Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
 Eva Freyhult^{1, 2}Email author,
 Mattias Landfors^{1, 2, 3},
 Jenny Önskog^{2, 5},
 Torgeir R Hvidsten^{2, 5} and
 Patrik Rydén^{2, 3, 4}Email author
https://doi.org/10.1186/1471210511503
© Freyhult et al; licensee BioMed Central Ltd. 2010
Received: 12 April 2010
Accepted: 11 October 2010
Published: 11 October 2010
Abstract
Background
Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or subclasses, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, preprocessing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization.
Result
We evaluated 2780 cluster analysis methods on seven publicly available 2channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, kmeans clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions.
Conclusions
The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.
Keywords
Background
Cluster analysis is a common approach to examine microarray expression data used both to group genes and samples/individuals. As an unsupervised method, the main advantage of cluster analysis is the ability to compare the expression profiles of different samples and detect groups of samples with similar expression profiles, e.g. to separate cancer patients likely to develop metastases without treatment from patients who are not likely to develop metastases and hence would not benefit from treatment. However, cluster analysis is by some believed to be overused [1] and is in need of thorough evaluation.
A few studies have evaluated different clustering methods and similarity metrics on realworld microarray data. One study found that modelbased clustering (e.g. Mclust) and kmeans performed best on cancer data, and that the frequently used hierarchical clustering method performed poorly [2]. Two other studies also report modelbased clustering as one of the best choice for gene clustering [3, 4], while yet another study found that performance varied too much between different evaluation criteria to be able to decide on one best method [5]. As has been the case in other bioinformatics areas, consensus methods have been shown to outperform the single best clustering method [6]. Although some agreement can be found in these studies, such as a tendency to rank modelbased clustering highly and hierarchical clustering lowly, there are even more disagreements. One reason might be that these comparative studies did not consider many of the other analysis choices that inevitably have to be made in a complete microarray experiment. Both preprocessing of microarray data (normalization, missing value imputation, standardization) and gene (feature) selection is almost always performed before clustering and the choice of these methods can have dramatic effects on the final clustering results. Previous studies have analyzed the effect of normalization on gene clustering, showing that the nonlinear loess normalization outperforms simpler normalizations such as scale and location normalizations [7]. Strengths and weaknesses of different imputation approaches have also been evaluated [8]. Maybe most importantly, the strong effect of gene selection on cluster analysis (mainly gene clustering) has been demonstrated using simulated data [9]. The importance of gene selection is not surprising given the impossibility of successful classification when discriminatory genes make up a small fraction of the total number of genes considered [10].
In this study we have used seven cancer data sets to demonstrate how cluster analysis is affected by the consecutive subprocesses; normalization of the microarray data, imputation of missing values, gene selection, standardization and clustering. To the best of our knowledge, no comparative study of clustering methods has taken into account all these accompanying analysis choices when evaluating the relative ability of these methods to retrieve natural groupings in gene expression data. We have focused on clustering of samples (individuals) commonly used in microarray studies to, e.g., identify subtypes of cancer with unique properties such as response to treatment. The analyses are implemented in R [11].
We show that the variation between data sets is huge and the best way to analyze one data set might not be optimal for another. However, some general conclusions can be drawn. We show that gene selection, clustering method and to some extent also normalization are important to the performance, and the choice of missing value imputations and standardization has only a minor effect on the performance (at least for the type of design considered in this study).
Methods
Data
Data sets
Name  Description  Size^{1}  Classes 

Alizadeh [13]  Diffuse large Bcell lymphoma (DLBCL) and other lymphoid malignancies (FL and CLL), normal cell samples as well as tissue type cell lines. http://llmpp.nih.gov/lymphoma, GEO: GSE60  133 × 7806 (133 × 7430)  DLCL (68) and other (65) samples (FL, CLL, normal cell samples and a variety of cell lines). 
Finak [14]  Samples from epithelial and striomal tissue from breast reduction tissue from tumoradjacent normal tissue. Agilent microarrays. GEO: GSE4823  66 × 33491  epithelial (34) and stromal tissue (32) 
Galland [15]  40 nonfunctioning pituitary adenomas (NFPAs). Agilent microarrays. arrayExpress: ETABM899  40 × 40475 (40 × 40291)  invasive (22) and noninvasive (18) 
Herschkowitz [16]  Human breast tumor samples (The full study includes 232 samples, but here only 119 samples run on a particular array (GPL1390) are included). GEO: GSE3165  106 × 19718  ERstatus, ER+ (59) and ER (47) 
Jones [17]  Highgrade lung neuroendocrine tumors. GEO: GSE1037  91 × 40233 (91 × 39746)  Patients with (72) and without (19) cancer. 
