Classification is a supervised learning approach, in which the classes (or labels) of a subset of samples are inputs to the algorithm. This is in contrast to clustering, which is an unsupervised approach, in which no knowledge of the samples is assumed. A training set is a set of samples for which the classes are known. A test set is a set of samples for which the classes are assumed to be unknown to the algorithm, and the goal is to predict which classes these samples belong to. The first step in classification is to build a 'classifier' using the given training set, and the second step is to use the classifier to predict the classes of the test set.

In the context of gene-expression data, the samples are usually the experiments, and the classes (or labels) are usually different types of tissue samples (for example, cancer versus non-cancer, different tumor types, rate of disease progression, and response to therapy). A typical microarray dataset consists of thousands to tens of thousands of genes, and dozens to hundreds of experiments. One challenge of classification using microarray data is that the number of genes is significantly greater than the number of samples. In this situation, it is possible to find both random and biologically relevant correlations of gene behavior with sample type. To protect against spurious results, the goal is to identify the smallest possible subset of genes that correlate most strongly with the known class labels. In addition, a small subset of genes is desirable for the development of expression-based diagnostics. The problem of selecting relevant genes (or features) for classification is known as feature selection.

Cross validation is a well-established technique used to optimize the parameters or features chosen in a classifier. In m-fold cross-validation, the training set is randomly divided into m disjoint subsets with roughly equal size. Each of these m subsets is left out in turn for evaluation, and the other (m - 1) subsets are used as inputs to the classification algorithm. In this work, we randomly divide each class into m disjoint subsets (where m is less than the size of the smallest class in the training set), so that each class is represented in the subset fed to the classification algorithm. The left-out subset of the training set is used to evaluate classification accuracy because the classes of this subset are known. The most popular form of cross-validation is leave-one-out cross-validation (LOOCV), in which m is equal to the number of samples in the training set, and each sample in the training set is left out in turn to evaluate the prediction results.

van't Veer et al. 14 recently applied a binary classification algorithm to cDNA array data with repeated measurements, and classified breast cancer patients into good and poor prognosis groups. Their classification algorithm consists of the following steps. The first step is filtering, in which only genes with both small error estimates and significant regulation relative to a reference pool of samples from all patients are chosen. The second step consists of identifying a set of genes whose behaviour is highly correlated with the two sample types (for example, upregulated in one sample type but downregulated in the other). These genes are rank-ordered so that genes with the highest magnitudes of correlation with the sample types have top ranks. In the third step, the set of relevant genes is optimized by sequentially adding genes with top-ranked correlation from the second step. Leave-one-out cross-validation is used to evaluate and choose an optimal set of features. van't Veer et al.'s approach takes variability estimates of repeated measurements into consideration by using error-weighted correlation in their method. However, this method involves an ad hoc filtering step and does not generalize to more than two classes.

Ramaswamy et al. 10 combined support vector machines (SVMs), which are binary classifiers, to solve the multiclass classification problem. They showed that the one-versus-all approach of combining SVM yields the minimum number of classification errors on their Affymetrix data with 14 tumor types. The one-versus-all combination approach builds k (the number of classes) binary classifiers, each of which distinguishes one class from all the other classes. Suppose binary classifier i predicts a discriminant value f_i(x) for a given sample x in the test set. The combined multiclass classifier assigns sample x to the class for which the corresponding binary classifier produces the highest discriminant value. In addition to not taking variability estimates of repeated measurements into account, this approach selects different relevant features (genes) for each binary classifier.

Nguyen and Rocke 2122 used partial least squares (PLS) for feature selection, together with traditional classification algorithms such as logistic discrimination and quadratic discrimination to classify multiple tumor types from microarray data. These traditional classification algorithms require the number of samples (experiments) to be greater than the number of variables (genes), and it is therefore essential to reduce the dimensionality before applying these traditional classification techniques. PLS is a dimension-reduction technique that maximizes the covariance between the classes and a linear combination of the genes. This approach can be generalized to multiple classes, but it does not make use of variability estimates of the data. In addition, it is a multistep process that involves a filtering step (to select genes with significant mean differences) and then application of PLS to further reduce the dimensionality so that the number of samples is greater than the number of dimensions.

