Silvia Saviozzi, Giovanni Iazzetti, Enrico Caserta,Alessandro Guffanti, and Raffaele A. Calogero
AbstractDNA microarray is an innovative technology for obtaining information on gene
function. Because it is a high-throughput method, computational tools are essential indata analysis and mining to extract the knowledge from experimental results. Filteringprocedures and statistical approaches are frequently combined to identify differen-tially expressed genes. However, obtaining a list of differentially expressed genes isonly the starting point because an important step is the integration of differentialexpression profiles in a biological context, which is a hot topic in data mining. In thischapter an integrated approach of filtering and statistical validation to select trustabledifferentially expressed genes is described together with a brief introduction on datamining focusing on the classification of co-regulated genes on the basis of their bio-logical function.
Key Words: GeneChip; SAM; dCHIP; MAS 5.0; geneontology.
1. IntroductionDNA microarray technology is an high-throughput method for obtaining informa-
tion on gene function. Microarray technology is based on the availability of genesequences arrayed on a solid surface (i.e., nylon filters, glass slides), and it allowsparallel expression analysis of thousands of genes. Microarray can be a valuable toolto define transcriptional signatures bound to a pathological condition (see Note 1) orto rule out molecular mechanisms tightly bound to transcription (see Note 2). How-ever, because our actual knowledge of gene function in high eukaryotes (e.g., human,mouse) is quite limited (see Note 3), microarray analysis frequently does not imply afinal answer to a biological problem but allows the discovery of new research pathsfor exploration from a different perspective.
Computational tools are essential in microarray data analysis and mining tograsp knowledge from experimental results. In this chapter we present an integrated
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filtering/statistical approach to identify differentially expressed genes (Fig. 1, flowchart of the procedure). Filtering and statistical validation are based on the use ofrobust computational tools that are relatively simple to use by scientists with limitedor no computational experience. The approach is focused on obtaining a robust set ofdata, keeping the number of false positives low, although this might imply the loss ofsome correct data. Furthermore, we briefly explore the data mining problem, focusingon the classification of coregulated genes on the basis of their biological function.
1.1. Microarray Technological Platforms1.1.1. DNA Spotted Arrays
Microarray technology was initially developed by Schena and coworkers (1); it isbased on spotting, in an ordered manner, on a solid surface of thousands of expressedsequence tag (EST) sequences/genes (see Note 4). mRNA relative expression levels(differential expression) are measured by cohybridization of cDNAs, derived bymRNA preparations from two different samples (e.g., normal and cancer-derived celllines), labeled with two fluorescent dyes (e.g., Cy3 → green color emission, Cy5 →red color emission). Recently gene-specific oligonucleotides ranging between 40 and
Fig. 1. Flow chart of the microarray data analysis involving the use of MAS 5.0 and dCHIPfor data filtering, as well as SAM and CyberT softwares for statistical validation of differentialexpressions. Analysis steps are described in text. Bgd, background; SE, standard error.
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70 bases have seemed to be an appropriate replacement for cDNA (see Note 5) (2,3),as they are suitable for high (>10,000 genes) and low (<1000) density spotting(see Note 6). The cohybridization technology has, however, a number of problemsthat can strongly affect the gene expression analysis. These problems can be solved, atleast to some extent, by using special reagents or computational techniques:
• The direct incorporation of the two dyes is not identical using conventional enzymes (4);however, using a postlabeling technique (see Note 7), this problem can be overcome.
• The fluorescence emission efficiency of the two dyes is not identical; therefore, dye swap-ping as well as mathematical adjustments are needed before differential expressionevaluation (5).
• Background noise associated with each spot is linearly dependent on the dye signal (5);therefore, background noise correction should be done using mathematical approachesconsidering its dependence on signal intensity.
• Array printing can be different within the various subsections of the array due to printinghead consumption. This problem can be overcome by using noncontact printing technol-ogy (see Note 8) or, to some extent, by performing local mathematical normalization (5).
• A mathematical model describing the hybridization of probes onto cDNA arrays is notavailable; therefore, it is not possible to define the hybridization specificity of each probeand thus assess the false-hybridization rate.
1.1.2. Oligonucleotide ChipsMicrorrays have also been developed using photolithographic oligonucleotide syn-
thesis (Affymetrix, Santa Clara, CA). This approach allows in situ synthesis of up to300,000 (approx 25 mers) oligos/cm2. cDNA spotted arrays are characterized by theuse of one long stretch of bases (>300) for each gene; in Affymetrix GeneChip, up to20 short oligonucleotides (probe set) are used to probe each gene/EST (Fig. 2), andprobe sets describing the same gene are distributed in various locations on the chip.To assess the target hybridization specificity of each oligo (PM: perfect match) of theprobe set, a “negative control” oligonucleotide (MM: miss match) is associated witheach PM. This oligonucleotide has a sequence equal to PM but with a single centralmismatch, which strongly destabilizes the hybridization of the target; the PM/MMcouple is called the probe pair. Consequently, evaluation of the hybridization signalson PM and MM probes gives an indication of the aptitude of any PM to identify aspecific target, as strong signal in the MM probe is a warning for the presence ofcrosshybridizing targets.