Sørlie [18]  Human breast carcinomas. http://genomewww.stanford.edu/breast_cancer/mopo_clinical/data/mopo_clinical.gz.tar  73 × 8033 (73 × 7734)  Clinical ERstatus ER+ (55), ER (18) 
Ye [19]  Samples from 40 hepatitis Bpositive patients with hepatocellular carcinoma (HCC). GEO: GSE364  87 × 8911  metastatic (P) and nonmetastatic (PN) patients. P (65), PN (22) 
As the goal of this study is to investigate how the performance of various cluster analysis algorithms is affected by the choice of preprocessing and gene selection, we need a true class partitioning to compare the computed clusters with. One should note, however, that there are often more than one way to partition data and that "true classes" in this context is a relative term.
We have focused on data that can be divided into two distinct classes, so that the results are more comparable between data sets. For some data sets several alternative class partitionings are possible. Here is a short description of the data sets and the classes that are used for the results presented in this paper. See also Table 1 for a summary.
Ye [19]
This data set includes samples from patients with hepatocellular carcinoma (HCC). The survival of the patient is highly correlated with metastasis. In the original study [19] the difference between metastatic and nonmetastatic patients is studied and this is also how we define our two classes; samples from patients with metastasis and samples from patients without metastasis.
Alizadeh [13]
This data set consists of samples from patients with diffuse large Bcell lymphoma (DLBCL) as well as samples from other lymphoid carcinomas (follicular lymphoma (FL) and chronic lymphocytic lymphoma (CLL), as well as normal cell samples and a variety of cell lines (see [13] for details)). The classes that we have used for this data are DLBCL and a class including all other samples.
Sørlie [18]
The purpose of the original study [18] was to classify breast carcinomas based on gene expression. Here, we want to use classes that are not defined by the gene expression, but rather by a clinical observation. In the hierarchical clustering in [18] the samples separate in two groups, one with low levels or no expression at all of ER and one with high ER expression. Therefore we decided to use classes based on the ER (estrogen receptor) status of the tumor sample i.e., the two classes are ER+ and ER.
Herschkowitz [16]
This is another breast cancer data set and we decided to use the same classes as for Sørlie i.e., ER status. In the original study the gene expression data was used to define novel cancer subtypes.
Jones [17]
The paper [17] aims to classify highgrade neuroendocrine tumors (HGNT) of the lung and in the study a number of different carcinomas as well as normal lung samples are included. Here, we have focused on the two most distinctive classes in this data set, the cancerous and the noncancerous samples.
Finak [14]
The original breast cancer study [14] investigate whether normal epithelium and stroma have distinct expression profiles compared to tumoradjacent normal tissue using Agilent microarrays. The conclusions are that morphologically normal epithelium and stroma exhibited distinct expression profiles, but molecular signatures that distinguished breast reduction tissue from tumoradjacent normal tissue are absent. The two classes that we use are defined by the distinguishable tissue types i.e., epithelial and stromal tissue.
Galland [15]
This data set consists of 40 nonfunctioning pituitary adenomas (NFPAs) classified as invasive or noninvasive on the basis of magnetic resonance imaging and surgical findings. The original study [15] used the Agilent microarray in order to identify genes with differential expression between invasive and noninvasive NFPAs. Here, we have used invasive and noninvasive NFPAs as our two classes.
All the data sets result from cDNA microarray experiments where a sample is compared to a common reference. The data values that we have been working on in this study are the log_{2}ratios (Mvalues) i.e., the logarithm with base 2 of the ratios between treated and reference channel.
Cluster analysis
In this paper, 2780 cluster analysis approaches for cDNAmicroarray data are considered. Here, a cluster analysis involves the consecutive subprocesses: normalization of the microarray data, imputation of missing values, gene selection, standardization and clustering. Within each subprocess several methods are considered. We have strived to include commonly used methods, but some rare methods have been included since they contrast the commonly used ones. The methods and the rationale for including them are described below.
Normalization
The methods considered in the normalization subprocess are the four combinations of two dyenormalization methods and two types of background correction. The two dyenormalization methods are global MAloess [20] and printtip (local) MAloess [21]. Both methods estimate the dye effect by modeling the log_{2}ratios (M) as a function of the average log_{2}intensity (A) using local regression (loess). In the global MAloess the dye effect is estimated on data from the entire array, while in the printtip version the dye effect is estimated individually on smaller subsets of the array. Both the dye normalization methods were applied to either background corrected data or data without background correction. For the background correction the local correction method is used [22], where the background estimates obtained from the image analysis are subtracted from the foreground estimates.