Dudoit et al. 23 compared the performance of different discrimination methods (including nearest neighbor classifiers, linear discriminant analysis and classification trees) for classifying multiple tumor types using gene-expression data. None of the discrimination methods they evaluated takes measurement variability into consideration, and their emphasis is on discrimination methods and not feature selection.

Yeung et al. 24 showed that clustering algorithms that take advantage of repeated measurements (including the error-weighted approach that down-weights noisy measurements) yield more accurate and more stable clusters. Here, we will focus on the supervised learning approach, instead of the unsupervised clustering technique.

Tibshirani et al. 17 developed a 'shrunken centroid' (SC) algorithm for classifying multiple cancer types. It is an integrated approach for feature selection and classification. Features are selected by considering one gene at a time: the difference between the class centroid (average expression level or ratio within a class) of a gene and the overall centroid (average expression level or ratio over all classes) of a gene is compared to the within-class standard deviation plus a 'shrinkage threshold' which is fixed for all genes. The intuition is that genes with at least one class centroid that is significantly different from the overall centroid are selected as relevant genes. The size of the shrinkage threshold is determined by cross-validation on the training set to minimize classification errors.

Our algorithms have the following desirable characteristics. Both EWUSC and USC exploit the interdependence of genes to reduce the number of selected features. EWUSC takes advantage of the variability of gene-expression data over repeated measurements, so no ad hoc filtering step is necessary. Both EWUSC and USC can be applied to data with any number of classes. Both EWUSC and USC adopt an integrated approach for both feature selection and classification. Both algorithms make no assumption on data distributions.

We illustrate the advantage of removing correlated genes (for example, the USC algorithm) on the NCI 60 data 12 for which there is no variability information. This dataset has been extensively used in other publications for classification algorithm development 222325. We illustrated and compared our USC and EWUSC algorithms with two real datasets: a multiple tumor dataset from Ramaswamy et al. 10 and a breast cancer dataset from van 't Veer et al. 14. These two datasets were chosen as they are publicly available in a form from which we can calculate or obtain error estimates for each gene-expression level or ratio. We used a subset of the multiple tumor data 10 that consists of 7,129 genes and 11 tumor types on Affymetrix chips. There are 96 samples in the training set, and 27 samples in the test set. For the Affymetrix dataset we estimated the variability in the gene-expression levels using the robust multi-array analysis (RMA) tool 2627 from the BioConductor project 28. A subset of the published data was used as we could only obtain raw data (.cel files) for a subset. The breast cancer dataset 14 consists of 25,000 genes with four repeated measurements on cDNA arrays. There are 78 samples in the training set, 19 samples in the test set, and two classes of patients: one class with good prognosis (with more than 5 years of survival time), and another class with poor prognosis (with less than 5 years of survival time). For the breast cancer cDNA array data, published p-values as calculated by Rosetta's Resolver software were used to calculate the error estimates. In addition, we created synthetic datasets with repeated measurements and compared the performance of EWUSC, USC and SC at different noise levels.

We adopted three criteria for assessing feature selection and classification algorithms: prediction accuracy, number of relevant genes and feature stability. Prediction accuracy is defined as the percentage of correct classifications on the test set. The number of relevant genes is the total number of genes used to achieve optimal prediction accuracy. Feature stability is the level of agreement of selected genes chosen over different cross-validation runs of the algorithm.

Using these algorithms we obtained the following general results. Exploiting gene interdependence by removal of correlated genes typically results in comparable or higher prediction accuracy using fewer relevant genes. This is highly desirable if one wishes to develop diagnostic tools from the selected set of genes. Using error or variability estimates as weighting factors generally yields higher feature stability and reduces the number of relevant genes on real datasets. On the multiple tumor data, our EWUSC algorithm achieves 16% increase in prediction accuracy, using only 10% of the genes as features (compared with using all the available genes in the published result). On the breast cancer data, our EWUSC algorithm produces the same number of classification errors as the published result using a larger feature set. Unlike the published algorithm for this dataset, however, the EWUSC algorithm is applicable to datasets with more than two classes.

