- Research article
- Open Access
Comparison and consolidation of microarray data sets of human tissue expression
© Russ and Futschik; licensee BioMed Central Ltd. 2010
- Received: 17 February 2009
- Accepted: 14 May 2010
- Published: 14 May 2010
Human tissue displays a remarkable diversity in structure and function. To understand how such diversity emerges from the same DNA, systematic measurements of gene expression across different tissues in the human body are essential. Several recent studies addressed this formidable task using microarray technologies. These large tissue expression data sets have provided us an important basis for biomedical research. However, it is well known that microarray data can be compromised by high noise level and various experimental artefacts. Critical comparison of different data sets can help to reveal such errors and to avoid pitfalls in their application.
We present here the first comparison and integration of four freely available tissue expression data sets generated using three different microarray platforms and containing a total of 377 microarray hybridizations. When assessing the tissue expression of genes, we found that the results considerably depend on the chosen data set. Nevertheless, the comparison also revealed statistically significant similarity of gene expression profiles across different platforms. This enabled us to construct consolidated lists of platform-independent tissue-specific genes using a set of complementary measures. Follow-up analyses showed that results based on consolidated data tend to be more reliable.
Our study strongly indicates that the consolidation of the four different tissue expression data sets can increase data quality and can lead to biologically more meaningful results. The provided compendium of platform-independent gene lists should facilitate the identification of novel tissue-specific marker genes.
- Gene Ontology
- False Discovery Rate
- Gene List
- Microarray Platform
- Tissue Class
Tissue in the human body shows a fascinating variety of structures and functions ranging from simple protection by the epidermis to complex information processing in the cortex. All these different types of tissues have in common that they are highly adapted to their specific task. How such variety can emerge from the same genetic code is not only scientifically an intriguing question, but also of fundamental medical relevance. To address this question, a necessary step is the generation of comprehensive catalogs of gene expression in different tissues.
Microarrays have become highly suitable tools for such an ambitious endeavour as they allow the genome-wide measurement of transcription abundance. Most early gene expression studies of human tissues were disease-orientated, i.e. the expression profiles of pathogenic tissues were compared to corresponding normal tissues. For instance, microarrays have extensively been used to detect previously unrecognized cancer subtypes [1, 2] and to predict clinical outcome [3, 4]. Yet, it is also crucial to create a comprehensive gene expression atlas for normal human tissues to facilitate rapid identification of new marker genes for improved diagnosis and of target genes for medical interventions. Furthermore, such atlas would undoubtedly increase our general understanding in physiology and would give us new insights into functions of genes. Indeed, large-scale microarray experiments have recently been used to construct such powerful expression repositories [5–8].
However, general concerns about the reliability of microarray data have been raised by several studies showing poor congruence of different microarray platforms [9–12]. Moreover, there are many experimental factors such as array platform, tissue handling, RNA isolation method and hybridization procedure which strongly influence the results of expression studies. As a consequence, results derived using a single microarray experiment may be seriously compromised by experimental bias. In contrast, the use of multiple independent data sets can be beneficial for the data reliability [13, 14].
This motivated us to critically compare and to consolidate four major microarray data sets of human tissue expression due to their outstanding importance for functional genomics and large-scale systems biology. Notably, although these data sets have rapidly become popular resources in biomedical research [15–19], this is the first study which systematically compares them. The ultimate aim of our investigations was to construct a compendium of platform-independent gene lists assessing the expression in normal human tissues by various means. Such a compendium can not only dispense biomedical researchers from cumbersome manual querying of multiple data sets, but also help to avoid potential pitfalls caused by platform-dependent artefacts.
The outline of the study was the following: After compilation of the tissue expression data sets, we first examined their overall similarity. For the assessment of tissue specific expression, we generated multiple gene lists utilizing complementary measures to detect distinct features of tissue expression. These lists were subsequently used for a critical comparison of results derived from the different experiments. Finally, we assembled lists of platform-independent tissue-specific genes. The assessment of the consolidated data indicated that they can provide a more reliable basis for the study of tissue expression.
Compilation of tissue expression from different experiments
Summary of analyzed tissue expression data sets.