In addition to the normalized data the raw data is included in the study. Henceforth the raw and the normalized data are denoted as; no.norm  no normalization; norm.pt  printtip MAloess; norm.pt.bkg  printtip MAloess with background correction; norm.global  global MAloess and norm.global.bkg  global MAloess with background correction.
The considered normalization methods are commonly used, although printtip normalization without background correction is often recommended [23, 24]. Background correction generally decreases the experiments ability to get unbiased estimates of the genes regulation (i.e. the bias), but increases the variance and consequently decreases the experiments sensitivity [25]. Studies of how the tradeoff between bias and sensitivity affects the performance of the cluster analysis has to our knowledge not been done.
Filtration and missing value imputation
Spots flagged by the scanner or the experimentalist, and also spots with lower signal than background (when background correction is adopted) are marked as missing values, see e.g. [25] for a discussion on how to treat flagged spots. Only samples with less than 50% missing values and genes with less than 30% missing values are included (samples and genes are filtered simultaneously). The remaining missing values are imputed [8, 26]. Here, we have adopted two commonly used approaches for missing value imputation;
ROW A missing value is replaced by the row median, i.e. the median for that gene.
SVD The Rfunction svdImpute in the package pcaMethods implements the SVDimpute algorithm as proposed by [26]. This algorithm estimates missing values as a linear combination of the k most significant eigengenes. SVD (singular value decomposition) is used to find the set of orthogonal principal components or eigengenes that will be used to estimate the entire data matrix (through linear combination). For a particular gene with missing values, regression is used to find the coefficients for the eigengenes. In the regression, missing values are removed (and the corresponding values in the eigengenes are of course also removed). The coefficients are then used to estimate the missing values by linearly combining the eigengenes. The method is iterative. In the first round all missing values are replaced by 0 when the eigengenes are computed, in the following rounds the missing values are replaced by their estimates. The iteration stops once the change in the data matrix between two iterations is small enough.
In some of the data sets the annotation of the samples is incomplete and samples with unknown class identity had to be removed. Finally, the data matrix is further reduced by computing the mean values for duplicate genes.
Gene selection
In most microarray analyses it is common to select a subset of genes prior to the analysis. The purpose of the gene selection can for example be to reduce the overall noise in the data or to be able to perform a particular downstream analysis. Not performing gene selection can be an alternative as well, although simulations in have indicated that keeping irrelevant genes during cluster analysis result in reduced accuracy [9].
Detecting the relevant genes is not trivial and we include several different approaches for gene selection in this study. Below follows a list of gene selection methods that are adopted in this study;
STD Select the N genes that have the highest standard deviation [27]. This is a widely used method, motivated by the fact that a differentially expressed gene will have Mvalues (log_{2}ratios) with a relative high standard deviation. However, the drawback is that variable genes may have high standard deviation although they are not differentially expressed.
M Select the N genes with highest absolute log_{2}ratios (Mvalues). This is a natural method to apply when the common reference design is used and two treatments are compared to a common reference. It is based on the assumption that few genes are differentially expressed and that it is unlikely that one treatment is upregulated and the other downregulated against the common reference.
Consequently, a high absolute Mvalue implies that the gene may separate the classes. However, there is a risk that both treatments are equally differentially expressed towards the common reference.
where $\overline{M}$ denotes the mean value of M, ${\sigma}_{M}^{2}{,}_{modif}$ is the variance of M (modified according to BaldiLong [28]) and n is the number of samples. This method is to our knowledge rarely used, but we choose to include it for two reasons; it's a natural prolongation of the Mmethod and it contrasts the STDmethod.
PC Principal component analysis (PCA) is performed on the data matrix and the k most significant principal components ("eigengenes") are used in the clustering procedure (instead of a subset of genes). This commonly used method makes a linear projection of the data into a lowdimensional subspace defined by a number of principal components that explain the most variance.
NONE No gene selection is done, hence all genes are included (due to computational costs this means that the modelbased method (Mclust) is not used). This approach is surprisingly common, but previous studies have shown that clustering performance may be degraded when irrelevant genes are selected.
In all the above methods, except PC and NONE, the number of selected genes, N, has been set to 15, 100, and 1000 (Mclust is not run with 1000 genes). In PC, the number of principal components has been set to 3, 5, and 15. The numbers were selected at levels believed to cover what is reasonable in practical applications. For some analyses, additional levels were investigated.