Target hybridization to Affymetrix GeneChips allows the generation of absoluteintensity values describing the mRNA expression level (see Note 9); therefore, to gen-erate a “virtual two-dye” experiment, two GeneChips have to be used (e.g., normalsample on chip A, pathological sample on chip B). Although Affymetrix arrays are farfrom the ultimate solution for the characterization of gene expression, they offer someadvantages with respect to DNA spotted arrays:
• The hybridization specificity of each PM can be assessed by the level of fluorescenceassociated with the corresponding MM.
• Probe pairs are distributed all over a gene sequence; therefore hybridization intensity isbound to retrotranscription reaction efficiency and can give are indication of the quality
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of the retrotranscribed RNA (degradation, presence of reverse transcriptase inhibitors,and so on).
• Recently a mathematical model assigning a degree of hybridization accuracy to each probeset (6) has been developed.
Microarray transcriptional profiling has a large variety of applications, but in thischapter we focus on the use of microarrays to explore molecular transcriptional mecha-nisms. The issue of data normalization and statistical analysis is addressed only forAffymetrix GeneChips.
of reverse transcriptase, yield of RNA purification steps, photomultiplier gain, amountof exposure, and so on) can strongly affect microarray hybridization intensities. It isassumed that sources of errors are multiplicative and strongly affect true expressionlevels (7), especially if the genes are moderately expressed (8). Therefore, normaliza-tion of gene expression data is a crucial preprocessing procedure that is essential fornearly all gene expression studies in which data from one array must be comparedwith data on another array. A number of normalization approaches may be taken intoaccount (9,10); however, so far a gold standard method has not been defined formicroarray data normalization (11). Thus, the chosen method should be motivated bythe application at hand and the goals of the data analysis. In this section we describe a
Fig. 2. Each gene/EST is represented by various probe sets scattered in the GeneChip.(A) Each probe set is made by up to 20 couple of oligonucleotides (probe pair) scattered overthe target gene. (B) Each probe set is made by two oligonucleotides of the same length perfectmatch (PM) and miss match (MM). PM perfect match probe has a sequence perfectly matchingthe target sequence; MM has the same sequence as PM but with a central mismatch whichradically alter its hybridization kinetic to the target gene sequence.
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microarray analysis approach in which various methodologies are used in such a wayas to emphasize their strengths. It is important to mention that microarray analysisfield is evolving very fast; consequently, any microarray analysis approach can beimproved on the basis of the availability of new computational tools.
In our laboratory, MAS 5.0 (www.affymetrix.com) and dCHIP (6) softwaresare used for GeneChips signal intensity normalization. SAM (12) and CyberT (13)(see Note 10) tools are utilized for statistical validation of differential expressiondata. All four tools have user-friendly interfaces, and they are a good starting point forinexperienced people to analyze microarray data. In this chapter, we will not useBioconductor (www.bioconductor.org), a microarray analysis suite based on R pack-age (cran.r-project.org), which is an integrated suite of software facilities for datamanipulation, calculation, and graphical display. Bioconductor is a powerful packageoffering a great deal of flexibility, more than the previously described tools possess,but it does not have a user-friendly interface and it can be tricky to use.
2.1. Affymetrix Microarray Suite 5.0 (MAS 5.0)Background Subtraction, Absolute Call, Array Scaling, and Data Filtering
MAS 5.0, produced by Affimetrix, allows reading and manipulation of the rawimage file (.DAT) acquired by the GeneChip microarray scanner. The raw data file isthen converted by MAS into an image file (.CEL), containing probe set’s intensitiesand locations (see Note 11). MAS 5.0 performs a background correction across theentire array, and subsequently an expression call (i.e., call P: gene is expressed; call A:gene is not expressed; call M: gene is marginally expressed) is assigned to each probeset. A gene is call absent (A) if all probe pairs of a probe set are excluded from theanalysis and a probe pair can be excluded if its MM cell is saturated. A gene is also callabsent (A) if there is an insignificant different between PM and MM in all probe pairs.To determine whether the difference between PM and MM is significant, a discrimi-nation value is calculated and its median is compared with a user-defined cutoff value(τ) (see Note 12). If all the probe pairs are not excluded (i.e., a probe pair is excludedif PM-MM is nonsignificant), a one-sided Wilcoxon’s test is used to calculate ap value that reflects the significance of the differences between PM and MM, and it isused to assign a call (i.e., P, A, M) to the probe set. The borderline between a call P anda call M is defined by a user-definable threshold (α1), as the border line between Mand A call is defined by an α2 threshold, which is also user-definable (see Note 13).
The probe set intensity signal is calculated as the one step bi-weight estimate of thecombined differences of all the probe pairs in the probe set. MAS also offers the pos-sibility of performing data scaling, which is a mathematical technique that can mini-mize discrepancies due to variables such as sample preparation, hybridizationconditions, staining, or probe array lot (Fig. 1, flow chart step a). The effects of scal-ing can be visualized by looking at log intensity ratio versus average log intensity.In Fig. 3A, two biological replicates are compared before and after scaling; scalingdoes not affect the overall similarity between the samples, as shown by the r2 correla-tion coefficient, which is 0.8995 for both raw and scaled samples. The tendency line ofthe scaled sample (black line) is much nearer to 0 with respect to raw data (dashed
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Fig. 3. (A) Plotting log ratio versus average log intensity for two replicates without datascaling (*, r2 = 0.8995) and after data scaling (circles, r2 = 0.8995) using MAS 5.0 software.(B) Plotting log ratio versus average log intensity for two replicates without data scaling(*, r2 = 0.8995) and after data scaling (circles, r2 = 0.9716) using dCHIP software.
