind usage of ratios is related to biases originating from spot size and overall intensity baseline disparities across arrays, but this effect is clearly captured with the present methodology (Figure 2). The intensitybased approach has obvious auxiliary advantages, in particular when it comes to evaluation of complex designs where treated samples are not consistently hybridized against a reference sample. Furthermore, the general arrangement supports normalization of singlechannel data; such a setup is shown in additional data file 1 with promising results. Moreover, the intensitybased approach enables future extensions to data containing more than two channels, which is presumably becoming an increasingly attractive choice. The extra information in the additional channel(s) could be used to increase the number of measurements [6] or for quality control [5].
Second, the methodology does not rely on assumptions that the majority of genes on the array, globally or in localized regions, are nondifferentially expressed. Thus, the approach is also applicable to custommade arrays where the majority of genes are in fact assumed to be DE (commonly referred to as boutique arrays). This is not true for the majority of the currently available methods, although recent extensions of the loess normalization method support such data [28]. There is an apparent danger in applying traditional normalization methods if this underlying assumption of abundance of nonDE genes is not met. Specifically, true biological effects will be eliminated by the normalization to an unknown extent in such situations, which may ultimately obscure the final interpretation of the results. Furthermore, it is not always evident beforehand if the assumption is valid without prior knowledge of the studied system and the anticipated effects.
Third, the methodology does not assume presence or absence of certain categories of biases (such as ANOVA and printtip based methods) or characteristics of these biases. For instance, assume that there exists an (unknown) structured variation related to the production of the microarray slides in different batches. This variation will not be captured by the general ANOVA model unless such an effect is anticipated; which is not true for the OPLS model. The only prerequisite for the present methodology to fully identify and discard biasrelated variation is that it is orthogonal to the differences related to the biological samples and is systematic (structured).
The evaluation and rationale behind the potential strengths of the method is, to a great extent, based on the use of external controls to certify the reliability of the results. We believe that the employment of external (spikein) controls is a very powerful approach as one estimates the accuracy of the arrays, not only the precision across replicates. See also [29] for useful discussions regarding evaluation of microarray performance and external validation.
One common criticism concerning the usage of global models, such as ANOVA, for normalization purposes is that the construction and evaluation requires statistical expertise (see, for instance [14], discussion section, on this subject). For the outlined method, the only prerequisite by the user is a specification of the sample groups. The remaining tasks, including model fitting, are fully automated using MCCV and need not be any more complicated for novice users than methods for withinslide normalization based on local regression. Model evaluation, as described in the results section, is a recommended but not mandatory step in the outlined strategy if highthroughput is required.
One known limitation of the methodology arises in situations where the group information is unavailable. This applies in unsupervised analyses, for instance when one is interested in detecting subclasses of a particular cancer type. As the true origin of the samples is unknown, this information cannot be utilized for normalization purposes.
In the main text, we have briefly discussed the similarities of the outlined method as compared to a twostep ANOVA approach as described in [9]. From a conceptual point of view, the approaches are related as both techniques aim to assess specific effects that can subsequently be retained or discarded. In the first step (normalization step) of the twostep ANOVA approach, various forms of bias are explicitly characterized. In the second step, the biological effects are estimated on the remaining sources of variation (residual). The presented OPLS approach roughly operates in the reverse order, as the biological effects are estimated at an initial stage and the systematic Yorthogonal effects (biasrelated) are discarded at a subsequent step. The OPLS normalization procedure could analogously be arranged to explicitly model unwanted effects in Y (such as array and printtip effects) and subsequently retain T_{o}P_{o}^{T} + E posterior to modeling. Differences in the results are essentially related to overlapping covariance structures. Assume that there exists structured variation in a data set that is covarying with both a biological effect as well