In total this results in 13 unsuperwised gene selections, these will be referred to by their method abbreviation (as above) followed by a number referring to the number of selected genes or the number of principal components.
Class dependent gene selection
We also apply two methods that use the class information in the gene selection i.e., that select genes that can separate the classes. It is intuitive to believe that a clustering analysis conducted on discriminatory genes will retrieve the classes that these genes discriminate between. However, it is important to point out that with a gene selection of this type the cluster analysis can no longer be considered as an unsupervised method [29]. We still include these methods as a positive control. The two methods are;
Again the modified variances are approximated according to BaldiLong [28].
Mdiff Select the N genes with the largest difference in M between classes A and B, i.e. ${\overline{M}}_{A}{\overline{M}}_{B}$.
Standardization
Standardization is a technique to put equal weight to the genes and is commonly used when the cluster analysis is based on expression levels. However, for the common reference design the analysis is based on log_{2}ratios. We have included standardization using the Ztransformation [30], i.e. scaling of the genes to mean 0 and standard deviation 1. In addition, we have considered nonstandardized data. The normalization, missing value imputation and gene selection are performed before the potential standardization.
Clustering
We have applied five commonly used types of clustering methods. Some of these methods have several different settings and taking a carefully selected subset of these into account resulted in 11 different clustering methods.
hclust The agglomerative hierarchical cluster analysis included in this study is implemented in the Rfunction hclust included in the package stats. The clustering is based on a set of dissimilarities between the samples. Here we have used dissimilarities based on the Euclidean distance, the Manhattan distance or the Pearson correlation coefficient (transformed into a distance by taking 1 minus the correlation coefficient). In addition, we have used two different methods for computing similarities between clusters; Ward's method [31] and the average linkage (UPGMA) method [32]. We will refer to the hierarchical clusterings as hclust.dist.meth, where dist and meth are unambiguous acronyms for the distance measures (eucl, manh, corr) and agglomeration methods (ward, ave), respectively.
kmeans The kmeans clustering algorithm that we use here is implemented in the Rfunction kmeans (package: stats). In this function, 100 random starting sets (sets of k cluster centers) are used and the algorithm adopted is that of Hartigan and Wong [33].
PAM Partitioning of data into k clusters around medoids (representative objects) can be done using the Rfunction pam in the package cluster. We have adopted two variants of defining distances in the function; Euclidean distance and Pearson's correlation.
SOM (Selforganizing maps). SOMs represent multidimensional data in a lowdimensional topological map. The grid used here is onedimensional and the number of grid points equals the number of clusters. The implementation of SOM in the Rfunction som in the package kohonen [34] is used.
Mclust We employed modelbased clustering using expectation maximization initialized by hierarchical clustering for parametrized Gaussian mixture models [35]. Here, the Rfunction Mclust in the package mclust is used. Due to computational costs, Mclust is only adopted after gene selection reducing the data to 500 genes or less.
Cluster evaluation
There are more than one way to evaluate the quality of a clustering, both when the true class identities are known and when they are not. Measures of clustering quality that are independent of the true partitioning include the average silhouette width (asw) and the CalinskiHarabasz (CH) index [36, 37]. Although the focus here is on measures that compare true classes to a predicted clustering, measure such as asw and CH can also be of interest e.g., to determine the optimal number of clusters.
For all the data sets considered in this study the true number of clusters is two. However, to fix the number of predicted clusters to two might not be optimal. There can for example be a few outliers that are placed in clusters of their own, forcing the two interesting clusters to be merged. In a manual evaluation this would be trivial to detect. In an attempt to mimic a visual inspection of the clusters we have come up with the following procedure for cluster evaluation; Instead of two, we predict ten clusters. The clusters are then joined into two clusters in an optimal way by optimizing a similarity score comparing the true partitioning with the joined predicted partitioning.
In this study we use the adjusted Rand index (aRand) as a measure of performance, since this is a sensitive measure recommended in the literature [38, 39].
Rand and adjusted Rand index
for some k, k_{1}, k_{2} ∈ {1, ..., s} and l, l_{1}, l_{2} ∈ {1, ..., t}, where k_{1} ≠ k_{2} and l_{1} ≠ l_{2}.
Where E[ ] denotes the expected value. aRand is a number between 1 and 1.
