Gene expression microarray data analysis demystified

Gene expression microarray data analysis demystified

29 Gene expression microarray data analysis demystified Peter C. Roberts VizX Labs, 200 West Mercer Street, Suite 500, Seattle, WA 98119, USA Abstrac...

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Gene expression microarray data analysis demystified Peter C. Roberts VizX Labs, 200 West Mercer Street, Suite 500, Seattle, WA 98119, USA Abstract. The increasing use of gene expression microarrays, and depositing of the resulting data into public repositories, means that more investigators are interested in using the technology either directly or through meta analysis of the publicly available data. The tools available for data analysis have generally been developed for use by experts in the field, making them difficult to use by the general research community. For those interested in entering the field, especially those without a background in statistics, it is difficult to understand why experimental results can be so variable. The purpose of this review is to go through the workflow of a typical microarray experiment, to show that decisions made at each step, from choice of platform through statistical analysis methods to biological interpretation, are all sources of this variability. Keywords: microarray, microarray data analysis, gene expression, normalization, preprocessing, statistical analysis, clustering, cross-platform comparison, correction for multiple testing, pathway, gene ontology.

Introduction Since the original description of the use of cDNA microarrays in gene expression analysis in 1995 [1], followed a year later by oligonucleotide arrays [2], the technology has rapidly moved from the domain of specialists to being available to the whole research community, through core facilities at most academic research institutions. The arrays themselves have evolved from representing less than 50 genes, to now addressing over 50,000 transcripts on whole genome arrays for complex mammalian genomes, in some cases represented by over 1 million individual features. These arrays have traditionally measured the differential expression of known and putative protein-coding genes. The gene expression microarray data analysis process can be broken down into three main parts: preprocessing, the conversion of the signal from an array scanner to a normalized value appropriate for comparison of expression across the arrays in a study; comparative statistical analysis, to identify significantly differentially expressed genes or co-expressed genes; and biological interpretation, preferably with a statistical measure of significance. Early microarray experiments only considered the difference in expression of a gene between two sets of samples, producing highly variable results; thus, it was soon realized that statistical analysis was required to obtain meaningful Tel.: +1-206-283-4363. Fax: +1-206-283-1606.

E-mail: [email protected] (P.C. Roberts). BIOTECHNOLOGY ANNUAL REVIEW VOLUME 14 ISSN 1387-2656 DOI: 10.1016/S1387-2656(08)00002-1

r 2008 ELSEVIER B.V. ALL RIGHTS RESERVED

30 results. Initially standard statistical methods were used to identify significantly differentially expressed genes and these are still widely employed. However, the low number of replicates used in a typical microarray study; the variability introduced by the many technical steps in preparing samples; and large number of individual tests being performed on a single array; are inappropriate for standard statistical methods, so new statistical analysis methods have been developed. Many of these have been implemented in the opensource statistical language R [3], usually as part of the Bioconductor project [4]. Unfortunately these different approaches can result in quite different results; consequently, no ‘best method’ can be identified. Recently it has been realized that there are common themes in these approaches [5], helping to identify the most appropriate methods. As microarrays have become more widely accessible to the broader research community, they are increasingly being utilized by investigators with limited knowledge of statistics. These researchers tend to be more interested in biological insights, whereas the emphasis in terms of analysis tools has been on generating statistically robust gene lists, often at the expense of biological interpretation [6]. Also, as gene expression data is now required to be deposited in publicly accessible repositories, researchers are performing meta analysis of this data. Again, they often lack the knowledge to assess the quality of the data sets of interest to them and what is the appropriate analysis approach. Little assistance is readily available to these ‘unsophisticated’ users, and most of the analysis tools available to them are designed for statisticians and bioinformaticians, which assume a level of a priori knowledge to be able to use them effectively. In this review I will endeavor to provide a general overview of the whole gene expression microarray data generation and analysis process. The focus will be on commercial gene expression microarray platforms, as custom inhouse spotted microarrays are increasingly being supplanted by commercial microarrays. In particular, the emphasis will be on the whole genome microarrays that have been designed to address the transcriptome of a given organism. Other applications for microarrays have been developed for genotyping, microRNA measurement, chromosomal region copy number and other areas, which will not be addressed in this review. Gene expression microarray platforms Microarray designs have utilized spotted cDNAs [1] or oligonucleotide probes [2]. Depending on the platform, oligonucleotides are synthesized in situ on glass slides or synthesized, purified and attached to substrates. In the case of the Illumina platform, the purified oligonucleotides are attached to beads that are randomly dispensed into wells on a slide. In addition there is the choice of one-color or two-color experimental designs. For one-color systems a single sample is hybridized to the array. For two-color experiments, two