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line), indicating that the discrepancies between the two datasets have somehow beenreduced. Scaled data can undergo filtering procedures to reduce the size of the datasetunder analysis, purging those data that can induce ambiguous results (e.g., differentialexpression generated by genes with signal in the background range; such data areparticularly dangerous, as low-intensity signals have very narrow standard deviationsand therefore are likely to pass statistical validation based on a t-test although they arenot informative). A first filtering step is performed by using as a threshold the numberof call As detected for each probe in all the arrays under analysis (Fig. 1, flow chartstep b), and taking out all probe sets called A in more than 90% of the analyzed arrays(not expressed gene set). The not expressed gene set is afterward used to define thethreshold for an additional filtering step, based on average background intensity signal(Fig. 1, flow chart step c). The background intensity threshold (Bgd) is estimated byplotting the frequency distribution of intensity signals of the not expressed gene setand defining the value delimiting the 10% upper tail of the intensity distribution (Fig. 4A).The Bgd value is then used to filter out genes with average expression level lower thanBdg (in all experimental groups left from the first filtering step) (Fig. 1, flow chart step c).
2.2. dCHIP Probe Set Quality Assessment,Data Normalization, and Filtering
dCHIP, a software developed by Li and Wong (6), allows analysis of GeneChipdata. dCHIP calculates gene calls and performs normalization in a different way thanMAS 5.0. Furthermore, if the number of arrays is at least 10, dCHIP allows the calcu-lation of a model-based expression index in the array as well as a probe-sensitivityexpression index. Fitting experimental probe set values with the calculated model, it ispossible to define a standard error value (SE) that gives an indication of the hybridiza-tion quality for each probe set. SE is quite useful for filtering out probe sets that differ-entiate too much from the mathematical model. The Invariant Set Normalizationmethod is used in dCHIP (14) to normalize arrays (Fig. 1, flow chart step α). In thisnormalization procedure, an array with median overall intensity is chosen as thebaseline array against which other arrays are normalized at probe intensity level. Sub-sequently, a subset of PM probes, with small within-subset rank difference in the twoarrays, serves as the basis for fitting a normalization curve. This normalization methodproduces a better fitting of the replicates, with respect to the MAS scaling procedure,as shown by the r2 correlation value (0.9716) (Fig. 3B) and by the disappearing ofprobe sets showing strong fluctuations between replicates. Normalized dCHIP datacan undergo filtering procedures to reduce the size of the dataset under analysis, purg-ing those data that can induce ambiguous results. Also, in this case a first filtering stepis done using as threshold the number of call As detected for each probe in all thearrays under analysis (Fig. 1, flow chart step β), taking out all probe sets called A inmore than 90% of the analyzed arrays. The second filtering step is instead based on SEthreshold (Fig. 1, flow chart step γ), in order to filter out probe sets that loosely fit themathematical probe set profile model. Taking for granted that the vast majority ofthe probe sets are characterized by a good match between the experimental data andthe calculated model probe set profiles (narrow SE), genes within the upper 3% tailof the SE distribution (Fig. 4B) are filtered out.
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Fig. 4. (A) Distribution of intensity values in the not expressed genes set (genes called A in>90% of the arrays under analysis). Dashed line indicates the threshold value delimiting the10% upper tail of the distribution. (B) The standard error (SE) provides a measure of probe sethybridization quality (i.e., high values indicate a low correspondence between the calculated—mathematical model—probe set hybridization profile and the experimental hybridization pro-file). The distribution of the SE associated to all probe sets was evaluated, and, assuming thatthe vast majority of the probe sets have a good hybridization quality profile, the SE valuedelimiting the 3% upper tail of the distribution (dashed line) is used as threshold to filter outthose probe sets that could give misleading results owing to their low hybridization quality.
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2.3. Combining MAS 5.0 and dCHIP DataDatasets generated by applying the filtering procedures suggested for MAS 5.0 and
for dCHIP only overlap partially, indicating that filtering can strongly affect the com-position of the dataset under analysis. Because, as previously pointed out, a gold stan-dard normalization and filtering procedure has not been defined, in our lab we preferto perform statistical validation of differential expression only on probe sets selectedby using more than one approach (Fig. 1, flow chart step s1, MAS 5.0 scaling, expres-sion calls + Bgd filtering ∩ dCHIP normalization, expression calls + SE filtering).This is the final dataset generated by combing two different analysis approaches, andthus we have two sets of intensity values, those derived from MAS 5.0 and those fromdCHIP. Inspecting the intensity values generated by MAS and by dCHIP for the samearray, it seems that the intensity range of MAS data is wider than that of dCHIP (Fig. 5A).This observation indicates that the two approaches produce different intensity levels,but it does not give any indication about which of the two sets is preferred to generatedifferential expression values. Calculating differential expressions using MAS ordCHIP data and plotting the distribution of differential expression values (Fig. 5B), itseems that although the dCHIP normalization and probe set intensity calculationapproach improves the quality of the replicates (Fig. 3B), it also strongly minimizesthe differences existing between controls and treated samples (Fig. 5B). As shownin Fig. 5, MAS 5.0 data produces a broad range of differential expressions, which areinstead meanly distributed in the range ±0.5-fold change for dCHIP data. The use ofdata within the ±0.5-fold change range can produce ambiguous results because it relieson very narrow expression changes, which can be caused by experimental fluctuationsunrelated to biologically meaningful effects. Therefore, for fold change estimation,we prefer to use the intensity values derived from MAS 5.0 scaling.