Since we compute aRand after joining ten initial clusters into two by optimizing aRand, the expected value of aRand will be greater than 0. By simulating 1000 random partitionings (of ten classes) and computing optimal aRand in the same way, we can estimate the expected value and the distribution of aRand by chance. The expected value varies a bit between data sets as the true classes vary both in total number of samples and relative class sizes.
Results and Discussion
In this study we have included seven data sets and a large number of cluster analysis approaches (normalization, standardization, gene selection, missing value imputation, clustering method). In total we have performed 2780 analyses per data set (not counting the 1280 cluster analyses where the gene selection depends on the true classes). All analyses are applied to the seven data sets resulting in 19460 cluster analysis that are all compared to the true partitioning using a cluster evaluation score (see previous section).
The gene selection methods T2 and Mdiff use class informations to select genes and are included as a positive control (i.e. we can not expect gene selection methods that don't use the class information to do better than this), but we will not include the result from these selections methods in our general conclusions. If not stated specifically, the results presented in the text and figures in this section are based only on the gene selection methods STD, M, T1, PC and NONE.
Differences between the data sets
The performance of the clustering methods on the Sørlie data set is very close to what is expected by chance, indicating that the samples do not separate into ER and ER+ based on the expression data. Henceforth, the Sørlie data set will therefore be excluded from this study.
Overall influence of the subprocesses
To see the influence of the subprocesses, i.e. normalization, standardization, gene selection, missing value imputation and clustering method, we build a regression model using the aRand value as response variable and the subprocesses, as well as the choice of data set, as predictor variables. Also interaction terms of order two are included in the model. (The model is computed in R by the function lm and an analysis of variance table is computed using the Rfunction anova in the package stats).
The result shows that the data set is most important, explaining 26.5% of the total variability in the response variable. Second most important is the gene selection (including the number of genes to select) (15.0%), followed by clustering method (4.9%), normalization (0.66%), and standardization (0.01%). All of these variables have a significant influence on aRand (p < 0.001), whereas missing value imputation method is of minor importance.
Reduction of the number of analyses
In an attempt to find a robust and high performing cluster analysis, pair wise method comparisons within the most important subprocesses (normalization, gene selection method and clustering method; see Figure 3) are made to remove nonfavorable methods settings (e.g. within clustering methods, kmeans is compared to SOM etc.). The following iterative process for removing methods with relativly low performances is considered:

Within each subprocess all methods are compared pair wise with respect to the adjusted Rand using Wilcoxon's signed rank test. The resulting pvalues are corrected using Bonferroni's correction (i.e. multiplied by the total number of tests).

The pair of methods generating the minimum pvalue is identified and if the corrected pvalue is below 0.001 the pair's least favorable method is considered to be a nonfavorable method.

All cluster analyses involving the nonfavorable method are removed and the process continued until no more removals are made.
Parameter filtering
Removed method  pvalue  Superior method 

T1 15  9.468e207  PC 5 
T1 100  4.037e127  PC 5 
M 15  1.435e111  PC 5 
hclusteuclaverage  4.926e100  kmeans 
hclustmanhaverage  4.536e97  kmeans 
STD 15  1.556e88  STD 1000 
M 100  2.045e76  M 1000 
P0  9.134e75  P1 
PC 3  1.241e50  PC 15 
T1 1000  3.739e36  PC 15 
pameucl  4.036e32  kmeans 
PC 5  1.033e21  PC 15 
pamcorr  8.688e18  hclustcorrward 
som  1.340e13  kmeans 
hclustcorraverage  2.337e10  hclustcorrward 
M 1000  4.359e07  PC 15 
As can be seen in Figure 2B the distribution of aRand values for each data set after the above filtration are generally shifted towards higher aRand values (see also Figure 2C for the complement to the selected approaches).
This initial reduction process is used to state the following hypotheses:

Normalization of data is likely to increase the performance of cluster analyses.

The number of genes (or principal components) should be relatively high.

Gene selection methods using the adjusted tstatistic (T1) and the mean of the Mvalue (M) are likely to perform worse than the STD and PC selection methods.

Hierarchical clustering using Ward's method for calculating the distance between clusters is likely to perform better than analyses using average linkage.
The above hypotheses are further investigated by considering more detailed comparisons, based on the subprocesses that remained after the initial filtering unless otherwise stated.
In the following analyses all methods within a subprocess are compared against each other. In total 78, 55 and 10 pairwise comparisons are made for the unsupervised gene selection, clustering and normalization subprocesses, respectively. Two methods are considered significantly different if the Bonferroni corrected pvalue is less than 0.01.