The SC approach 17 is essentially a robust version of the 'nearest centroid' approach, in which a sample is assigned to the class with the nearest average pattern. Features are selected by considering each gene individually. The overall centroid of a gene i is defined as the average expression level/ratio of gene i over all the experiments. The class centroid of a gene i in class k is defined to be the average expression level/ratio of gene i over all the samples in class k. A gene is predictive of the class if at least one of its class centroids significantly differs from its overall centroid. One obvious definition of significantly in the previous sentence is 'differs by more than the variation (or standard deviation) within the class', which is essentially a modified form of a t-test. The shrunken centroid method adds an additional term (s₀ described in 17 and in the section Details of algorithms below) to the within-class standard deviation - for example, the difference between the in-class average and the overall average must exceed the in-class variation by s₀. A t-test like statistic, relative difference (d_ik), is defined to represent the difference between the class centroid and the overall centroid divided by the variance (in-class variation + s₀) and the absolute value of d_ik is reduced by the 'shrinkage threshold' Δ. Δ is determined by cross-validation such that the number of classification errors is minimized on the training set.

Our USC algorithm adds a step to the SC algorithm to remove redundant, correlated genes. The benefit of removing highly correlated genes is twofold. First, it reduces the number of relevant features (genes) needed for classification. A small feature set is highly desirable if one wishes to use the results of feature selection and classification to develop diagnostic tools such as reverse transcription PCR (RT-PCR)-based tests on a small number of the most relevant genes. Second, the removal of redundant genes reduces the impact of over-fitting, and hence, potentially improves classification accuracy.

The SC algorithm produces a set of relevant genes, S_Δ, for any given shrinkage threshold Δ. As Δ increases, the number of relevant genes in S_Δ decreases; for example, the gene list is reduced to selected genes for which the within-class centroids are farther away from the overall centroid and for which the within-class variation is small. Each gene is considered independently in the SC algorithm. Our modification exploits the correlation between genes by removing genes that are highly correlated within the set of relevant genes S_Δ. Specifically, we compute the pairwise correlation for each pair of genes (g_i, g_j) in S_Δ for each Δ. If the pairwise correlation is greater than a correlation threshold ρ₀, the gene g_j with the smaller relative difference is removed from the set of relevant genes. This results in a set of relevant genes S(Δ, ρ₀) for each shrinkage threshold Δ and each correlation threshold ρ₀. These relevant genes are used to classify new samples. The USC algorithm is equivalent to the SC algorithm when no correlated genes are removed (that is, ρ₀ = 1). We apply this USC algorithm to the training set using cross-validation to determine the number of classification errors for each Δ and each ρ₀. The optimal parameters for Δ and ρ₀ are chosen such that the number of cross-validation classification errors is minimized on the training set. These optimal parameters are then used to classify samples from unknown classes on the test set. Our results show that the removal of correlated genes provides a significant improvement over the SC algorithm in classification results, and hence our USC algorithm is useful for datasets in which error estimates are not available.

Our EWUSC algorithm is based on the USC algorithm with a key modification: we take advantage of error estimates or variability over repeated measurements. We define an error-weighted overall centroid, error-weighted class centroid, error-weighted relative difference, error-weighted shrunken class centroid, and error-weighted discriminant score in order to down-weight both noisy genes and noisy experiments. In addition, we adopt the error-weighted correlation in the removal of highly correlated genes to select relevant genes. Thus the EWUSC algorithm is identical to the USC algorithm except for error-weighted definitions to down-weight noisy genes and noisy experiments in our calculations. When all genes and all experiments have the same variability estimates, the EWUSC algorithm is equivalent to the USC algorithm. As our results show, this error-weighted approach typically reduces the number of relevant genes and improves feature stability, and thus the EWUSC is usually the method of choice when error or variability estimates are available. A detailed description of the EWUSC algorithm is given later in the paper.

In the NCI 60 data 12, cDNA microarrays were used to study the expression of approximately 60 cell lines derived from tumors with different sites of origin (see Table 1). We used the same pre-processed dataset as in Dudoit et al. 23, which consists of log expression ratios of 5,244 genes over 61 experiments. Two prostate and one unknown cell lines from the original data 12 were excluded in their analysis because of their small class sizes. Only one leukemia and one breast cancer cell line were repeated three times, and hence there are no repeated measurements or variability estimates available for all 61 samples. These repeated experiments of the leukemia and breast cancer cell lines are treated as individual samples. In addition, no additional test set is available for this data. To compare our results with those of Dudoit et al. 23, we adopted their 2:1 scheme in which one third of the samples are reserved as a test set.