3. Microarrays Differential Expression Statistical ValidationOnce the final dataset is generated, robustness of differential expression can be
evaluated by combining fold change with statistical validation. Because microarrayresults are influenced by various experimental errors (5), it is important to performreplicates of the experiments to assess the variability of the gene expression levels inthe treatment and control groups and to evaluate the statistical meaning of those varia-tions (see Note 14). Statistical validation is quite important because the simple-mindedfold approach, in which a gene is declared to have significantly changed if its averageexpression level varies by more than a constant factor, usually 2, between the treat-ment and the control conditions, is unlikely to yield optimal results because the foldchange factor can have different significance depending on expression level (13). Usu-ally, for a limited number of replicates, parametric (e.g., t-test) or nonparametric tests(e.g., Wilcoxon’s rank test) can be carried out. However, when multiple hypothesesare tested, as in the case of thousands of genes present on a microarray, the probabilitythat at least one type I error (i.e., a gene is considered differentially expressed althoughit is not true) is committed can increase sharply with the number of hypotheses.For these reasons, a variety of approaches have been developed to avoid this kind oferror (5,12,13).
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Fig. 5. (A) Intensity values for the same data set calculated with MAS 5.0 and with dCHIP.(B) Distribution of fold change generated using MAS scaled data (black line) or dCHIP nor-malization (gray line). Although it performs better than MAS scaling in normalizing replicates(see r2 correlation in Fig. 3), in our hands dCHIP probe set intensity calculation has a strongeffect on the differences existing between treatment and control samples and minimizes thefold change variations. The distribution of fold changes obtained using MAS scaled data isspread over a broad fold change range with respect to dCHIP data. dCHIP data are mainlydistributed between –0.5 and 0.5 log2 fold change, which is frequently considered a sort of grayzone in which it is very hard to discriminate between real differential expressions and falsepositives.
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SAM (Significance Analysis of Microarrays), developed by Tusher and coworkers(12), is a statistical technique for finding significantly differentially expressed genesin a set of microarray experiments. The input to SAM is gene expression measure-ments from a set of microarray experiments, as well as a response variable from eachexperiment. In SAM the “unpaired response variable” that refers to grouping like“untreated” (i.e., 1) or “treated” (i.e., 2) perfectly fits the analysis of differential geneexpression measured using GeneChip. SAM measures the strength of the relationshipbetween gene expression and the response variable, and it uses repeated permutationsof the data to determine whether the expression of any gene is significantly related tothe response. The user can decide on the acceptable false discovery rate, setting asignificance cutoff, and he or she can also set a specific fold change threshold to ensurethat called genes change at least a prespecified amount. To calculate the false discov-ery rate, data are randomly permuted, and the user should indicate the number of per-mutations to be used; because the number of permutations is affected by the number ofreplicates up to triplicates, we suggest that the user perform the full set of permuta-tions (>700). Concerning the definition of the threshold parameters, we usuallyperform SAM analysis selecting a significant cutoff that gives less than one false posi-tive, and we combine this threshold with a fold change of at least |1.5| (Fig. 1, flowchart step s2).
CyberT, developed by Baldi and Long (13), allows calculation of how meaning-ful a differential expression is using a Bayesian probabilistic framework. In particu-lar, CyberT uses a Bayesian approach to calculate a background variance for each ofthe genes under analysis, using such values to balance experimental fluctuationswithin a limited number of replicates. In CyberT, a Bayesian version of the t-test canbe performed as the user has defined the number of neighboring genes needed toestimate the background variance for any of the genes in the dataset and the degreeof confidence in the background variance versus the empirical variance. As shownby the authors (13), the Bayesian approach appears robust relative to the use of foldchange alone, as large nonstatistically significant fold changes are often associatedwith large measurement errors. Furthermore, the Bayesian approach is about twiceas consistent as a simple t-test for identifying differentially expressed genes overtwofold expression change in independent samples of size 2 (i.e., two experimentsvs two controls).
SAM and CyberT are different ways to identify differentially expressed genes;because we want to obtain a robust set of differentially expressed data, we validateSAM data by CyberT (Fig. 1, flow chart step s3, SAM analysis ∩ CyberT analysis).Genes found to be differentially expressed by SAM analysis are mapped on a plot inwhich differential expression values are plotted with respect to CyberT p values(see Note 15), calculated for the same dataset used in SAM analysis. We keep inconsideration for further analysis only those genes that passed the SAM test and showa p value < 0.005 (Fig. 6, dashed line) in CyberT analysis. As can be observed inFig. 6, all genes passing the SAM test and showing a log2 fold change = |0.5| also havep values <0.005.
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4. Microarray Data Mining4.1. Transcription Profiles Clustering
Array technology has made it straightforward to monitor simultaneously theexpression patterns of thousands of genes. The challenge now is to make sense of sucha massive dataset. In a simple experimental design in which a control sample is com-pared with a treated sample, the user gets a set of differentially expressed genes thatcan be ranked by their relative induction. In a more complex experimental design,involving, for example, time-course or parallel analysis of different transcription acti-vators (e.g., different isoforms of the same transcription factor), a key goal is to extractthe fundamental patterns of gene expression inherent in the data. These techniques areessentially different ways to cluster points in multidimensional space. Various cluster-ing approaches have been applied to transcriptional expression profiles generated bymicroarray analysis (15–18); however, in gene expression classification it is not pos-sible to identify a universal clustering approach because the optimal clustering algo-
Fig. 6. p values generated by CyberT (which indicate the probability that an observed foldchange is a casual event) for all differential expressions are plotted with respect to fold change(*). Genes shown as differentially expressed in SAM analysis are shown as circles. Differentialexpressions are considered statistically validated if they pass SAM analysis and show in CyberTat p < 0.005 (dashed line).