Gene selection
STD and PC are the gene selection methods that perform best and STD perform significantly better than both M and T1 independently, regardless of how many genes that are selected.
Clustering methods
Hierarchical clustering using Ward's method and the correlation distance and kmeans perform significantly better than PAM and SOM, see Figure 7. Note that Mclust cannot be directly compared to other methods, since there are fewer data points to compare for Mclust as only gene selections with up to 100 genes are included. However, based on the analyses where genes are selected using PC5, PC 15, and STD 100, Mclust performs significantly better than PAM.eucl, hclust.eucl.ave, and hclust.manh.ave, but significantly worse than kmeans.
Normalizations
Five normalization procedures are compared i.e., 10 pair wise comparisons are made.
We will concentrate on the data sets that benefit most from normalization, i.e. Alizadeh, Galland and Jones. The Alizadeh and Jones data sets used custom made arrays and here both global and printtip normalization are applied. We cannot see any significant (adjusted p < 0.01) differences between these methods (data not shown) and choose to concentrate on the global method since the printtip method was not used on the Agilent data sets.
The choice of using or not using background correction has a clear impact on the performance, but there is no significant difference, see Figure 8. Arguably, methods using background correction have an increased variance for the observed aRand. This indicates that background correction works well in combination with some of the considered methods, but considerable worse with others. We argue that methods that are able to omit genes that cannot distinguish the true classes will work relatively well together with background correction. The reason for this is that background correction reduces the bias of affected genes (i.e. genes that can be used to separate the classes) and increases the variance of all genes [25]. The decrease in bias will improve the clustering, but the increase in variance will make gene selection more difficult and have a negative effect on the clustering. Hence, background correction should work relatively well if we only include a limited number of irrelevant genes. In order to test our hypothesis we perform gene selection using the supervised gene selection methods T2 and Mdiff with 100 genes. For the Jones data almost all analyses have aRand close to one and it is not possible to see any difference between the normalization methods, data not shown. Also for the Galland data there is no significant difference between the normalization methods. However, for the Alizadeh data the positive background correction effect (although nonsignificant) indicated using unsupervised gene selection become more evident after supervised gene selection (significant (adjusted p < 0.01, 10 tests)).
Conclusions
The optimal preprocessing and gene selection procedure is highly dependent on the data set subjected to the analysis and it is important to remember that the true classes used in this study are not the only alternative for dividing the data samples into classes. It is however clear that the choice of preprocessing, gene selection and clustering method does have an influence on the cluster analysis result. We also show that standardization and the choice of missing value imputation has a minor influence on the clustering. Of course missing value imputation as such is necessary, but the two methods (ROW and SVD) that we have adopted perform approximately equally well.
Furthermore, we show that normalization increases the performance and that there are some indications that background correction has a positive effect if a large proportion of the irrelevant genes can be filtered during gene selection.
Gene selection itself has a huge impact on the downstream cluster analysis and both the selection method and the number of selected genes are important. We show that gene selection does improve the results, but the number of selected genes need to be relatively high when using an unsupervised gene selection method. In a comparison between gene selection methods, the best alternatives are the standard deviation method and the principal component method. Furthermore, hierarchical clustering using Ward, kmeans clustering and Mclust are the clustering methods that perform best in this study.
In this study the performance of over thousand cluster analysis methods are evaluated. We have focused on the more general effects; but there are clear indications that interactions between subprocesses play a vital role. As an example, consider a scenario where the user has decided to use kmeans clustering on the 100 genes with highest standard deviation post standardization and imputation with row median, but are unsure of which normalization to apply. In this case Additional file 5 or 6 can be used to compare normalization procedures. For the above example the mean adjusted Rand index ranges from 0.610.69, with favor of normalization with background correction. Although it is not advisable to consider such an investigation as a general result it may provide guidelines to favorable method choices.
The study shows a high variability in the performance between the data sets. Consequently, there is a risk that the results are not valid for all experimental data. It should also be noted that our study focus on 2channel experiments with a common reference design and that the conclusions cannot necessarily be extended to other platforms and experimental designs.
In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.
Declarations
Acknowledgements
TRH and JÖ are funded by the Swedish Research Council (VR) and The Swedish Governmental Agency for Innovation Systems (VINNOVA).