Specifically, we randomly divided each class in the original data (61 experiments) into roughly three parts such that the training set consists of a total of 43 experiments and the test set consists of a total of 18 experiments. Table 2 gives the class sizes of the training and test sets. The optimal parameters are determined using cross-validation on the training set with 43 samples, and these optimal parameters are used to classify the 18 samples in the test set. We repeated this random partition of the original data into three parts multiple times.

The multiple tumor dataset 10 consists of a large number of tumor samples spanning 14 different tumor types hybridized to Affymetrix chips. On the Affymetrix platform, each target gene is represented by 11-20 short oligo probes of approximately 25 base-pairs (bp). Our goal is to take advantage of the variability over different oligos for the same genes using our EWUSC algorithm. We pre-processed the raw multiple tumor data with the log scale robust multi-array analysis (RMA) measure 27 implemented in the BioConductor project. The RMA measure is a summary statistic for the expression levels over all the different oligos for the same gene. The standard error of the RMA measure is a variability estimate of the expression level over the different oligos representing the same target gene. In order to obtain the RMA measures and their associated standard errors on the multiple tumor data, the raw data (.cel files) are necessary. Because we have access to only a subset of the raw multiple tumor data, we used a subset of the original data in our study. The subset of multiple tumor data we used consists of 7,129 genes, 96 samples in the training set, and 27 samples in the test set. These samples span 11 different tumor types (Table 3). The smallest class size is four on the training set, and hence, four-fold cross-validation (m = 4) is used on this data.

The breast cancer data 14 consists of primary breast tumor samples hybridized to cDNA arrays containing approximately 25,000 genes. Two hybridizations were carried out for each sample using a dye-reversal technique. Hence, there are four repeated measurements for each gene and each sample. The p-values of log expression ratios are also available. These p-values are results of the four repeated measurements and an error model based on extensive control experiments 29. A p-value close to 1 represents low confidence that an expression ratio is significantly different from 1, while a p-value close to 0 represents high confidence that an expression ratio is significantly different from 1. We converted these p-values into error estimates of log ratios, which are used in our EWUSC algorithm.

The breast cancer dataset consists of approximately 25,000 genes, 78 samples in the training set, and 19 samples in the test set. van't Veer et al. 14 divided these samples into the good and poor prognosis groups, which have greater than 5 and less than 5 years of survival time respectively. Hence, there are two classes in this dataset (see Table 4). We performed 10-fold cross-validation (m = 10) on the breast cancer data.

We also created synthetic datasets to compare the performance of our algorithms. Our approach is to start with 'patterned genes' which have a different expression pattern in each class, and are therefore relevant in classifying unknown samples. The next step is to introduce noise (variation in both the class and non-class values) to these patterned genes in order to reflect 'real-life' data. Finally, 'non-patterned genes', which are irrelevant in classifying samples, are added to these synthetic datasets. Even with this simple synthetic data-generation approach, generating sensible synthetic data turned out to be a nontrivial task. There are two parameters that control the noise levels in the synthetic datasets, the biological noise level (α) and the technical noise level (λ). The biological noise level (α) controls the level of biological noise within each class (and hence, the signal-to-noise ratio) such that the classes are less separable with a higher α. The technical noise level (λ) controls the noise level over repeated measurements such that a high λ indicates relatively noisy repeated measurements. The primary difficulty in generating synthetic data is setting the parameters of α and λ, and the proportion of the patterned genes. As it is not obvious how to set these parameters to reflect 'real-life' data, we experimented with different parameter settings, such as different biological noise levels: low (α = 0.1 with signal-to-noise ratio approximately 20), medium (α = 1 with signal-to-noise ratio approximately 2), or high (α = 2 with signal-to-noise ratio approximately 1); and low (λ = 1) or high (λ = 5 or 10) technical noise. We also experimented with different proportions of patterned genes, and concluded that this parameter does not have any significant impact on the results.

Another issue in generating 'realistic' synthetic data involves the generation of non-patterned genes that are irrelevant in distinguishing the classes. We addressed this issue by random sampling with replacement from a real dataset (that is, the breast cancer dataset 14). Specifically, for each non-patterned gene, we randomly sample a gene g from the breast cancer data, and then randomly sample from the experiments of gene g in the breast cancer data such that these non-patterned genes would not show any class-specific expression patterns but would show realistic variations in expression levels over all classes.