Number of genes
Number of samples
Number of tissues
Johnson et al., Science 2003
Agilent oligonucleotide exon microarrays
Schadt et al., Genome Biology 2004
Agilent oligonucleotide microarrays
Shyamsundar et al., Genome Biology 2005
Dual-channel cDNA microarrays
Su et al., PNAS 2004
Affymetrix HG-U133A and GNF1H arrays
For comparative analysis, the microarray probes were mapped to their corresponding Entrez Gene identifiers to obtain a common index system. Expression values of replicated probes were averaged. To facilitate the comparison, the samples were assigned to 19 main tissue classes based on their physiology and histology. Subsequently, the expression values were averaged over samples belonging to the same tissue class. Further details of the applied data pre-processing and the assignments of samples to tissue classes can be found in the Methods section and the Additional file 1 (Supplementary Materials).
Comparison of tissue expression data sets
Gene-wise correlation of correlations.
Assessment of tissue specificity
A main purpose of the four tissue expression data sets has been the identification of tissue-specific and housekeeping genes. Does the observed divergence influence the detection of such genes? To address this question we used two different measures to capture distinct features of tissue-specific expression.
Identification and comparison of tissue-specific genes
To examine in more detail the similarity of tissue-specific expression in different data sets, we created separate gene lists for each data set and tissue type using the described measures. Genes with positive PEM score and FDR < 0.25 were defined here as tissue-specifically over-expressed and with negative PEM score and FDR < 0.25 as tissue-specifically under-expressed. For assessing the uniqueness of tissue expression, we defined a gene as uniquely over-expressed in a given tissue if it is maximal expressed in this tissue and FDR < 0.25 holds for the corresponding MAX score. Note that we have chosen a rather high FDR, but the main conclusions of our comparison also hold for more stringent values (see also Additional file 1 - Figure S1).
Notably, the number of genes detected as tissue specific appeared to depend on the numbers of available samples in a tissue class (Additional file 1 - Table S2). Tissue classes with fewer samples seemed to be associated with fewer tissue-specific genes. To elucidate this statistical dependency in more detail, we simulated the effect of a reduction in samples on the detection of tissue-specifically over-expressed genes. Here, we chose the brain tissue class as reference, since it comprised the largest numbers of samples in all four data sets (Additional file 1 - Table S2) and also yielded large numbers of over-expressed genes (Figure 5). Based on random sampling, the number of brain samples in a data set was reduced while the samples in all other tissue classes were conserved. Subsequently, the number of brain specific genes in the modified data set was calculated. Generally, a strong decrease in the number of significant genes was observed for a reduced number of included brain samples confirming the suspected interference. For instance, less than 500 genes were on average classified as brain specific in the Geneatlas data set if only 6 brain samples were included. This contrasts the 1123 genes detected using the full set of 44 brain samples (Additional file 1 - Figure S5).
To enable a less biased comparison between tissues, we sought to reduce the influence of the number of samples on the number of detected genes by a tissue-specific adjustment of the FDR threshold for significance. Again, brain tissue was used for calibration. The underlying idea was to select a modified FDR threshold, so that the number of brain-specific genes found for a reduced data set (i.e. with a smaller number of brain samples) remains the same as for the full data set with the original FDR threshold. As before, we selected randomly brain samples, constructed reduced data sets and calculated the significance of genes. For calibration, the number of selected brain samples was set equal to the number of samples found in the other tissue classes. As result, we observed that the FDR threshold had generally to be increased for tissue classes including fewer samples (Additional file 1 - Table S5). For example, the adjusted FDR threshold of 0.08 for liver tissue, which comprises of 5 samples in the Stanford set, corresponds to a FDR threshold of 0.05 for brain tissue, which comprises of 8 samples in the same data set. For muscle tissue with only 3 samples, the equivalent threshold is considerably larger with FDR < 0.18. Notably, the application of the adjusted FDR thresholds resulted in drastic changes in the distribution of tissue-specially over-expressed genes (Additional file 1 - Figure S7A-D). In particular, tissue classes with few samples showed a strong relative increase in tissue-specific genes.