Authors’ Affiliations
References
 Allison DB, Cui X, Page GP, Sabripour M: Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006, 7: 55–65. 10.1038/nrg1749View ArticlePubMedGoogle Scholar
 de Souto MCP, Costa IG, de Araujo DSA, Ludermir TB, Schliep A: Clustering cancer gene expression data: a comparative study. BMC Bioinformatics 2008, 9: 497. 10.1186/147121059497View ArticlePubMedPubMed CentralGoogle Scholar
 Thalamuthu A, Mukhopadhyay I, Zheng X, Tseng GC: Evaluation and comparison of gene clustering methods in microarray analysis. Bioinformatics 2006, 22(19):2405–2412. 10.1093/bioinformatics/btl406View ArticlePubMedGoogle Scholar
 Datta S, Datta S: Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 2003, 19(4):459–466. 10.1093/bioinformatics/btg025View ArticlePubMedGoogle Scholar
 Datta S, Datta S: Evaluation of clustering algorithms for gene expression data. BMC Bioinformatics 2006, 7(Suppl 4):S17. 10.1186/147121057S4S17View ArticlePubMedPubMed CentralGoogle Scholar
 Giancarlo R, Scaturro D, Utro F: Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer. BMC Bioinformatics 2008, 9: 462. 10.1186/147121059462View ArticlePubMedPubMed CentralGoogle Scholar
 Kim SY, Lee JW, Bae JS: Effect of data normalization on fuzzy clustering of DNA microarray data. BMC Bioinformatics 2006, 7: 134. 10.1186/147121057134View ArticlePubMedPubMed CentralGoogle Scholar
 Aittokallio T: Dealing with missing values in largescale studies: microarray data imputation and beyond. Brief Bioinformatics 2010, 11(2):253–64. 10.1093/bib/bbp059View ArticlePubMedGoogle Scholar
 Tritchler D, Parkhomenko E, Beyene J: Filtering genes for cluster and network analysis. BMC Bioinformatics 2009, 10: 193. 10.1186/1471210510193View ArticlePubMedPubMed CentralGoogle Scholar
 Jin J: Impossibility of successful classification when useful features are rare and weak. Proc Natl Acad Sci USA 2009, 106(22):8859–64. 10.1073/pnas.0903931106View ArticlePubMedPubMed CentralGoogle Scholar
 R Development Core Team:R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria; 2009. [http://www.Rproject.org]Google Scholar
 Dobbin K, Simon R: Comparison of microarray designs for class comparison and class discovery. Bioinformatics 2002, 18(11):1438–1445. 10.1093/bioinformatics/18.11.1438View ArticlePubMedGoogle Scholar
 Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM: Distinct types of diffuse large Bcell lymphoma identified by gene expression profiling. Nature 2000, 403(6769):503–511. 10.1038/35000501View ArticlePubMedGoogle Scholar
 Finak G, Sadekova S, Pepin F, Hallett M, Meterissian S, Halwani F, Khetani K, Souleimanova M, Zabolotny B, Omeroglu A, Park M: Gene expression signatures of morphologically normal breast tissue identify basallike tumors. Breast Cancer Res 2006, 8(5):R58. 10.1186/bcr1608View ArticlePubMedPubMed CentralGoogle Scholar
 Galland F, Lacroix L, Saulnier P, Dessen P, Meduri G, Bernier M, Gaillard S, Guibourdenche J, Fournier T, EvainBrion D, Bidart JM, Chanson P: Differential gene expression profiles of invasive and noninvasive nonfunctioning pituitary adenomas based on microarray analysis. Endocr Relat Cancer 2010, 17(2):361–371. 10.1677/ERC100018View ArticlePubMedGoogle Scholar
 Herschkowitz JI, Simin K, Weigman VJ, Mikaelian I, Usary J, Hu Z, Rasmussen KE, Jones LP, Assefnia S, Chandrasekharan S, Backlund MG, Yin Y, Khramtsov AI, Bastein R, Quackenbush J, Glazer RI, Brown PH, Green JE, Kopelovich L, Furth PA, Palazzo JP, Olopade OI, Bernard PS, Churchill GA, Dyke TV, Perou CM: Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol 2007, 8(5):R76. 10.1186/gb200785r76View ArticlePubMedPubMed CentralGoogle Scholar
 Jones MH, Virtanen C, Honjoh D, Miyoshi T, Satoh Y, Okumura S, Nakagawa K, Nomura H, Ishikawa Y: Two prognostically significant subtypes of highgrade lung neuroendocrine tumours independent of smallcell and largecell neuroendocrine carcinomas identified by gene expression profiles. Lancet 2004, 363(9411):775–781. 10.1016/S01406736(04)156936View ArticlePubMedGoogle Scholar
 Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lønning P, BørresenDale AL: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001, 98(19):10869–10874. 10.1073/pnas.191367098View ArticlePubMedPubMed CentralGoogle Scholar
 Ye QH, Qin LX, Forgues M, He P, Kim JW, Peng AC, Simon R, Li Y, Robles AI, Chen Y, Ma ZC, Wu ZQ, Ye SL, Liu YK, Tang ZY, Wang XW: Predicting hepatitis B viruspositive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nat Med 2003, 9(4):416–423. 10.1038/nm843View ArticlePubMedGoogle Scholar
 Dudoit S, Yang YH, Callow MJ, Speed TP: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 2002, 12: 111–140.Google Scholar
 Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res 2002, 30(4):e15. 10.1093/nar/30.4.e15View ArticlePubMedPubMed CentralGoogle Scholar
 Eisen MB: ScanAlyze User manual. 1999.Google Scholar
 Qin LX, Kerr KF, of the Toxicogenomics Research Consortium CM: Empirical evaluation of data transformations and ranking statistics for microarray analysis. Nucleic Acids Res 2004, 32(18):5471–5479. 10.1093/nar/gkh866View ArticlePubMedPubMed CentralGoogle Scholar
 Rydén P, Andersson H, Landfors M, Näslund L, Hartmanová B, Noppa L, Sjöstedt A: Evaluation of microarray data normalization procedures using spikein experiments. BMC Bioinformatics 2006, 7: 300. 10.1186/147121057300View ArticlePubMedPubMed CentralGoogle Scholar
 Fahlén J, Landfors M, Freyhult E, Bylesjö M, Trygg J, Hvidsten TR, Rydén P: Bioinformatic Strategies for cDNAMicroarray Data Processing.In Batch Effects and Noise in Microarray Experiments: Sources and Solutions Edited by: Scherer A. john Wiley & Sons, Ltd; 2009, 61–74. [http://onlinelibrary.wiley.com/doi/10.1002/9780470685983.ch6/summary] full_textView ArticleGoogle Scholar
 Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB: Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17(6):520–525. 10.1093/bioinformatics/17.6.520View ArticlePubMedGoogle Scholar
 Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L, Chan WC, Botstein D, Brown P: 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol 2000, 1(2):RESEARCH0003. 10.1186/gb200012research0003View ArticlePubMedPubMed CentralGoogle Scholar
 Baldi P, Long AD: A Bayesian framework for the analysis of microarray expression data: regularized t test and statistical inferences of gene changes. Bioinformatics 2001, 17(6):509–519. 10.1093/bioinformatics/17.6.509View ArticlePubMedGoogle Scholar
 Dupuy A, Simon RM: Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007, 99(2):147–157. 10.1093/jnci/djk018View ArticlePubMedGoogle Scholar
 Casella G, Berger RL: Statistical Inference Duxbury. 2002.Google Scholar
 Ward JH: Hierachical grouping to optimize an objective function. J Am Statist Assoc 1963, 58: 236–244. 10.2307/2282967View ArticleGoogle Scholar
 Sokal RR, Michener CD: A statistical method for evaluating systematic relationships. Univ Kans Sci Bull 1958, 28: 1409–1438.Google Scholar
 Hartigan JA, Wong MA: A Kmeans clustering algorithm. Applied Statistics 1979, 28: 100–108. 10.2307/2346830View ArticleGoogle Scholar
 Wehrens R, Buydens L: Self and Superorganising Maps in R: the kohonen package. J Stat Softw 2007., 21(5):Google Scholar
 Fraley C, Raftery AE: Modelbased clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 2002, 97: 611–631. 10.1198/016214502760047131View ArticleGoogle Scholar
 Rousseeuw P: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 1987, 20: 53–65. 10.1016/03770427(87)901257View ArticleGoogle Scholar
 Calinski R, Harabasz J: A dendrite method for cluster analysis. Communications in Statistics 1974, 3: 1–27. 10.1080/03610927408827101View ArticleGoogle Scholar
 Steinley D: Properties of the HubertArabie adjusted Rand index. Psychological methods 2004, 9(3):386–396. 10.1037/1082989X.9.3.386View ArticlePubMedGoogle Scholar
 Milligan GW, Cooper MC: A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis. Multivariate Behavioral Research 1986, 21(4):441–458. 10.1207/s15327906mbr2104_5View ArticlePubMedGoogle Scholar
 Rand WM: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 1971, 66: 846–850. 10.2307/2284239View ArticleGoogle Scholar
 Cox TF, Cox MAA: Multidimensional Scaling. Chapman and Hall; 1994.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.