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rithm to be used depends on the nature of the dataset and what constitutes meaningfulclusters in the problem under analysis. Therefore, in our lab we test various tools togenerate transcription profile clustering. We usually prefer clustering approaches inwhich the number of clusters is not an arbitrary parameter defined by the user but isdefined by the algorithm on the basis of the dataset under analysis (www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html; www.stat.washington.edu/fraley/mclust).
An example of a transcription profile obtained using adaptive quality-based clus-tering (17) can be seen in Fig. 7. The three clusters refer to transcription profilesderived by microarray analysis of six p63 isoform-driven gene expressions (Saviozziet al., unpublished results), and they are the clustering solution obtained using theweb-based clustering tool developed by De Smet and coworkers (www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html) (17). This tool is based on a two-stepalgorithm. As the first step, transcription profiles are grouped in spheres in which thedensity of expression profiles is locally maximal (based on a user-defined estimate ofthe radius of the cluster). In the second step, the radius of each cluster is optimized sothat only the significant coexpressed genes are included in the cluster. It is interesting tonote that using a k-way partition clustering approach (19) (http://www-users.cs.umn.edu/~karypis/cluto/) in which we forced the program to generate three clusters,we got 95% overlaps between the k-way clustering and the adaptive quality-basedclustering (data not shown). This result indicates that the partition in three clusters isprobably a good solution for the dataset under analysis, as homogeneous results can beobtained using different clustering approaches.
Even after transcriptional profiles are clustered, much work must be done to extractsome functional information from microarray data. Clustering, when possible, classi-fies genes by their transcription profile similarity, which gives only partial informa-tion about the functional correlation existing between differentially expressedgenes, and belonging to the same cluster; it does not imply a functional correlation.An important topic in microarray data mining is therefore to bind transcriptionallymodulated genes to functional pathways or classify them in functional classes tounderstand how transcriptional modulation can be associated with specific biologicalevents (genetic disease phenotypes, molecular mechanism of drugs action, cell differ-entiation, development, and so on) or at least to find out whether some specific bio-logical process is strongly affected by transcriptional modulation in the experimentunder analysis. In other words, researchers need to rely on robust gene functionalannotations and on tools to link functional annotations to transcriptional profiling.
Genomic sequencing has clarified that a large fraction of the genes specifying thecore biological functions are shared by all eukaryotes; therefore, knowledge of thebiological role of such shared proteins in one organism can often be transferred toother organisms. The Gene Ontology (GO) Consortium (www.geneontology.org) hasas a main task to define a structured, precisely defined, common controlled vocabu-lary for describing the roles of genes and gene products in any organism. Therefore
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Fig. 7. Clustering results obtained using the tool developed by De Smet et al. (17).The adaptive quality-based clustering was generated using the microarray data derived by tran-sient transfection of six p63 isoforms in a p53 null cell line (Saviozzi, unpublished observa-tions). The clustering procedure produces three clusters, based on a required probability ofgenes belonging to cluster equal to 0.85. Similar clustering results were obtained using a k-waypartition clustering approach (data not shown).
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GO can be a valuable tool to link differentially expressed genes to specific functionalclasses.
GO is divided into three categories:
• Molecular function, which contains information about the tasks performed by individualgene products (e.g., transcription factor, DNA helicase).
• Biological process, which describes broad biological goals, such as mitosis or DNA repair,that are accomplished by ordered assemblies of molecular functions.
• Cellular component, which indicates subcellular structures, locations, and macromolecu-lar complexes (e.g., nucleus, ribosome).
The important point of the GO approach is that each node (ontology term) of theontology can have more then one parent, and a gene can be associated with more thanone node. The availability of more than one GO tag for each gene offers the opportu-nity to categorize genes on the basis of their GO features. For this reason we havedeveloped a GO clustering tool, written in Java and downloadable at http://www.bioinformatica.unito.it/downloads/clustering/GO-clustering/. The tool relies on the useof GO terms associated with genes annotated in locus link databases (ftp://ftp.ncbi.nih.gov/refseq/LocusLink/ LL.tmpl.gz) and on a k-way clustering tool specificallydeveloped for document clustering (CLUTO, http://www-users.cs.umn.edu/~karypis/cluto/). To cluster genes on the basis of their GO terms, users need to give a list oflocus link identifications (LL ids) (see Note 16) of the genes of interest and set theclustering parameters. The tool will download from the NCBI ftp site the latest ver-sion of the LL.tmpl file, and it will associate the GO biological function terms witheach of the LL ids supplied by the user. Our tool provides a graphical interface to setthe clustering parameters and offers text and graphical visualization of the resultingclusters. A good approach for defining the optimal clustering solution is based on theevaluation of internal cluster similarity (ISim), external similarity (ESim), entropy(Entpy), and purity (Purty) of the clustering solution. ISim displays the average simi-larity between the objects of each cluster, and ESim displays the average similarity ofthe objects of each cluster and the rest of the objects. Small entropy values and largepurity values indicate good clustering. The optimal clustering solution can be searchedfor by modifying at least some of the available clustering parameters, (e.g., number ofclusters, method to be used for clustering the objects, similarity function to be usedfor clustering, clustering criterion function, factor by which the program will prunethe clustering features before performing the clustering, number of different cluster-ing solutions to be computed by the various partitional algorithms, maximum numberof refinement iterations to be performed within each clustering step, and so on).An example of the clustering optimization procedure is as follows:
Step 1. Use default settings and change the number of clusters searching for increasingISim and decreasing ESim. During this process it is important to keep track of the numberof items (genes) present in the clusters.