For our comparison, we utilized the pre-processed data as provided by the authors of the microarray experiments (except for the Stanford data set which was additionally normalized), as most other previous studies employing these resources have also used the readily available pre-processed data and our analyses should be applicable for the interpretation of these studies. However, it is well known that normalization and pre-processing procedure can have considerable influence on the results of microarray data analyses. Also, it has been shown that re-annotation of Affymetrix GeneChips can improve the quality of derived transcriptional data. Thus, we examined whether alternative data pre-processing procedures may improve the concordance of the microarray data sets. For the Geneatlas dataset, we used an alternative annotation and normalization scheme as described in the Methods section. The Rosetta1 and Rosetta2 datasets were additionally log-transformed. Finally, between-array normalization was applied to all four data sets. For comparison, we applied the same procedures as for the original datasets, i.e. correlation of correlation and correlation of brain-specific expression, and calculated the overlap for uniquely over-expressed genes. Notably, we did not observe a general increase in the similarity. While the measured correlation values tend to decrease (Additional file 1 - Table S3, Figure S3), the overlap slightly increased (Additional file 1 - Figure S4). Nevertheless, the overlap of all 4 alternatively pre-proceeded data sets remained small with 240 genes compared to 232 for the original data sets. As a general improvement of similarity was not observed using alternative pre- processing and annotation schemes, we chose to use the original data sets for the subsequent analyses. This choice also facilitates the assessment of previous studies which were generally based on the original data sets. Notably, the comparison of alternative processing schemes for the Geneatlas data set revealed a strong similarity of expression changes as well as a highly significant overlap of detected tissue-specific genes (Additional file 1 - Table S4, Figure S5).
Uniquely over-expressed genes in brain and liver
The conducted comparison revealed a substantial influence of the microarray platform on the composition of the constructed gene lists. As such platform-dependent bias can compromise the data quality; we reasoned that a consolidated list of common tissue-specific genes might yield more reliable results. This list could be simply generated by direct intersection of lists derived from the different experiments. However, Figures 6 and S2 show that such procedure would result in a very limited number of genes. We therefore chose the alternative scheme (described in the following section) to merge the evidence from different data sets.
Genes obtaining large MAX scores in all four data sets should be strong candidates for reliably being tissue-specifically over-expressed. We assessed this hypothesis by examining in detail brain- and liver-specifically over-expressed genes. For integrative scoring, we first ranked the genes in the four data sets according to their MAX values and calculated their mean ranks, which was subsequently used for generated consolidated lists. The follow-up analysis of the resulting gene lists demonstrated that this simple scheme is remarkably efficient for a reliable identification of tissue-specific genes.
Top 20 brain-specific genes.
Glial fibrillary acidic protein, a major intermediate filament proteins of astrocytes
Stathmin-like 2, a neuronal growth-associated protein
Growth associated protein 43, regulates growth of axons during development and regeneration
Neurofilament, light polypeptide, a major constituent of the axoskeleton
Proteolipid protein 1, predominant myelin protein present in CNS
Peripheral myelin protein 2
Gamma-aminobutyric acid (GABA) B receptor 2
Internexin neuronal intermediate filament protein alpha
Dynamin 1, involved in clathrin-mediated endocytosis
SH3-domain GRB2-like 2, Endophilin 1, mediator of synaptic vesicle formation
Synaptosomal-associated protein 25 kDa, a SNARE protein required for neuronal exocytosis
Neural protein 22
Myelin basic protein, major constituent of myelin sheath of oligodendrocytes and Schwann
Tubulin polymerization promoting protein
S100 calcium binding protein B, glial-derived protein serving as neurotrophic factor and neuronal survival protein
Neurofilament, medium polypeptide 150 kDa
Neurocan, involved in the modulation of cell adhesion and migration.
Neurospecific KIF3C kinesin family member 3
Secretogranin III, a neuroendocrine secretory protein
Stathmin-like 4, regulation of the microtubule cytoskeleton
Top 20 liver-specific genes.