31 RNA samples, labeled with different dyes, are mixed and hybridized on the same microarray. Two-color systems can provide experimental design flexibility, since the second sample can either be an experimental sample or a standard RNA sample applied to all the arrays [7,8]. Although the original idea behind the development of two-color systems was that the competitive hybridization would reduce errors due to slide variation, it has been found, from well-controlled experiments, that analyzing the two samples independently, rather than generating ratios, can increase experimental accuracy [9]. The commercial microarray platforms are predominantly one-color oligonucleotide microarrays. The Agilent platform is one exception, initially designed as a two-color system [10], that has been modified for the analysis of one-color or two-color experiments. Most companies offer focused arrays for specific research areas and well characterized genes and transcripts. They also offer design and manufacturing of custom arrays. Over time the array manufacturers have increased the number of features that can be placed on one slide. Now several manufacturers offer multiplexed whole genome arrays. Table 1 compares the technologies of the major commercial whole genome microarray platforms. The key difference between platforms lies in the number and length of oligonucleotide probes on the microarrays: either short-oligonucleotide (25–30 bases) or long-oligonucleotide (50–70 bases). Long oligonucleotides have greater sensitivity and are better for analyzing low copy number mRNAs [11–13]; short oligonucleotides have better specificity, being less likely to cross-hybridize with other RNAs [14]. Most platforms have a single probe for each target, each probe occurring once at a fixed location on the array. Two platforms have multiple probes per target: Illumina BeadChips have over 20 randomly located technical replicates for each probe; Affymetrix 3u expression GeneChips have 11 probe pairs per probe set and the recently introduced GeneChip Gene ST arrays have 26 probes per gene. In addition to the differences in technology, the commercial companies differ in the number of species-specific arrays they offer. Affymetrix, through their GeneChip Consortia Program, offers a large number of microarrays to support different eukaryotic genomics projects. Nimblegen has focused on arrays for prokaryotic genomics. All manufacturers have whole genome arrays for human, mouse and rat. Gene content Most commercial microarrays for human, mouse and rat whole genomes share common targets, primarily transcripts from the National Center for Biotechnology Information (NCBI) Reference Sequence collection (RefSeq) [15]. Each manufacturer has tried to expand beyond this basic set of welldefined targets, in an endeavor to address all the protein-coding genes on the

Nimblegen: HG18 4plex Phalanx Biotech: Human OneArray

22,000 47,633

In-situ ink-jet

Spotted

Spotted

Beads

Beads

In-situ photolithography In-situ photolithography Spotted

60

30

50

50

60

60

60

30,968

24,000

48,000

57,347

34,000

42,000

28,869

60

25

54,000

In-situ photolithography In-situ photolithography

25

Affymetrix: U133 plus 2 Affymetrix: Human gene ST 1.0 Agilent: Human 4  44k Applied Biosystems: Human genome survey v2 Applied Microarrays: CodeLink human whole genome Illumina: Human-6 v2 Illumina: HumanRef-8 v2 Nimblegen: HG18

Targets

Oligonucleotide deposition

Oligo length

Manufacturer: array

1

3

8

1  W20

1  W20

1

1

1

26

11 pairs

Probes per target

32,050

72,000

3,85,000

70,00,000

100,00,000

54,841

35,000

44,000

764,885

13,00,000

Total features

1

4

1

8

6

1

1

4

1

1

Arrays per slide

Table 1. Comparison of human whole genome microarray platforms. Data obtained from manufacturers websites.

3u end

3u end

3u end

3u end

3u end

3u end

3u end

3u end

Exons

3u end

Probe location

33 genome. In most cases this predated the completion of the sequencing of the human genome [16,17], when it was assumed that there would be a lot more protein-coding genes than were ultimately found in complex mammalian genomes. UniGene clusters [18] with a limited number of expressed sequence tags (EST), proprietary sequences not available publicly, and predicted genes were all sources of additional target sequences. In most cases, probes were designed to target the 3u end of individual transcripts. The ability to map transcripts to the genome, an important modification to UniGene cluster generation, has led to consolidation into fewer transcriptional loci. The public sequence databases are continually being updated and UniGene clusters are revised on a regular basis, resulting in reassignment of some transcripts. One result of this is that probes, previously thought to map to different genes, have been shown to map to the same gene, in some cases to the same transcript. This redundancy needs to be taken into consideration during biological interpretation. Also, probes previously mapped to a gene can be disassociated from that gene, which can cause confusion when gene lists are reanalyzed. Consequently, care should be taken in the conclusions drawn from microarray experiments that the probe target is an authentic transcript of the gene it is mapped to. The microarray manufacturers do update their probe mapping but it is usually not on a consistent basis and not in sync with the public databases. This can lead to ambiguities between public information and the probe annotation supplied by the array manufacturers. Another source of ambiguity, documented for Affymetrix 3u expression arrays in particular, is probe sequence inaccuracies [19–25], so the probes do not match their stated target sequence. There are also probes that map to more than one gene, therefore are not specific. Several groups have created modified CDF files to eliminate these ambiguous probes from analysis, leading to more reliable results [21,22,25]. Gene expression microarray experiment process The standard workflow for a microarray experiment is shown in Fig. 1. The key to successful gene expression experiments is good experimental design and attention to detail. There are many technical steps between sample preparation and microarray scanning (Fig. 2), each of which can introduce new sources of error and bias, which can have a profound impact on data analysis and interpretation. To minimize the impact of technical error, it is advised that a single technician process all the samples at the same time and run them on microarrays from the same manufacturing batch [5]. If this is not possible, random samples for each condition should be distributed between either technicians or days to avoid bias [26]. The principle of randomization should also be used with multiplex arrays; in most cases more than one slide will be used and samples should be randomly assigned to the slides.

34 Experimental design

Sample preparation RNA extraction Reverse transcription In vitro transcription

Microarray processing Hybridization Washing Scanning

Data preprocessing Non-specific signal correction Normalization Filtering

Differential expression Comparative statistics Multiple test correction Clustering

Biological interpretation Gene annotation Gene ontologies Pathway analysis

Fig. 1. Gene expression microarray experiment workflow. The goal of a typical

microarray experiment is to identify genes that are statistically significantly differentially expressed and identify the underlying biological processes. The actual methods used at any point will be defined by the experimental design and the microarray platform.