as an unwanted effect. In the twostep ANOVA approach, this variation will be identified and discarded in the first (normalization) step. Consequently, systematic biological information can be discarded if covarying with unwanted effects, which is a stringent normalization criterion. In the presented OPLS approach, only variation that is completely unrelated (orthogonal) to the biological sample variation will be discarded. Using the same hypothetical example as for the twostep ANOVA approach, the OPLS method will thus retain the biological variation in the data set after normalization. We see that in some cases (as in the presented examples) this approach can be more powerful in identifying differential expression. This is essentially a consequence of the fact that if we are not aware of all the present biasrelated effects, then explicit modeling is not viable in practice.
One could easily imagine situations where one is interested in noncategorical information, for instance exact spikein sample concentration gradients for the singlechannel platform. It is certainly possible to use OPLS for such purposes; specifically to calibrate the measured concentrations to the known concentrations and subsequently predict the unknown (but measured) concentrations according to the same model. This is an example of multivariate calibration [30], which is an established field of linear modeling. However, since this would involve a different setup, aim and partly also notation compared to the presented method, we will not discuss such a potential normalization strategy in detail.
Several remaining features of the OPLS methodology, when utilized for normalization purposes, are left unevaluated in this study. As the normalization is modelbased, a finite model space is covered where the regression is defined to be valid. This implies that one can test for model outliers, which can for instance be exploited as a quality control step to detect flawed hybridizations. Furthermore, the outlined strategy makes no explicit use of the predictive information in T_{p}P_{p}^{T}, reflecting biological differences at a sample (T_{p}) and variable (P_{p}^{T}) level. In relation to the twostep ANOVA method, this would roughly correspond to the second step where biological effects are differentiated. The OPLS method host numerous capabilities for interpretation of this information (see for instance [31] on this subject), but remains the scope of a future paper.
Conclusion
Presented is a methodology for normalization of microarray data using multivariate regression as implemented in the OPLS method. The strengths of the strategy are demonstrated based on both public and inhouse produced data, where identification of known differential expression is shown to be augmented compared to other evaluated methods. Illustrated examples are based on data from the dualchannel microarray platform but the general setup of the strategy allows simple extensions to multichannel platforms as well.
Methods
Constructing the data tables
The following text refers to the twochannel platform but can easily be generalized to singlechannel or multichannel data. Let X consist of the log_{2}transformed intensity values from each channel, i.e. not using ratios for the intensity estimate within the same array. If we are measuring intensity values on S arrays on array layouts containing K elements, the dimensionality of X will thus be (2S × K).
Now let us assume that the data consists of L groups, which are measured in replicates. In the demonstrated examples, L is the biological replicates of different treatments, which are measured several times, but could also be some other effect of interest. Y is constructed as a sparse binary matrix of dimensionality (2S × L), where each element in Y is either 0 (sample does not belong to group) or 1 (sample belongs to group). For the sake of simplicity, we will assume that no sample belongs to multiple groups, which implies that the algebraic rank of Y is L1 when Y is meancentered, but this is not a general restriction. In the H8k and POP2.3 data sets, one treated sample and one reference sample have been used with a varying number of biological and technical replicates. Each measured channel will denote one row in the Y matrix. In this particular case, Y will consist of two columns (one for each treatment) and will, posterior to meancentering, have the algebraic rank L1 = 1. The readers that are familiar with discriminant analysis theory will note that the structure of Y essentially describes a classification problem [32].