Hemopexin, heme-binding plasma protein synthesized by the liver
Serpin peptidase inhibitor, clade D, member 1, cofactor of heparin in plasma
Solute carrier family 22 member 1, main organic cation uptake system in hepatocyte
Serpin peptidase inhibitor, clade C (antithrombin), member 1
Retinol dehydrogenase 16, involved in lipid metabolism in liver
Apolipoprotein C-II, component of very low density lipoprotein
Cytochrome P450, family 2, subfamily E, polypeptide 1. cytochrome oxidase system
Serpin peptidase inhibitor, clade A (antitrypsin), member 7
Cytochrome P450, family 2, subfamily 2, polypeptide 9 -
Cytochrome P450, family 3, subfamily A, polypeptide 7
Complement component 8, beta polypeptide
Complement component 8, alpha polypeptide
Vitronectin - plasma protein promoting cell adhesion
Apolipoprotein H (beta-2-glycoprotein I)
CPS1 carbamoyl-phosphate synthetase 1, enzyme in the hepatic urea cycle
Carboxypeptidase B2 plasma protein regulating fibrinolyses,
Histidine-rich glycoprotein, plasma protein
Apolipoprotein B, isoform apoB-100, exclusively synthesized in the liver
Fatty acid binding protein 1 found in the liver
Solute carrier family 27 (fatty acid transporter), member 5, involved in lipid synthesis
Assessment of reliability of derived gene lists
The inspection of the derived consolidated lists for brain and liver indicated that multiple confirmations led to accurate identification of tissue-specific genes. Since the comparison of tissue-specific genes pointed to a considerable platform dependency, another positive aspect of the proposed integration method would be a reduced dependency, or respectively, an increased reliability of the detected gene lists. Clearly, the derived consolidated gene lists show reduced platform dependence. But is this simply the consequence that the MAX scores from all compared platforms were merged, or can the proposed integration method still reduce the platform dependency of gene lists when compared to data which were not included in the integration? To assess whether the latter is the case, we conducted a cross-validation. We examined whether the consolidated MAX scores for brain tissue derived from three integrated data sets show a higher correlation with the MAX scores of the fourth data set than with the MAX scores of the three individual data sets. This procedure was performed for all four microarray experiments.
Gene ontology analysis of single data sets and integrated data
Assessment of the results showed that we obtained biologically more reasonable results for consolidated lists. For example, pancreas tissue was strongly associated with the GO category "proteolysis", which corresponds to its exocrine function. Similarly, liver tissue was strongly associated with "metabolism", which is known to be the most prominent function of the liver (Figure 8A). In contrast, we do not obtain such a strong association for the gene lists based solely on the Geneatlas data set (Figure 8B). Moreover, tissues based predominantly (or to a high grade) on muscle cells such as (skeletal) muscle, heart (i.e. cardiac muscle) and trachea (which contains smooth muscular fibres) form a cluster based on the consolidated gene lists. Likewise, the lymphoid tissues, i.e. lymph node and tonsil, showed a similar GO annotation profile and were grouped together. This was not the case for the clustering based on tissue-specific genes that were derived using Geneatlas data only. Notably, similar short-comings were observed for corresponding clustering analyses of the other three single data sets (Additional file 1 - Figure S8). These findings support the conjecture that the consolidated gene lists lead to more reliable and biologically more meaningful results.
Microarrays have become widely used tools to measure gene expression for the prediction of disease outcome, the profiling of new disease subtypes and the identification of disease-specific markers [1–4]. To achieve these goals, it is crucial to acquire detailed knowledge about expression levels in normal tissue. Several microarray studies have therefore aimed to profile human tissue expression and have rapidly become important resources for biomedical investigations [15–19]. However, the vast majority of the subsequent analyses (including our own ) have relied on only one of these data sets. Any bias inflicted by the chosen data set may severely compromise the reliability of results. Due to their increasing importance, we re-analysed four large tissue expression data sets. Our aim was to examine whether a platform-dependency is prominent or rather neglectable compared to the biological variability monitored in these data sets.
Remarkably, we found that the detected tissue-specific expression is strongly influenced by the choice of microarray platform. The observed divergence could have multiple reasons. Foremost, the set of tissue samples was not the same for the compared experiments. Thus, differences would be caused by biological variation. Furthermore, experimental factors such as difference in probe sequences, RNA handling, hybridization protocols, scanning, and image analysis are likely to contribute to the observed divergence. In our study, we applied some alternative pre-processing schemes to improve the concurrency of the data sets. Although these schemes did not yield a larger overlap of tissue-specific genes, we anticipate that improved pre-processing procedures can support the consolidation of the data generated by different microarray data sets. Especially, a more accurate annotation of probes (i.e. their mapping of corresponding genes) will help to improve the comparability of microarray platforms [26, 27]. Thus, ongoing efforts in this direction remain important for the re-utilization of existing microarray data.