Experimental design Gene expression microarray experiments are designed for one of two purposes: evaluation of differential gene expression between groups, referred to as class comparison; or for classification studies, referred to as class discovery and class prediction [27]. These experiments are expensive and time

35 Random priming

3’ in vitro transcription Purified total RNA

External RNA controls Affymetrix; tERC: Agilent; Applied Biosystems

rRNA reduction Reverse transcription cDNA

2nd cycle reverse transcription cDNA

In vitro transcription cRNA

Fragmentation Fragmented cDNA

Fragmentation Fragmented cRNA

Terminal labeling Fragmented biotinylated cDNA

Hybridization

cERC: CodeLink

cERC: Affymetrix; Applied Biosystems

cERC, control oligo: Affymetrix

Fig. 2. RNA sample processing. The traditional method for labeling RNA samples

for hybridizing to microarrays has used 3u in vitro transcription to generate labeled cRNA for hybridizing to the microarray. The microarray manufacturers usually provide kits for this process that contain external RNA controls (ERC). These are added either to the total RNA (tERC), as controls for the reverse transcription and in vitro transcription reactions; or to the cRNA (cERC) prior to or after fragmentation, as controls for the microarray processing steps. Affymetrix whole transcript arrays use a different protocol, using random hexamer priming to generate labeled cDNA that is hybridized to the microarray. This may include an rRNA reduction step, depending on the amount of starting RNA. An additional control oligo, added at the same time as the cERC, is an additional control for the microarray processing.

consuming and a good experimental design is essential, to maximize the return in terms of usable information [26–30]. Experimental design involves a clear scientific hypothesis, an appreciation of the number of factors to be compared and the confidence that can be assigned to the observations: the simplest design is usually the best. Confidence in the results, the power of the analysis, is derived from using the appropriate number of replicates [31–35]. Microarray studies utilize two types of replicates, technical or biological. Biological replicates, samples from individual subjects, are the only choice for good biological inference [26,36]. Technical replicates, a single or pooled sample applied to multiple

36 microarrays, only measure the consistency of the experimental system and provide limited biological information [26]. In some cases, such as two-color systems when controlling for dye-dependant effects, technical replicates are combined with biological replicates; the average intensity of the technical replicates should be used in subsequent statistical tests. Due to the amount of data generated from microarray experiments, classic power calculation methods for estimating the number of replicates required in an experiment are inadequate, so microarray-specific methods have been proposed [31–35]. In general a minimum of five replicates per group is recommended. This number will change based on whether the samples come from inbred or outbred animals, which will increase the biological variance [34]. The reality of microarray experiments is that investigators tend to run a limited number of replicates due to sample or cost constraints; even so, no less than three replicates should be used [6]. Pooling of samples can be used, when the cost of samples is much less than the cost of microarrays or when insufficient RNA is available to run on an array and amplification methods are not desirable [33,37,38]. The same number of samples should be used for each pool and multiple pools should be used for each group. Pooling may introduce biases and does not provide the same statistical power as analyzing individual samples, but it is better than comparing a limited number of samples [5]. Pooling of samples is not appropriate for classification studies, as these rely on inter-individual variation and co-variation [5]. For investigators with limited statistical knowledge considering running microarray experiments, it cannot be overemphasized that time spent in experimental design with a statistician will ensure that valid conclusions can be drawn from the results, especially for more complex experimental designs. Sample preparation The steps involved in sample preparation are shown in Fig. 2. There are several different commercial reagents and kits in the market suitable for RNA isolation. The key factor is to make sure that the input total RNA sample is of high integrity. The standard method for assessing this is to use an Agilent 2100 Bioanalyzer, which can be used to generate an RNA integrity number (RIN) [39], to assess RNA quality. The microarray manufacturers generally provide or recommend reagent kits for labeling samples and kits are also available from other commercial sources. These kits usually use a modification of the Eberwine method to produce complementary RNA (cRNA) labeled with a dye or other functional group [40]. The kits for specific platforms often contain external RNA controls (ERC), that are complementary in sequence to control probes on the microarrays and are often present as a concentration series, so can be used to assess concentration response [41,42]. The ERC are used to monitor both the RNA labeling reactions, by being added to the total RNA sample prior to cDNA synthesis,

37 and the hybridization, washing and scanning process, by being added to the cRNA immediately prior to hybridization. In addition to this standard approach, there are several different methods for amplifying RNA from samples with low RNA concentrations [43]. As with pooling of samples, RNA amplification can introduce biases that should be taken into consideration when designing experiments and analyzing data. Data preprocessing The purpose of data preprocessing is to convert the raw signal for labeled RNA hybridized to a probe to a normalized value, an adjustment to account for variance from technical rather than biological sources [36]. Many papers have been published on preprocessing of microarray data, especially for twocolor systems and Affymetrix arrays. Usually the process involves quantification of the signal from the microarray, background corrections and normalization within and across arrays. In most cases the microarray manufacturers provide software that adequately performs most of these functions. There are also preprocessing packages available in Bioconductor, developed by researchers not satisfied with the methods provided by the array manufacturers. A logarithmic transformation followed by quantile normalization has become the preferred method of preprocessing for onecolor microarrays [44]. Quantile normalization assumes that the arrays have a similar signal distribution, which is typical for most experiments. However, quantile normalization should not be used when comparing tissues with markedly different expression profiles. The purpose of the logarithmic transformation is to stabilize the variance inherent in the microarray data, changing calculations from multiplicative to additive [36]. However, as the intensity values approach zero, this transformation is less effective. To offset this, some algorithms add a constant to the intensity values, so computations of low-intensity signals have improved variance [45]. A large number of methods have been developed for preprocessing Affymetrix data [45,46]. This was because the standard Affymetrix software, Microarray Suite 5 (MAS5) [47] and earlier versions use the difference in signal between paired perfect match (PM) and mismatched (MM) probes, to account for nonspecific binding. In some cases the MM probe has a stronger signal than the PM probe, resulting in negative signal values, and on occasion false-negative expression. MAS5 also does not normalize across arrays, instead scaling within arrays to a defined intensity value. The alternate methods generally ignore the MM probes, using global or model-based background correction, and normalizing across arrays. The most significant source of difference between the preprocessing methods is how they perform background corrections [45]. Of the alternative protocols that have been developed, RMA [48] and GCRMA [49] are those most commonly utilized. Affymetrix has also developed a new algorithm, PLIER [50], that uses an