In particular, our synthetic training sets consist of 1,000 genes, 80 samples, and 4 classes such that there are 20 samples in each class. Our synthetic test sets consist of 1,000 genes and 40 samples with 10 samples in each class. We generated 64 patterned genes which have a different expression pattern in each class, for example, genes that are upregulated (or downregulated) in only m of the four classes, where m = 1, 2, 3. In addition, there are five duplicates of each of these 64 patterned genes such that there are a total of 320 patterned genes and (1,000 - 320 = 680) non-patterned genes. Ideally, the perfect classification algorithm would select only one of these five copies of the patterned genes. We also investigated the effect of the number of repeated measurements by generating synthetic datasets with 1, 4 or 20 repeated measurements. These synthetic datasets are available from our supplementary website 30.

As the class information for the test sets is available, we define prediction accuracy as the percentage of correct classifications on the test set. The class information on a test set is used only to evaluate the performance of classification and feature-selection algorithms, and is unknown to the algorithms.

One of the goals of classification is to select a minimal set of relevant genes (or features) that can be used in future diagnosis or classification of tissue samples. We judge each method by the total number of relevant features required for optimal classification accuracy. A small set of relevant genes is desirable because it is more cost-effective in the development of diagnostic tools based on the results of expression analysis. For example, the cost of an RT-PCR test to classify patient samples is directly proportional to the number of genes which must be tested to make the diagnosis. As shown below, both the USC and EWUSC methods usually result in a significant reduction in the numbers of selected genes for classification. We feel this represents a major advance in classification algorithms.

Because relevant genes are derived from the training set and the choice of the training set is often arbitrary, a set of relevant genes that is insensitive to the training sets used would be desirable. Hence, we define feature stability as the level of agreement between the set of relevant genes chosen in each fold of the cross-validation data with the set of relevant genes chosen using the full training set. Specifically, for each fold of the cross-validation data and for each set of parameters (Δ and ρ₀), we compute the Jaccard index 31 which measures the level of agreement between the set of relevant genes chosen in this fold and the set chosen using the full training set. The Jaccard index lies between 0 and 1. A high Jaccard index (close to 1) implies high level of agreement, and hence, high feature stability (a mathematical definition of the Jaccard index can be found in the section Details of algorithms, below). We define feature stability of one cross-validation run for a given set of parameters (Δ and ρ₀) as the average Jaccard index over all m folds of cross-validation. In our experiments, we usually have five random runs of cross-validation; hence we adopt the average Jaccard index over these five random runs of cross-validation as our measure of overall feature stability for given parameters (Δ and ρ₀).

Ramaswamy et al. 10 reported 78% classification accuracy on the multiple tumor data using SVMs combined using the one-versus-all approach. In contrast, our EWUSC algorithm achieves a classification accuracy of 93% on the test set of the multiple tumor data. As we used a subset of the original multiple tumor data and pre-processed the raw data using the RMA measures 27, we evaluated the performance of SVM combined with the one-versus-all method on the identical pre-processed subset of multiple tumor data used in our experiments with the EWUSC and the USC algorithms. In our comparison study, we used the signal to noise (S2N) measures 9 to select relevant features for each binary SVM classifier. To produce directly comparable results, we used the exact same five splits of the training set into cross-validation data.

Figure 4 compares the prediction accuracy on the test set of the multiple tumor data using the EWUSC and USC algorithms at the estimated optimal correlation threshold (ρ₀ = 0.8), the SC algorithm 17 and SVM (with S2N for feature selection). There are a few observations from Figure 4. First, USC produces higher prediction accuracy than SC using the same number of relevant genes. As SC is equivalent to USC at ρ₀ = 1, our results show that removing highly correlated genes reduces the number of relevant genes and improves prediction accuracy. Second, EWUSC generally produces higher prediction accuracy than USC using the same number of relevant genes, except when both the number of relevant genes and prediction accuracy is low. This shows that we can potentially improve prediction accuracy by taking advantage of error estimates in the data.