Step 2. Keep the number of clusters constant and change the clustering methods(clmethod) to select the method that improves the clustering quality features (ISim, ESim,Entpy, Purty).
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Step 3. Repeat step 1 using the optimal clustering method.
Step 4. Change the similarity function to be used for clustering (sim) to define which oneimproves the clustering quality features. Repeat step 1.
Step 5. Change the clustering criterion (crfun) and select the one that improves the clus-tering quality features. Repeat step 1.
Step 6. Find the optimal value for the factor (colprune) by which the tool will prune theGO features before performing the clustering. This is a number between 0.0 and 1.0 andindicates the fraction of the overall similarity for which the retained GO features mustaccount. This parameter can be useful because some GO features just affect in a negativeway the clustering quality features, but pruning GO terms can reduce the number of itemsthat can be clustered. Repeat step 1. Table 1 shows the effect of pruning: reducing thenumber of GO terms involved in clustering the ISim is improving although the number ofclustered genes it is reduced. Pruning in this specific case affects the ESim, in a negativeway but the improvement in ISim counterbalances it.
Step 7. Other parameters are available for greater fine tuning of the clustering solution.However, by performing the previously described steps, a reasonable clustering solutionis obtained using GO terms.
Figure 8 gives an example of GO clustering done on the full set of annotated genescontaining p53-responsive elements in their promoters (20). The results of clusteringby GO terms can be a valuable tool to inspect microarray results under a functionalperspective and to identify whether specific biological functions are modulated at atranscriptional level. However, GO clustering is strongly influenced by the amount ofGO annotation available, which is still a limiting factor because not all gene areannotated.
4.3. Microarray Literature Data MiningWhen GO annotations are not available or more information is needed, the pub-
lished literature might provide a source of information to assist in the interpretation ofmicroarray data. Functional data are rapidly accumulating in the scientific literature,and the biologist needs to retrieve this information, which has been collected byMEDLINE (www.ncbi.nlm.nih.gov), a database that contains over 12,000,000 bio-medical journal citations. Finding gene correlations using MEDLINE is time-consum-ing, even if in recent years some tools have been developed to perform automatedinformation extraction on the MEDLINE database (21,22). For this reason we have
Table 1Effect of Colprune in Clustering Genes by GO Terms
Colprune
1.0 0.9 0.8
% of total items in clusters 0.97 0.59 0.43Average ISim 0.15 0.51 0.83Average ESim 0.007 0.026 0.059
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Fig. 8. Genes containing p53-responsive elements were extracted by the Wang et al. (20)data set and clustered by their GO annotations. Genes were classified in 12 groups, and acolprune of 0.9 was used to optimize the ISim of the clusters. The GO descriptive features ofclusters are as follows: cluster 1, GO:0006832 (small molecule transport) 98.7%; cluster 2,GO:0006366 (transcription from Pol II promoter) 98.0%; cluster 3, GO:0007165 (signal trans-duction) 98.2%; cluster 4, GO:0007048 (oncogenesis) 97.4%; cluster 5, GO:0007186 (G-pro-tein-coupled receptor protein signaling pathway) 96.7%; cluster 6, GO:0006508 (proteolysisand peptidolysis) 52.2%, GO:0006464 (protein modification) 47.1%; cluster 7, GO:0007275(development) 49.7%, GO:0006357 (regulation of transcription from Pol II promoter) 46.6%;cluster 8, GO:0007399 (neurogenesis) 47.0%, GO:0007155 (cell adhesion) 46.9%; cluster 9,GO:0006629 (lipid metabolism) 38.3%, GO:0006091 (energy pathways) 36.5%, GO:0006899(nonselective vesicle transport) 24.4%; cluster 10, GO:0000074 (regulation of cell cycle)54.2%, GO:0008283 (cell proliferation) 16.4%, GO:0008285 (negative regulation of cell pro-liferation) 16.4%, GO:0006468 (protein amino acid phosphorylation) 12.2%; cluster 11,GO:0007268 (synaptic transmission) 38.3%, GO:0007345 (embryogenesis and morphogen-esis) 35.1%, GO:0007601 (vision) 24.4%; cluster 12, GO:0006955 (immune response) 38.3%,GO:0007267 (cell-cell signaling) 21.7%, GO:0007166 (cell surface receptor-linked signaltransduction) 19.7%, GO:0006954 (inflammatory response) 13.1%, GO:0006960 (antimicro-bial humoral response) 6.1%.