38 improved PM–MM background correction and quantile normalization. These methods are all available in Bioconductor, mostly in the affy package. There is some concern that they have been optimized using a limited data set from a single human array version, so may have different performance with other arrays [5,51]. It is also likely that the preprocessing method of choice will vary with experimental design. For two-color systems preprocessing involves other factors. It is well known that dyes have individual biases that need to be adjusted for. In particular, the dye cyanine 5 (Cy5), commonly used in two-color microarray experiments, is rapidly degraded by ozone [52], whereas the other commonly used dye, Cy3, is not. Consequently, air quality can become a major factor in two-color analysis. As with the Affymetrix arrays, background correction methods have a marked effect on preprocessing [53]. Local background subtraction can result in negative intensities and should be avoided in favor of model-based methods, where only positive intensity values are returned. There have been arguments for not performing any background correction but this can also result in problems with downstream analysis methods with cDNA microarrays [53]. For Agilent microarrays, the feature extraction software corrects for both dye biases and background. It has been shown that this results in an increase in the variability in low-intensity data [54], that may be due to the background correction used. In general, logarithmic transformation followed by loess smoothing, is the normalization method of choice [55]. These methods are available in the limma Bioconductor package. The large number of technical replicates for probes on Illumina arrays, allows for more robust variance stabilization and normalization, utilizing both quantile and loess normalization [56]. This can be implemented using the lumi Bioconductor package. Differential expression analysis The main goal of microarray experiments is to identify genes that are significantly differentially expressed between two or more experimental conditions, usually by comparing the average intensity values of the replicate samples. Microarray analysis methods attempt to minimize two types of error in measures of differential expression: type 1 or false-positive errors and type 2 or false-negative errors [5]. Controlling type 1 errors is the major goal of many of the statistical methods developed specifically for microarray analysis. Type 2 errors are more likely to be due to properties of the platform being used; a gene may not be detected due to limited sensitivity. Filtering data using quality values and fold change cutoffs Because some genes are not expressed in any sample and not all expressed genes are differentially expressed between samples, it makes sense to remove

39 these uninformative data points from the analysis process using filters. These are usually based on either quality flags or fold change cutoffs, though more sophisticated methods have been described [57]. The simplest measure of differential expression is fold change, the magnitude of the difference in the expression of a gene between the conditions, usually reported as a ratio or log-ratio. Using fold change alone as a measure of significant differential expression is not appropriate, as it does not assess the reproducibility of the measurement or confidence in the observation [5,58]. For low-intensity genes, a relatively small change in signal can have a marked effect on fold change, resulting in type 1 errors. A fold change cutoff value can be set for filtering genes, above which genes are considered differentially expressed; however, the cutoff value is arbitrary, and setting it too high can result in type 2 errors. During the preprocessing of the array data there is usually an assessment of whether a gene is expressed: does the gene have signal significantly above nonspecific signals? This is usually reported as a quality value, which is different for each platform: the Affymetrix MAS5 algorithm flags genes as being present (P), absent (A) or marginal (M) but RMA and GCRMA do not provide a quality metric; the Codelink platform provides similar flags as measures of signal quality; Illumina BeadStudio software provides a detection p value based on the signal from the replicate beads, which is usually reported as the inverse of the p value; Agilent feature extraction software provides several values but the IsWellAboveBG flag is generally used; and Applied Biosystems uses a signal-to-noise ratio for expression and a flag value for signal quality. Quality values can be used to set quality filters for the data. Some software allows for different filtering options: only analyzing genes with perfect quality scores in all samples; allowing genes to be analyzed where the majority of samples have a perfect quality value; or analyzing all genes regardless of quality flags. In cases where the gene is not expressed in one sample group but is in another sample group, either a nominal positive expression value needs to be used for the unexpressed samples, to avoid division by zero, or the gene should be excluded from analysis, which is usually undesirable. Comparative statistics The most common microarray experiment compares expression between just two groups of samples. This means that standard t tests are used to assess the statistical significance of the observed change in expression at an individual gene level [58]. The actual t test to be used is dependent on the experimental design. Usually an unpaired Student t test is used, as the samples are considered to have equal variance, being randomly assigned to each group. In some cases, such as before and after drug treatment, a paired t test