Third, our SVM results (on a subset of the multiple tumor data pre-processed with RMA measures) are generally much better than the published result of 78% 10 (on the full dataset pre-processed with MAS 4). Fourth, SVM with S2N as our feature-selection method produces high prediction accuracy at the expense of using a lot of relevant genes. For example, SVM requires a total of 1,699 genes over all the binary classifiers to achieve 93% prediction accuracy, whereas our EWUSC algorithm requires only 610 relevant genes to achieve the same prediction accuracy. If we are willing to trade off prediction accuracy with the number of relevant genes, EWUSC achieves 89% prediction accuracy with only 89 relevant genes.

Let x_ij be the expression level for gene i = 1, 2, ..., p and samples j = 1, 2, ..., n. Suppose there are a total of K classes, and let C_k be the set of all n_k samples in class k. The overall centroid of gene i is,

,

and the class centroid of class k and gene i is,

.

The relative difference, d_ik, is the difference in class centroid () and overall centroid (), standardized by the within-class standard deviation of gene i (s_i); that is,

,

where

,,

and s₀ is the median value of the s_is over all genes i. The relative difference d_ik is similar to a t-statistic, comparing the class centroid to the overall centroid. The shrunken relative difference d'_ik reduces d_ik by an amount Δ if |d_ik| > Δ, otherwise, sets d'_ik to zero; that is,

.

Hence, d'_ik gets rid of genes with class centroids not significantly different from the overall centroids. The amount of shrinkage Δ is determined by m-fold cross-validation such that the number of cross-validation classification errors is minimized. Genes with at least one positive shrunken relative difference d'_ik (over all classes k) are selected as relevant features. The shrunken class centroid () is defined as . The discriminant score for a new sample x* and class k is defined as

,

where π_k = n_k/n. The first term in the discriminant score represents the standardized squared distance of x* to the shrunken class centroid, and the second term represents a correction for the class prior probability. Sample x* is assigned to the class k with the minimum discriminant score.

We define feature stability as the average level of agreement between the set of relevant genes chosen in a fold of the cross-validation data and the set of relevant genes chosen using the full training set over all m folds of the cross-validation data. Let S(Δ, ρ₀) be the set of relevant genes chosen using the entire training set, and let S(m, Δ, ρ₀) be the set of relevant genes chosen in the mth fold of the cross-validation data with parameters Δ and ρ₀. We define the number of true positives (TP) as the number of relevant genes chosen in both S(Δ, ρ₀) and S(m, Δ, ρ₀). Similarly, we define the number of false positives (FP) as the number of relevant genes chosen in S(m, Δ, ρ₀) but not in S(Δ, ρ₀), and the number of false negatives (FN) as the number of relevant genes chosen in S(Δ, ρ₀) but not in S(m, Δ, ρ₀). The Jaccard index, J(m, Δ, ρ₀), is defined as TP/(TP + FP + FN). Intuitively, the level of agreement is high when there are many true positives, and relatively few false positives and false negatives. Hence, a high Jaccard index indicates a high level of agreement. Feature stability is the average Jaccard index over all m folds; that is, J(Δ, ρ₀) = average of J(m, Δ, ρ₀) over all m folds.

The basic idea behind SVM 33 is that it maps data points to a high-dimensional space such that the data points are linearly separable. However, SVM avoids computations in high-dimensional space by the use of kernel functions, which allows computations in the input space. There are many different types of kernel functions, with different effects. Brown et al. 34 showed that the radial kernel functions work very well in classifying genes on array data.

We augmented the SVM implementation by Noble et al. 35 to incorporate the signal to noise (S2N) measure for feature selection. The S2N measure is defined as the difference of the means in the two classes divided by the sum of the standard deviations of the two classes. Because we adopt the one-versus-all approach 1036 to combine the binary SVM classifiers, each binary classifier distinguishes samples of a given class from samples from all the other classes. The multiple tumor dataset consists of 11 classes (see Table 3 for details), and so there is a total of 11 binary SVM classifiers for this data. We applied the S2N measure to select a given number of relevant genes on the four-fold cross-validation data using a binary SVM classifier (with a radial kernel function). We then combined the results from each of the 11 SVMs by assigning the sample to the class of the classifier with the maximum discriminant value. This process is repeated for each of the five random fourfold splits of the training set. The results on the cross-validation data are shown in Figure S7(a) on 30, in which the average number of classification errors is plotted against the number of relevant genes chosen. The next step is to apply this process to the entire training set, and use the selected genes to classify the samples on the test set. The results on the test set are shown in Figure S7(b) on 30, in which the number of classification errors on the test set is plotted against the number of relevant genes chosen.