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developed a tool (MedMOLE) that makes it simpler to extract functional knowledgeby literature abstracts directly/indirectly related to differentially expressed genes iden-tified by microarrays. In our specific application, it was necessary to let documentsgroup on the basis of their content, expressed in terms of nouns, verbs, and adjectives,as the functional meaning of genes should emerge from that context. Indeed, it wasnecessary to recognize the names of genes inside the texts both for querying pur-poses and for interpreting the results, i.e., being able to relate each group of docu-ments (or biological function) to the involved genes. The gene name recognition is aparticularly difficult task, even for the information extraction tools, as these names donot follow any predefined rule and have many aliases.
We then built a “gene name extractor” based on a gene names dictionary containingofficial gene names and aliases built on the basis of the LocusLink database. In orderto let scientists mine only the subset of MEDLINE abstracts that are of their owninterest, we built a web-based application, MedMOLE, that makes available on-line aportion of the MEDLINE database processed by our algorithms and ready for the min-ing phase (see Note 17). The dataset was generated by extracting from the NCBI’sPubmed (since 1990) all documents frequently containing gene or protein names bymeans of the query “(gene OR protein) OR (genes OR proteins).” We obtained about1.7 million documents containing gene names associated with 9609 LL ids out of thetotal 14,659 LL ids present in the gene names dictionary at the time this chapter wassubmitted. Users can mine the gene names-linked MEDLINE dataset using any generickey words or by applying a list of LL ids that interrogates the LocusLink database,implemented in our local SRS server, and builds a MedMOLE query using official andalias gene symbols. MedMOLE outputs (server-based) are made by graphical and tex-tual components (Fig. 9). Users can perform the analysis by applying default param-eters (30 clusters, clusters based on both frequent and rare words and intraclustersimilarity set to 0.35) or they might decide to define the number of clusters, select onlyrare or frequent words as objects of the clustering, and select the minimum similaritybetween documents inside a cluster. Each analysis has to be repeated various times inorder to optimize the clustering solution. After the user has selected the clusteringparameters and performed the clustering step, the results are shown as a list of clusters(Fig. 9A), each one described by the number of abstracts in the cluster, a link to theabstracts’ contents, the cluster number, up to seven key words being the most descrip-tive of the cluster, and a link to the gene names found in the cluster (report). The reportis particularly interesting because it contains a list of the gene names found in thecluster and statistics regarding the gene name features, as well as a list of potentialgene associations generated by using the a priori algorithm for the induction of
Fig. 9. (opposite page) (A) clusters defined by MedMOLE and listed by decreasingsize. (B) Graphic representation of the clusters generated by MedMOLE. Segmentslinking clusters indicate a certain grade of similarity between the linked clusters.The numbers associated to the linking segments indicate the degree of similarity.(Increasing numbers indicate an increase of similarity.) (C) The plot describes thefrequency of gene names found in the MedMOLE clusters.
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Fig. 9.
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association rules (23,24). The Agrawal A Priori rules induction algorithm is a power-ful method to find regularities in a set of documents/transactions. The tool tries toidentify sets of items that are frequently found together, so that from the presence ofcertain items in a set of documents one can infer that other items are present, e.g., geneA, gene B → gene X (support, confidence). Consider a set (T) of all abstracts contain-ing a group of gene names. The support of a subset of gene names S is the percentageof those abstracts in T containing S. As an example, abstracts contain a given set S ofgene names, e.g., S = {AP2, SP1, P53}. If U is the set of all abstracts containing allgene names in S, then support (S) = (|U|/|T|) * 100%, where |U| and |T| are the numberof abstracts in U and T, respectively. The confidence of a rule R = “gene A and gene B→ gene C” is instead the support of the set of all items that appear in the rule dividedby the support of the antecedent of the rule, i.e., confidence (R) = [support({A, B, C})/support({A, B})] * 100%.
To give an idea of the potentiality of the tool, we have analyzed with MedMOLEthe 38 genes, containing p53-responsive elements, present in the GO cluster 8 (Fig. 8;cluster 8 has as main descriptive features the GO terms neurogenesis and cell adhe-sion). MedMOLE was queried over 1996–2002, using gene LL ids together with thekey word p53, in order to identify which of those genes have already been described inassociation with the p53 gene. From this analysis we obtained 920 documents thatwere clustered in 30 groups. We observed that only 8 of the 38 genes used in the querywere present in the clusters, and 4 of them were associated by the a priori rule induc-tion algorithm to p53. So, with few mouse clicks, it was possible to determine thatonly 10% of the genes related to the GO cluster 8 can be found in abstracts togetherwith p53 and that four of them show a strong statistical association with p53.
In our opinion MedMOLE is a tool that can offer an easy way to navigate the scien-tific literature related to a group of genes derived by microarray analysis (see Note 18).In particular, the choice of clustering the abstracts, by their informational content,might help in the identification of a specific biological function bound to differentialexpression. Furthermore, the availability of the a priori rule induction algorithm cansimplify the identification of potential functional association between genes.
5. Notes1. Microarray analysis have been used successfully to define transcriptional signatures to
allow for patient-tailored therapy strategy in breast cancer (25) or to classify better tumorshaving no histological counterparts in normal tissues (26), such as synovial sarcomas,which are grouped together as “miscellaneous soft tissue tumors” in the latest edition ofthe WHO Soft Tissue Tumor Classification (27).
2. Microarray analysis can be a useful tool to identify genes directly activated/repressed byexpression of a transcription factor (e.g., STAT1, p53, p63, and so on). Subsequently,primary response genes can be identified by computational searching of factor-specificresponsive elements in a DNA region located upstream of genes found to be differentiallyexpressed in microarray experiments.