40 provides more power. When the samples are not equally variable a Welch’s t test is appropriate. The standard t tests report a p value for each comparison at an individual probe level. The p value is the confidence that there is a true difference in expression, and p values that fall below a nominal level, usually 0.05, are considered significant. The number of replicates in a typical microarray experiment are not usually sufficient to make standard t tests robust, as they are sensitive to the effects of outlier values [58]. In addition, the large number of individual tests being run in parallel means that there will be a large number of type 1 errors. Consequently, modified t tests have been developed specifically for microarray analysis, utilizing an approach referred to as variance shrinkage [5]. They are nonparametric, using the variance of all the genes on the array to improve the power of the test. In particular Bayesian statistical approaches have been used, as they have been found to improve analysis of microarray experiments with a limited number of replicates [59,60]. The significance analysis of microarrays (SAM) is another popular approach [61]. Though these approaches improve on the classic t tests in terms of controlling the false-positive rate, there is still no ‘best method’ [62]. When samples from more than two conditions are being compared, an analysis of variance (ANOVA) is often used to estimate the relative expression of each gene in each sample [58]. Depending on the experimental design there are different types of ANOVA that can be used. As with the t test, attention needs to be paid to the variance of the samples in a group and between the groups. When only a single factor is present, the one-way ANOVA is used to compare expression at the individual gene level. When two or more factors are being compared, a two-way ANOVA should be used [6]. For more complex designs, for instance, multiple conditions with biological and technical replicates, multi-way ANOVAs should be used [58,63]. Also, modifications of the standard ANOVA have been described that use global gene variance instead of, or combined with, gene-specific variance, to control type 1 errors [58]. Corrections for multiple testing The standard statistical approach for controlling the false-positive rate when using multiple comparisons is to use corrections for multiple testing. These adjust the p values based on the total number of tests being performed. There are two approaches that are taken: a family wise error rate (FWER) control, the simplest being the Bonferroni correction [5,58]; or a false discovery rate (FDR) correction, usually that of Benjamini and Hochberg [64]. Figure 3 shows an example of the effect of different correction methods on the number of significant genes identified. The FWER corrections adjust the p value to reflect the probability that one or more false-positive errors occur in a list. The Bonferroni correction is

41

5% PCER 12,143 probes

5% FDR 8,053 probes

5% FWER 87 probes

Fig. 3. Effect of different multiple testing approaches on significantly differentially expressed gene lists. The numbers are from analysis of the CodeLink data set kidney control and aristolochic acid treated kidney sample from the MAQC rat toxicology study [116] (GEO accession: GSE5350) without any additional preprocessing or filtering. The CodeLink rat whole genome array has 33,790 probes. The per comparison error rate (PCER) was from an unpaired Student t test with a p value cutoff of 0.05. The Benjamini and Hochberg correction was used for FDR. The Bonferroni correction was used for the FWER. Data was generated using GeneSifter.

very stringent, dramatically reducing or even eliminating lists of differentially expressed genes and therefore increasing the chance of type 2 errors. There are step-down modifications of the Bonferroni correction that are less conservative, including those developed by Holm [65] and Westfall and Young [66]. However, the FWER corrections are a poor choice in discovery research, where a limited number of false-positive results are acceptable compared to eliminating true positives. The FDR correction is less stringent and adjusts the p value to control the frequency of type 1 errors in the list of significantly differentially expressed genes [67]. Positive FDR (pFDR) applies a factor to the FDR, equivalent to the proportion of nondifferentially expressed genes to the total number of genes, which reduces the correction [68]. This increases the power of the analysis, while not eliminating all false-positive errors [58]. Rather than controlling FDR below a threshold, it has been suggested that FDR estimating procedures are preferable for microarray analysis [5]. These assign a false-positive probability value to each differentially expressed gene. The control and estimation of FDR is an active area of investigation [69–71].

42 Cluster analysis Another commonly used method for statistically analyzing microarray data from multiple conditions is to use clustering. This can be used on normalized data without any other analysis being performed or on a statistically significant list of differentially expressed genes from an ANOVA. Clustering algorithms recognize patterns in the data [72]. Usually the clustering algorithms are unsupervised, the raw data being analyzed with no assumption of underlying structure. Two basic approaches are taken, either the visualization of overall expression patterns or the partitioning of genes into discrete groups. In either case, genes with similar expression profiles are grouped together. It is often assumed that genes that cluster together are coexpressed; however, unsupervised clustering algorithms will always produce clusters based on the parameters that are set; the quality and relevance of the clusters is not a factor. For classification applications, where the quality and relevance of the groups are very important, additional information about the relationship of the samples is used for supervised clustering algorithms. Hierarchical clustering is the original method used, and is still widely utilized [73]. This is a simple agglomerative clustering method, where genes are sequentially added to the cluster based on the similarity of their expression profile. There are also hierarchical clustering algorithms that take a divisive approach, starting with a single cluster and finishing with the individual genes, but this is more computationally intensive. It is a useful tool for visualizing expression patterns in microarray data, the typical output being a dendrogram of the genes, from which clusters of closely matched genes can be identified. Usually a graphic visualization, referred to as a heatmap, is also generated, where the log ratios of the intensity are usually represented as a color scale, from intense red for the highest positive values, through black for the mean intensity, to intense green for the most negative values. Figure 4A shows an example of the typical output. Many different algorithms have been used for partitioning microarray data. These include K-means algorithms [74], self-organizing maps (SOM) [75] and partitioning around medoids (PAM) [76]. There is no good method of knowing what is the best algorithm to use for a particular dataset [77,78]. These approaches require that the number of clusters to partition the data into be specified at the outset; however, it is difficult to assess what is the appropriate number of clusters for a particular dataset. One approach to assessing the appropriate number of clusters is to use silhouette widths [79]. These are a measure of how closely the genes in a cluster match the mean expression profile for the cluster; the larger the overall mean silhouette width, the better the clustering of the data. This requires repeated clustering using different numbers of initial nodes. Figure 4B shows an example of a PAM output with silhouette values. Other methods have also been developed to