Despite the differences, however, all four data sets show a larger similarity than expected by chance. This similarity provided us the basis for the consolidation of the different expression experiments. In fact, dissimilarity of expression patterns can be used to eliminate spurious expression patterns. It has recently been demonstrated that the expression of genes displaying a similar pattern for different platforms tends to have an increased reproducibility with respect to external validation by qRT-PCR . Thus, cross-platform comparison can be employed as powerful means to eliminate unreliable gene expression measurements.
Therefore, we propose the integration of multiple tissue expression data sets. We generated lists of tissue-specific genes for each single data set and integrated them to a single consolidated list. Naturally, data integration would not be beneficial if any of the data sets to be integrated is unreliable. However, the re-analyses as well as the original studies strongly demonstrated that each of the data sets shows convincible overall reproducibility. Nevertheless, we observed biologically more accurate results for the integrated list in contrast to lists derived by one experiment only in an exemplary GO analysis. Additionally, we constructed a compendium including tissue expression data for 18909 non-redundant genes and scores for tissue specificity to allow researchers their own analysis. The consolidation is based on simple, but yet effective scoring methods. We would like to note that the rather stringent requirements can be relaxed using the compendium. For example, we would find a considerably larger overlap of tissue-specific genes if we only require that they are shared between two or three data sets. Therefore, researchers using the compendium are encouraged to adjust the thresholds to their own requirements. To facilitate such further analysis our compendium provides the complete data sets with the specificity scores. The compendium will be particularly beneficial for analyses involving a large number of genes. A possible application might be the construction of tissue-specific networks which have attracted increased attention in systems biology [29, 30]. Using the compiled data, potential platform-dependency can easily be eliminated in order to avoid severe artefacts in the analyses.
In conclusion: Although various studies have compared disease-related microarray experiments, this analysis constitutes the first systematic comparison of large-scale microarray data sets of normal human tissues. We detected a prominent platform bias, which however can be overcome by data integration. Thus, an important contribution of this study is in the compilation of platform-independent tissue-specific lists which will be provided freely. We anticipate that they will be of great asset for biomedical scientists. Finally, we hope that our study provides a solid basis for the future wide-spread use of microarray tissue expression data in genomic research.
Data collection and pre-processing
The data sets Rosetta1  and Rosetta2  were provided directly by the authors. Both data sets were generated using Agilent spotted oligonucleotide microarrays. The provided data sets comprised expression patterns for 10,000 and 50,000 transcripts in 50 and 54 different tissues, respectively. The data sets provided have been background corrected (due to spatial variation and sequence-specific effects) and normalized using dye-swap normalization (i.e. based on reverse labelling of target and reference samples) by the authors. For the assessment of the influence of preprocessing on the comparison, we additionally log-transformed and subsequently normalized the data across arrays using the quantile normalization which is based on the transformation of the original value to the corresponding quantile's value of a reference chip 
Shyamsundar et al.  measured gene expression in 115 tissue samples on dual-channel cDNA microarrays containing 39,711 human cDNAs. The raw data was downloaded from the Stanford Microarray Database. We normalized the raw data using optimized local intensity-dependent normalization (OLIN) which is based on iterative local regression and optimization of model parameters by internal cross-validation. It has been demonstrated that it can correct favourably for potential dye and spatial biases in two-channel microarray data [32, 33]. Optionally, quantile normalization was applied.
Su et al.  created a gene expression profile of 79 human tissues using Affymetrix HG-U133A and customized GNF1H arrays comprising 45,953 probe sets. Altogether, 16499 non-redundant genes were profiled by replicated hybridizations.
Expression summaries had been derived using the Affymetrix Microarray Suite 5 (MAS5) algorithm with global median scaling. The MAS5-processed data were downloaded from the Geneatlas webpage. To improve the concordance with the other data sets, we also calculated expression levels using an alternative annotation and normalization scheme. Starting from the CEL files (downloaded from NCBI GEO repository - Series GSE1133), we used an customized CDF annotation file for the U133A GeneChip (downloaded from http://www.xlab.unimo.it/GA_CDF/) and the GC-RMA normalization method for the calculation . After quantile normalization, the data were merged with the GNF1H-derived and GC-RMA normalized data (downloaded from Geneatlas website).