3. At the time this chapter was submitted, the average knowledge of model organisms(e.g., Escherichia coli, Saccharomyces cerevisiae) was more than 80%, which meansthat more than 80% of the genes in E. coli/S. cerevisiae have been annotated (i.e., anno-
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tated genes are those assigned to a specific biological pathway and/or biochemicalfunction). On the other hand, higher eukaryotes (e.g., Homo sapiens, Mus musculus) areless characterized, which means that less than 30% of the genome is functionallyannotated.
4. Automated partial DNA sequencing on randomly selected tissue-specific complementaryDNA (cDNA) clones was used to obtain a collection or short sequences associated withexpressed sequence tags (ESTs). ESTs are mainly located at the 3' end of the genesequence. This fast approach to cDNA characterization facilitated the tagging of mostexpressed human genes before the genome sequence.
5. Kane et al. (2) suggest that the cDNA probe similarity might cause inaccurate expressionmeasurements on cDNA microarray, and spotting more specific cDNA probes of theunique regions from a set of genes and reducing the length of the probe sequences couldreduce the potential for cross-hybridization. Probe length may also affect the degree ofnonspecific hybridization, as observed in part on cDNA arrays. Therefore, the use ofoligonucleotides might overcome the described limitation of cDNA arrays. Furthermore,oligonucleotide synthesis is now less expensive than cDNA preparation. Owing to thelarger number of operations required for cDNA preparation with respect to oligonucle-otide synthesis, the latter has a limited rate of gene/sequence misleading assignment, andthe availability of all genome sequences for various eukaryotic genomes allows the opti-mization of gene-specific oligonucleotide design.
6. Spotting, hybridizing, and reading instruments are commercially available. However, ourcurrent strategy, if low-density arrays are needed, is to buy custom-spotted arrays fromspecialized companies as the setting up of microarray spotting facilities can be long, frus-trating, and expensive.
7. A postlabeling procedure is more suitable because the yield of cDNA is higher than withdirect incorporation. Indirect incorporation of the reactive dyes of Cy3 and Cy5 into theprobe is greater and more even than CyDye-labeled nucleotide incorporation. The postla-beling method is less prone to incorporating artifacts caused by the size of CyDye nucle-otides (e.g., chain termination, proximity quenching, sequence-specific bias).
8. The BioChip Arrayer (Perkin Elmer Life Sciences, Boston, MA) is based on a noncontactdispensing technology. The PiezoTip™ never comes into direct contact with surface,reducing the risk of carryover contamination and enabling high-quality spotting (homog-eneous spot size, homogeneous deposition in all spot surface, and so on) at high densities.
9. Absolute expression of a gene is described by two parameters:a. Absolute call, which is a qualitative index of gene expression. Absolute call is defined
using three letters: P, M, and A, where P indicates the presence of gene expression,M indicates borderline expression, and A signifies expression absence.
b. Absolute intensity, which is a quantitative description of gene expression equal to thefluorescence intensity value measured averaging PM-MM (Microarray AffymetrixSuite V).
10. CyberT is a web-based application; a mirror site is available at www.bioinformatica.unito.it.
11. For more information about the image files manipulation performed by MAS 5.0, checkthe Affymetrix manual (www.affymetrix.com).
12. Increasing τ can reduce the number of false present calls but may also reduce the numberof true present calls.
13. Decreasing the significance level α1 can reduce the number of false detected calls andreduce the number of true detected calls. Increasing the significance level α2 can reducethe number of false undetected calls and reduce the number of true undetected calls.
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14. Owing to the limited availability of biological materials or to budget limitations, a largenumber of replicates (more than four) can rarely be done. However, in our hands a rea-sonable compromise is to perform experiments as biological triplicates.
15. The p value generated by CyberT indicates the probability that a differential expression iscaused by chance. In other words the p value indicates the probability that the averageintensity of the control samples belongs to the same distribution of the experimentalsamples.
16. LocusLink provides a single-query interface to a curated sequence and descriptiveinformation about genetic loci. It contains information on official nomenclature, aliases,sequence accessions, phenotypes, EC numbers, MIM numbers, UniGene clusters, homol-ogy, map locations, and related web sites. Sequence accessions include a subset ofGenBank accessions for a locus, as well as the NCBI Reference Sequence (RefSeq).The reference sequences are generated applying a seed sequence as a query in a BLASTanalysis. For mRNAs, BLAST results are sorted to identify the longest sequence thatretains a gap-free alignment with a minimum of mismatches through the coding region.Sequences that also have a full-length coding region are used to create the predicted andprovisional RefSeq mRNA/protein records. These RefSeq records are generated via anautomatic process. All provisional RefSeq records will undergo a manual review step.During the review process additional RefSeq records may be generated to represent well-characterized transcript variants.
17. MedMOLE is a web-based tool that can be used upon registration at http://www.cineca.it/HPSystems/Chimica/medmole/index.html. Interfaces for querying MedMOLE by LL ids,gene names, or key words are available at http://www.bioinformatica.unito.it/bioinformatics/medmole/welcome.html.
18. Examples of the use of MedMOLE in the analysis of data derived by microarrays can befound at http://www.bioinformatica.unito.it/bioinformatics/medmole/tutorials.html.
AcknowledgmentsThis work was partially supported by PRIN2001 2001057147 and FIRB RBAU01JTHS/
RBNEO157EH grants of the Italian Ministry of University and S&T Research-MIUR.
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