B

Fig. 4. Examples of hierarchical and partition clustering. The data is from a study of male germ cell tumor samples analyzed using Affymetrix human U133A GeneChips (GEO accession: GSE3218) and preprocessed with GCRMA in GeneSifter. (A) Heatmap and dendrogram from hierarchical clustering of genes of the Wnt signaling pathway between the sample groups. (B) Partitioning around medoids (PAM) silhouettes, four clusters from 4,975 significantly differentially expressed genes, identified using a one-way ANOVA and Benjamini and Hochberg FDR correction, with an adjusted p value o0.0001 and at least a four-fold change in expression compared to a normal testis control. (The color version of this figure is hosted on Science Direct.)

NT S CC E1 E2 T YS

A

44 address the question [80–82]. There is a similar issue with validation of the quality of the clusters [77,82]. Intuitively, genes in the same cluster are co-expressed and therefore share a biological function; however, this assumption is often not borne out by the functional analysis of the genes in a cluster [72]. The reasons for this are: the complexity of the biology underlying a given gene list; the limited number of samples relative to the number of genes on a typical microarray; and the strict assignment of genes to clusters. Standard clustering techniques assign a gene to a single cluster and, once assigned, it cannot be reassigned to a different cluster. Fuzzy clustering has been used to address this problem, by assigning probabilities to genes in clusters; ultimately, the gene is assigned to the cluster where it has the highest probability score [83]. Other approaches, for instance principal component analysis (PCA) [84] and independent component analysis (ICA) [85,86], have used linear models, which allow genes to belong to more than one cluster. Supervised classification To use microarray data as a phenotype classification tool, it is necessary to identify a set of discriminative genes that can be used to assign samples to pre-defined categories. The original description of the use of gene expression microarray data for cancer classification was that of Golub [87]. Since then there have been many publications describing different approaches to the problem [5,88], utilizing several standard data sets for cancer classifier testing [88]. There are several inherent problems in the development of classifiers, including the presence of redundant transcripts in gene expression data; and introducing bias by not using completely separate data sets to create and then validate the predictive algorithm. This leads to models that are susceptible to overfitting, performing well with test data but not with new data [5]. It is widely accepted that a smaller set of non-redundant informative genes will provide the most accurate classifiers; however, there is no current method of choice to identify such genes. Biological interpretation Once a list of significantly differentially expressed genes has been obtained, the next consideration is the identification of the biological processes represented in the list. The information associated with a particular gene, the annotation, is available from many online sources [89–91]. The NCBI has many annotation resources [92], including Entrez Gene, which integrates much of the gene information for a large number of organisms. There are similar resources at the European Bioinformatics Institute (EBI) [93]. Two sources of extensive gene and genome level annotation for multiple species are Ensembl [94] and the UCSC Genome Browser [95], that has tracks

45 showing the location of Affymetrix probe sets. For organisms other than human, mouse and rat annotation is generally sparse. Intensively studied organisms, such as yeast and Drosophila, have rich data resources. Many of the organisms that have had their genomes sequenced have very limited annotation, usually in a dedicated database that is difficult to query. Commonly used sources of functional information associated with genes are the Gene Ontology (GO) database [96] and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database [97]. Useful functional information can also be found at PANTHER [98], which has pathway and ontology information. It is relatively easy to collect information for a single gene, if somewhat time consuming, but it is not easy to identify broad biological themes in this way. Microarray gene lists can have thousands of entries and it can be difficult to query databases to obtain all the related information and mine that information for common themes. For GO several tools have been developed that can take a list of genes and return lists of ontologies, with statistical measures of their significance [99–101]. Similar reports can be generated for pathways [99]. Statistical significance is assessed using either z scores or p values and FDR corrections. The z score represents whether the number of genes associated with an ontological term or pathway is significantly overrepresented, a score W2, or underrepresented, a score o 2, compared to the normal distribution. The DAVID system integrates a lot of different data sources and provides rich functional reports for microarray gene lists [102]. Gene expression microarray data analysis software Software is absolutely essential to the analysis of microarray data. However, there are very few software packages that cover all the steps in microarray analysis. This means that data tends to go through a series of individual software applications that mirror the steps in the workflow in Fig. 1. There is also a limited selection of software resources that not only provide analysis tools but also associated data storage and management capabilities. Open-source software There are several papers published each month on some aspect of microarray analysis and these papers generally have a link to access the associated software. Some are set up as websites, some software is available as Microsoft Excel plug-ins [61], but most of the statistical approaches appear as packages in Bioconductor [4]. Bioconductor is a software resource for genomics data analysis for biostatisticians and bioinformatics experts, who appreciate its power and flexibility, and do not mind the difficult interface. As mentioned earlier, there are specific packages in Bioconductor for the