Notably, Rosetta1 and Rosetta2 does not include replicates of samples, whereas Stanford contains biological replicates and Geneatlas includes technical replicates by hybridizing the same tissue samples to two different Affymetrix chips. For comparative analysis, microarray probes were mapped to a common index system, in our case Entrez Gene Ids using the annotation provided by the authors or the SOURCE database http://smd.stanford.edu/cgi-bin/source/sourceSearch. Probes which could not be mapped were excluded from the analysis. Signals from probes linked to the same gene were averaged. In order to assess and to compare the tissue expression, the samples were assigned based on their physiology and histology to 19 main tissue classes found in all four data sets. The 19 main tissue classes are adrenal gland, brain, heart, kidney, liver, lung, lymph node, muscle, prostate, pancreas, placenta, salivary gland, thymus, thyroid, tonsil, testis, trachea, uterus, and uterine corpus. Expression values of tissues samples assigned to the same class were averaged to facilitate the comparison. Tables including the averaged expression values for the analysed data sets can be found as Additional files 2, 3, 4 and 5. Detailed information about the performed classification of tissues and the distribution of tissue across the different microarray experiments can be found in the Additional file 1 - Table S2.
Correlation of correlations
where U ij is the Spearman rank correlation of genes i and j in data set U, V ij is the Spearman rank correlation of genes i and j in data set V, and the sums are over all distinct pairs of genes i and j. Large correlation of correlations signifies similar co-expression of genes in two data sets. Similarly, we can proceed for tissue-based comparisons. The same steps as for the gene-based comparison were applied, but the Spearman correlation coefficients were calculated across all genes for all possible pairs of tissue types. For the tissue-based comparison, large correlation of correlations indicates similar tissue expression patterns.
The obtained results were used for hierarchical clustering to evaluate the similarity of expression patterns in the compared data sets. As distance measure, we chose 1-r UV . Clustering was conducted using Ward's minimum variance method and visualized with clustered image maps (heatmaps).
Measures for tissue specific expression
where PEM o and PEM b are the scores calculated for the original and randomized data sets, N is the number of randomization (N = 100) and δ(x) = 1 for x ≥ 0, respectively δ(x) = 0 for x < 0. Thus, the significance of the observed PEM scores can be derived by comparison with the generated background distributions . For our analysis, genes with a positive PEM score achieving FDR < 0.25 were defined as tissue-specifically over-expressed whereas genes with negative PEM score and a FDR < 0.25 were defined as under-expressed.
where fc(t) comprises the fold changes in all tissues t compared to the mean expression and fc(t') comprises the fold changes in all tissues except the one with the highest fold change of this gene, respectively. Note that this MAX score is assigned to a gene only for the tissue class with the largest expression; all other MAX scores of the gene are set to zero for the remaining tissue classes. A gene with a large MAX value shows high gene expression in one tissue but not in the other tissues, i.e. it is uniquely over-expressed. To assess the significance, the same permutation-based procedure as applied to PEM scores was employed. In our study, genes with a FDR < 0.25 were denoted as uniquely tissue-specifically over-expressed. The derived MAX and PEM scores as well as their corresponding FDR for different tissues and data sets can be found in Additional file 6 and 7. For the Geneatlas data set, we additionally provide the corresponding results for the alternative pre-processing and annotation scheme in Additional file 8.
The detected dependence between the number of tissue-specific genes and the number of samples included in a tissue class may interfere with a comparison among tissues. In order to reduce such interference, an adjustment of FDR thresholds for tissue-specific over-expression was performed for each data set independently. First, we calculated the number of genes N which were significantly over-expressed in brain in a chosen data set based on a FDR threshold of α (with α = 0.01, 0.05, 0.10 and 0.25). Second, a sub-set of s brain samples were randomly selected and the remaining non-selected brain samples were subsequently excluded from the data set. PEM scores for brain-specific over-expression and corresponding FDR were derived as previously described for this reduced data set. The FDR threshold for significant brain-specific over-expression was adjusted to a level α' so that the number of significant genes was the same as detected number N for the full set using a FDR threshold of α. The procedure was repeatedly performed (n = 103) generating a distribution of adjusted thresholds. As a conserved estimate for adjustment of the FDR threshold, the 5th percentile was chosen. The size s of the sub-set was set to values that reflected the number of observed samples in different tissue classes (given by Additional file 1 - table S2).