In order to process the multiple tumor data 10 with the RMA measure implemented in the Bioconductor project, we need the raw data (.cel files) which contain the expression level for each oligo (probe cell). The original multiple tumor data consists of 14 tumor types which were hybridized to both the Affymetrix Hu6800 and Hu35K chips. However, only a subset of the original '.cel' files (mostly data from the Hu6800 chips) is available. Hence, the subset of the multiple tumor data we used consists of all the 7,129 genes on the Hu6800 chips and 11 tumor types, with 96 samples in the training set and 27 samples in the test set. Table 3 shows the tumor types and class sizes for both the training and test sets.

The log ratios and their associated p-values are available from the breast cancer data. The p-values are confidence measures that expression ratios are significantly different from 1. Using the error model documented in the 'Error Model' supplement of Hughes et al. 29, we converted the p-values into error estimates. Assuming the distribution of error magnitudes can be approximated by the normal distribution, significance values (or p-values) can be derived from the Gaussian error function of the ratio of an observed log expression ratio to its error estimate 29. The p-value (p) for an observed log ratio (r) is related to the error estimate of the observed log ratio (s) by p = 2 * (1 - Erf(|X|) where X represents the ratio of an observed log expression ratio (r) to its error estimate (σ) and Erf is the Gaussian error function. Hence, the error estimates of the log expression ratios can be derived from the p-values. However, when a p-value is equal to 1, the error estimate is arbitrarily large. Hence, we ignored the corresponding expression ratio in our EWUSC algorithm when its p-value is equal to 1.

The synthetic training sets consist of 1,000 genes, 80 samples, and four classes such that there are 20 samples in each class, and the synthetic test sets consist of 1,000 genes and 40 samples with 10 samples in each class. Two parameters control the noise levels in the synthetic datasets - the biological noise level (α) and the technical noise level (λ). Let P be the matrix of patterns with 64 rows and 4 columns such that each entry P [i,j] is the ith pattern of class j (i = 1,2,..., 64, j = 1,2,3,4). Table 8 shows the pattern matrix P used to generate synthetic datasets in our study. Let X(i, j) be the true expression value of gene i under experiment j before technical noise is added. Let Y(i, j, r) be the rth measured expression value of gene i under experiment j, where i = 1, 2, ..., p, j = 1,2, ..., n, r = 1,2,..., R. Suppose gene i is generated from the mth patterned gene that belongs to class k. X(i, j) is generated from the random normal distribution with mean P [m,k] and standard deviation α. Technical noise is randomly sampled from a real dataset. Four hybridizations were repeated on the yeast galactose data 32, and the standard deviation of each gene under each experiment is adopted as our estimated technical noise. Let ε be the randomly sampled technical noise (standard deviation over four repeated measurements) from the yeast galactose data 32. Y(i,j,r) is generated from the random normal distribution with mean X(i,j) and standard deviation ελ. Hence, a high technical noise level λ indicates noisy repeated measurements. Moreover, there are five duplicates of each of these 64 patterned genes so that there is a total of 320 patterned genes. Each of these five duplicated patterned genes is generated using the same row in the pattern matrix P.

For non-patterned genes, we randomly sample from the breast cancer data such that these non-patterned genes do not exhibit any class-specific expression patterns. Specifically, let q be a non-patterned gene. Suppose we randomly sample a gene g and experiment e from the breast cancer data such that E[g,e] is the expression ratio of gene g and experiment e and s[g,e] is the error estimate of gene g and experiment e in the breast cancer data. Y(q,j) is generated from a random normal distribution with mean E[g,e] and standard deviation s[g,e] for sample j in the synthetic training or test set. Note that all expression values of the non-patterned gene q are sampled from the same gene g (which is chosen randomly) from the breast cancer data. As experiment e is independently sampled for each sample j, any class specific expression pattern in the original breast cancer data would be destroyed.

Both the synthetic training and test sets are generated using the same model described above. In our experiments, we set p = 1000, α = 0.1, 1 or 2, and λ = 1 (low technical noise) or 10 (high technical noise) with R = 1 or 4 or 20 repeated measurements. We also experimented with synthetic datasets with a higher fraction of non-patterned genes and showed that these larger datasets produce similar results (data not shown).