46 different commercial microarray platforms. Other packages can be more general in nature and gather tools for specific analysis approaches, for instance Bayesian methods. For a casual user the learning curve is steep, requiring the learning of the R statistical scripting language [3], on which Bioconductor is based. Some graphical user interfaces have been developed to make Bioconductor more accessible. As previously mentioned, the DAVID Knowledgebase and associated tools are a popular application for biological interpretation [102]. Finding other sources of microarray analysis and interpretation applications is a daunting task [103], due to the sheer number available, though attempts to catalog them have been made [104]. Commercial software The commercial software available for microarray analysis integrates much of the functionality available in separate Bioconductor packages. The applications often support only one or two microarray platforms. Most have preprocessing, differential expression analysis and clustering tools and allow the integration of R or other scripting languages. There is often an emphasis on data visualization. However, biological interpretation tools are usually not well integrated, unless that is the focus of the application. Table 2 summarizes the capabilities of the most popular commercial applications. Though the user interfaces make them easier to use than most open-source software, they are generally designed for sophisticated users and so can be difficult to learn for nonexperts. The exception is GeneSifter, the only webbased application, which was designed with the philosophy that non expert research scientists should be able to perform their own microarray data analysis for common experimental designs. This system has broad microarray platform support, built-in data management, preprocessing, differential expression analysis, clustering tools and integrated biological significance analysis. A common criticism of commercial software is that it is a ‘black box’; the actual code being run for a statistical analysis being inaccessible to the user. GeneSifter only uses algorithms from R and Bioconductor. Cross-platform comparisons In spite of attempts to standardize microarray data reporting, led by the Microarray Gene Expression Database Society (MGED) [105] with the Minimum Information About a Microarray Experiment (MIAME) standard [106], most attempts to compare results from different microarray platforms tended to show poor concordance [12,23,107–111]. The sources of the inconsistencies were identified as: the difficulty of comparing platforms at a gene level [12]; employing different protocols for sample preparation [108,110]; and different statistical tests in data analysis [111]. When

Integromics: ArrayHub

TIBCO: Spotfire Decision Site VizX Labs: GeneSifter Biodiscovery: GeneDirector Genologics: Geneus X

X X

X X X X

All

All

Affymetrix, Illumina Affymetrix, ABI, twocolor

X

X

X

Affymetrix, Illumina All

X

X

All

X

X

X

X

X

X

X X

X

X

X

X

X

Differential expression

X

All

X

X

X

Affymetrix, Nimblegen Affymetrix

Affymetrix, two-color Genepix, other Affymetrix, cDNA

X

Affymetrix

Genomatix: ChipInspector Insightful: S+ArrayAnalyzer Molecular Devices: Acuity Ocimum Biosolutions: Genowiz Partek: Genomics Suite Rosetta Biosoftware: Resolver SAS: JMP Genomics

X

All

Preprocessing

Agilent: Genespring GX Biotique Systems: X-ray DNAStar: ArrayStar

Data storage

Microarray platforms

Company: product

X

X

X

X

X

X

X

X

X

X

X

X

Gene annotation

X

X

X

X

X

Functional analysis

Table 2. Comparison of commercial gene expression microarray data analysis software capabilities.

Win

Win, Mac, Linux Client/server

Web browser

Win

Win

Client/server

Win, Linux

Win, Mac, Linux

Win, Mac, Linux Win, Linux, Solaris Client/server

Win

Win

Win, Mac

Computer platform

R

S-Plus

SAS

Proprietary

Proprietary

Proprietary

Proprietary

S-Plus

Proprietary

Proprietary

Excel

Proprietary

Statistics

48 comparing cDNA and short-oligonucleotide platforms, similar trends in direction of differential expression, but not magnitude, were observed [109]. It was also found that results were more variable between laboratories than between platforms when common samples were run at several sites [108,110]. When common procedures were implemented, and good consistency between replicate samples was achieved, the consistency between laboratories and platforms improved. Microarrays were identified as being key technologies in future submissions to the U.S. Food and Drug Administration (FDA) and the lack of uniformity between studies and platforms was of major concern. This led to two initiatives, the MicroArray Quality Control Consortium (MAQC) [111] from the FDA and External RNA Control Consortium (ERCC) [112] from the National Institute of Standards and Technology (NIST). Both represented government, commercial and academic interests. The MAQC Consortium published a group of papers in September 2006 addressing many of the issues that had been raised, to try and identify the sources of discordance observed in other studies [9,41,113–116]. All the data generated is publicly available and acts as a superb resource for comparing analysis methods. The main study [115] utilized a pooled titration series using different ratios of two reference RNA samples: a Universal Human Reference RNA and a Human Brain Reference RNA. These were assayed on six different commercial microarray platforms and the NCI provided an in-house spotted oligonucleotide microarrays. Each platform was used at multiple test sites. Each test site ran five replicate assays for each RNA pool. The microarray providers used their own software for intensity signal quantification and a quality measure for each probe on the array. However, this made the resulting analysis more complicated because of the differences in data preprocessing and quality filtering. To make sure that, as far as possible, each platform was actually measuring the same target, a common set of 12,091 probes, mapping to a non-redundant list of genes and transcripts, was identified. The generation of this list was aided by the actual probe sequences being made available by the companies involved, allowing more rigorous comparisons than had previously been possible. In general, the inter-platform detection of these genes varied more than intra-platform variation between test sites; however, the different platforms appeared to detect similar changes in gene abundance. The optimum method for generating overlapping gene lists between platforms was to use a ranked fold change analysis, which ignored the absolute degree of observed change. Using standard statistical tests reduced the concordance between platforms. Perhaps not surprisingly, the simple statistical methods chosen have been challenged [117]. The two reference RNAs were also used to compare the concordance of results from one-color and two-color microarray studies [9]. This comparison used three different platforms, hybridizing both one-color and two-color