As previously stated, the objective function of OPLS is to find predictive components that simultaneously maximize the covariance and correlation between X and Y. As a consequence of the structure of Y, the predictive information in T_{p}P_{p}^{T} describes the maximum difference between the groups, which is the main biological discrepancies given that the groups denote different biological samples as in the presented examples. In relation to the ANOVA strategy outlined by [8], the information in T_{p} resembles what is characterized by the V (variety) term and P_{p}^{T} what is characterized by the G (gene) term (see also discussion on this subject). The Yorthogonal variation in T_{o}P_{o}^{T} will then portray the remaining structured variation, which is independent of the sample groups. Array effects, dye effects, spatial effects and possible interactions between these effects will all fall into this category. Fundamental to the concept is that these effects are not confounded with the sample group effects in Y due to improper experimental design. Note also that we are not using degrees of freedom to explicitly distinguish these sources of systematic biases from each other.
The normalized data matrix X_{norm} (2S × K) is subsequently reconstructed as in Equation 8, i.e. without the Yorthogonal structures.
X_{norm} = T_{p}P_{p}^{T} + E (8)
Model estimation
In OPLS modeling, two parameters A_{p} and A_{o} need to be estimated, which are related to the dimensionality of T_{p}P_{p}^{T} and T_{o}P_{o}^{T}, respectively. For the problems described here, we will set A_{p} to the algebraic rank of the meancentered Y, i.e. to L1. This corresponds to the fundamental assumption that discriminatory variation between the groups is present in X. The remaining parameter A_{o} determines the amount of variance that is peeled off from the X matrix (in this case, microarray signals). The value of A_{o} is essentially datasetspecific. A too low value of A_{o} implies that there is still systematic variation in X that is unrelated to Y, which lowers the possibilities of identifying differential expression (increases type II errors). A too high value of A_{o} will, on the contrary, increase the risk of false positives (type I errors) due to the decrease in variance in T_{p}P_{p}^{T}. For the data set described here, we have utilized groupbalanced Monte Carlo CrossValidation (MCCV) [25] to estimate a suitable value of A_{o}. More detailed descriptions of the employed MCCV strategy, which is fully automated, are available in additional data file 1.
External controls
The demonstrated H8k data set contain external (spikein) controls, based on the Lucidea Universal Scorecard (GE Healthcare, Uppsala, Sweden) system where expression ratios are known beforehand. The external controls are essentially of two different types. The calibration clones are printed at a 1:1 ratio in various concentrations on the slide. As these clones are known not to be differentially expressed (DE), any erroneous assessment of DE will yield false positives (FP). We will utilize the calibration clones to determine the true negative (TN) rate, where TN = 1 – FP. The ratio clones are printed at ratios of 1:3, 3:1, 1:10 and 10:1 in different concentrations on the slide. As these clones are known to be DE, we will use these clones to determine the true positive (TP) rates. Other capabilities of the Lucidea scorecard system, such as the utility clones, have not been utilized in this study. The calibration and ratio clones are spatially scattered across the arrays and constitute a representative subset of approximate two percent of the total number of elements on the microarrays.
Differential expression
The results of the normalization methods based on the true negative (TN) rates from the calibration controls, the true positive (TP) rates from the ratio controls and the total number of differentially expressed genes are illustrated. Differential expression was set at p_{adjusted} < 0.05 based on Student's ttest after employment of the stepwise false discovery rate method of Benjamini and Hochberg [35] to account for multiple testing inflation. All available clones were employed for multipletest correction, not only the external (spikein) control subset. All calculations of differential expression are, for consistency, based on the log_{2}transformed ratios (M values) within each slide, even for methods that do not employ ratios for normalization purposes.
Implementation and availability
A R package [36] including all required sources is available on request from the corresponding author.
Abbreviations
 ANOVA:

Analysis of variance
 OLIN:

Optimized local intensitydependent normalization
 VSN:

Variance stabilization
 OSC:

Orthogonal signal correction
 OPLS:

Orthogonal projections to latent structures
 MCCV:

Monte Carlo CrossValidation
 MLR:

Multiple linear regression
 LVR:

Latent variable regression
 DE:

Differential expression
 TN:

True negative
 TP:

True positive
Declarations
Acknowledgements
The authors are grateful to Dr. Gordon K. Smyth and colleagues at the Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia for kindly supplying the H8k data set. This work was supported by grants from the Swedish Foundation for Strategic Research (MB, JT), the Knut and Alice Wallenberg Foundation (JT), the European Commission through the Directorate General Research within the Fifth Framework for Research (AS, SJ), the Swedish Research Council (MB, DE, AS, TM, SJ, JT), EUstrategic funding (DE) and the Functional Genomics Initiative at Swedish University of Agricultural Sciences (DE, TM).
Authors’ Affiliations
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