49 samples to each. This eliminated problems due to different platforms being used for each experimental design. Within each platform there were high correlation coefficients and good concordance between the differentially expressed gene lists for the two approaches. Two-color designs appeared to have slightly better sensitivity but one-color designs had lower compression of the expression values. Using individual intensity values rather than ratios for the two-color design appeared to have the greatest sensitivity. As well as the artificial RNA sample comparison of the main MAQC study, four platforms were compared using a biologically relevant toxicogenomics data set [116]. This data was derived from four groups of six rats treated with a range of plant-derived toxins [31]. Liver samples were collected for all groups, as well as kidney samples from control animals and those treated with aristolochic acid, a nephrotoxic compound. These six groups of six replicate samples were assayed on five sets of rat whole genome microarrays from four commercial sources. The results validated the approach used in the main study that rank fold change was the best method of maximizing gene list overlap between platforms. The fold change ranking also resulted in much better concordance when comparing platforms, based on the biological functions significantly represented in gene lists. The MAQC study also included validation of the microarray data using quantitative measurement of gene expression [113]. Generally, good concordance was seen between the quantitative assays and the microarray results for genes that were detectable on both platforms. Where discordance was observed it could be explained by difference in the location of probes, meaning that alternate splice variants could be detected. Genes found to have low expression by the quantitative assays were those that showed the most variable concordance between both the quantitative assays and microarray platforms and between the different microarray platforms in the main MAQC study. This reflected the sensitivity range of the different microarray platforms. Overall, the MAQC study showed that data was consistent at individual test sites, reproducible between test sites and comparable between platforms. However, to ensure the reliability of microarray-based studies, there is still a need for unified metrics and standards, to identify poor quality arrays and monitor performance at microarray facilities.

Public gene expression data repositories A condition of many journals for publishing papers in which gene expression microarray data is described is that the data has to be publicly accessible. This is also a condition of federal grant funding agencies. Two major data repositories are the Gene Expression Omnibus (GEO) [118] at the NCBI, and ArrayExpress [119] at the EBI. The data submitted to the repositories has to

50 be MIAME-compliant, so all the experimental details are available. Many submitters also include the original raw data files. The two repositories currently contain nearly 300,000 individual samples in approximately 10,000 studies, most of which are from gene expression microarray experiments. This huge volume of data is available for meta analysis, either to extend or confirm researchers own results, or for data mining, for instance to identify previously missed gene and disease relationships [120,121]. However, when performing meta analysis it is important to remember that analysis across microarray platforms is not straightforward.

The future The original design philosophy for gene expression microarrays was to measure the expression of all protein-coding genes. However, with the refinement of whole genome annotation, it has become clear that the estimates of the number of these genes have been over optimistic. The latest estimate for humans is approximately 20,500 [122]. This will lead to further consolidation of gene expression microarray platform content, ultimately leading to the same set of genes and transcripts being represented on each platform. As the content goes down and feature densities increase, multiplexing of arrays will increase. At the same time the number of potential non-coding RNA transcripts has dramatically increased after the publication of the results of the ENCODE pilot project [123]. Tiling microarrays were extensively used in this and the follow-up project. It is likely that new microarrays will appear to measure the diverse RNA species that are being identified and also to study transcriptional control elements. This is already occurring with microarrays for micro RNAs and recent announcements of the release of microarrays for several platforms containing CpG elements. It is likely that microarray platforms will face increasing competition from high-density RT-PCR based platforms. Another technology that will have a profound effect on microarrays is digital gene expression (DGE) [124], using the new massively parallel sequencing systems. DGE is basically an extension of serial analysis of gene expression (SAGE) [125], a tag counting method of gene expression analysis, first described in the same issue of Science as the original cDNA microarray paper. The problem with SAGE was that it was expensive to sequence enough tags to get true quantitation of gene expression. The new sequencing technologies mean that deep sequencing is much cheaper and much faster and millions, rather than thousands, of tags are sequenced. This technology has been touted as the end of microarrays, and has resulted in Applied Biosystems Inc. abandoning their microarray platform in favor of their new sequencing platform. It is more likely that

51 the two technologies will be complementary; DGE results leading to new microarray designs. As well as new technologies for measuring gene expression, the use of microfluidic devices to study gene expression at a single cell level, and laser microdissection to analyze select sets of cells from tissues, will lead to a greater appreciation of localized gene expression and potentially better classification. Gene expression microarray data analysis has generally reached a state of consensus. However, developments in microarray technology, new competing technologies and sample preparation will require new analysis methods or refining of existing methods. Improvement in classification methods and more biologically meaningful clustering approaches are required. Increased interest in meta analysis of co-variance of genes across studies and across platforms should see improvements in data analysis methods for this purpose. Also, gene expression microarray data is increasingly being used with other types of data to obtain a larger biological picture, referred to as systems biology. This is a rapidly expanding field and integration of these disparate data types will be a challenge. Acknowledgements The author would like to thank Leah Klein for helpful discussions and critical review of the manuscript. References 1. Schena M, Shalon D, Davis RW and Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467–470. 2. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H and Brown EL. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 1996;14:1675–1680. 3. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, 2007. 4. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH and Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004;5:R80. 5. Allison DB, Cui X, Page GP and Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006;7:55–65. 6. Olson NE. The microarray data analysis process: from raw data to biological significance. NeuroRx 2006;3:373–383. 7. Kerr KF, Serikawa KA, Wei C, Peters MA and Bumgarner RE. What is the best reference RNA? And other questions regarding the design and analysis of two-color microarray experiments. OMICS 2007;11:152–165.

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