Assessment of reliability by cross-validation
To assess the reliability of the consolidated gene lists we employed a cross-validation method. First of all, we reduced the number of genes to 6685, i.e. all the genes analyzed in all four data sets. Next, only those genes with MAX score > 0 for brain in all four data sets were selected (140 genes). For those genes we calculated the Spearman rank correlation between each of the four data sets. For cross-validation we consolidated three of the four data sets for each of the four possible combinations. For assessment of reliability we calculated the Spearman rank correlation of the consolidated gene lists and the gene list set not included in the consolidation and compared this value to the mean of the correlation values for the gene lists set based on individual data sets.
GO analysis of over-expressed genes
We utilized gene annotation by Gene Ontology to assign tissue types to biological processes. Here, we assessed whether over-expressed genes in a given tissue type tend to be associated with specific biological processes. First, we calculated the enrichment of tissue-specific genes based with a PEM > 0.8 in biological process categories. The obtained scores for enrichment can subsequently be used to assign weighted profiles of biological processes to the given tissue type. A well-known difficulty, however, is the selection of the suitable set of GO categories, as many of categories are highly specific. Here, we aimed to select only those categories that include enough genes for adequate statistical validation but not too many genes to ensure the functional homogeneity of member genes. Following a previously proposed scheme, we chose therefore so-called informative GO categories: Each GO category should contain more than 300 genes and each of their child categories should contain less than 300 genes . The significance of association between tissue type and GO category was calculated using Fisher's exact test assessing the accumulation of over-expressed genes in a tested category. P-values obtained were adjusted and converted to false discovery rates (FDRs) using the Benjamini-Hochberg method . A small FDR signifies enrichment of tissue-specifically over-expressed genes in a GO category. To examine the similarities of enrichment profiles of the different tissue types, the results were visualized as cluster image maps.
We thank John Castle from Rosetta Inpharmatics LLC for providing us the tissue expression data sets (i.e. Rosetta1 and Rosetta2) and the reviewers for the valuable suggestions. We are also grateful to Miguel Hernandez and Ravi Kalathur for critical reading of the manuscript. The work presented was supported by the SFB 618 grant of the Deutsche Forschungsgemeinschaft (DFG). MF acknowledges the funding by the Fundação para a Ciência e Tecnologia ( IBB/CBME, LA, FEDER/POCI 2010).
- Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000, 403: 502-511. 10.1038/35000501.View ArticleGoogle Scholar
- Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999, 286: 531-537. 10.1126/science.286.5439.531.PubMedView ArticleGoogle Scholar
- Futschik ME, Sullivan M, Reeve A, Kasabov N: Prediction of clinical behaviour and treatment for cancers. Applied bioinformatics. 2003, 2 (3 Suppl): S53-58.PubMedGoogle Scholar
- Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, Gascoyne RD, Muller-Hermelink HK, Smeland EB, Giltnane JM: The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med. 2002, 346: 1937-1947. 10.1056/NEJMoa012914.PubMedView ArticleGoogle Scholar
- Johnson JM, Castle J, Garrett-Engele P, Kan Z, Loerch PM, Armour CD, Santos R, Schadt EE, Stoughton R, Shoemaker DD: Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science. 2003, 302: 2141-2144. 10.1126/science.1090100.PubMedView ArticleGoogle Scholar
- Saito-Hisaminato A, Katagiri T, Kakiuchi S, Nakamura T, Tsunoda T, Nakamura Y: Genome-wide profiling of gene expression in 29 normal human tissues with a cDNA microarray. DNA Research. 2002, 9: 35-45. 10.1093/dnares/9.2.35.PubMedView ArticleGoogle Scholar
- Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Saphinoso LM, Moqrich A: Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci USA. 2002, 99 (7): 4465-4470. 10.1073/pnas.012025199.PubMed CentralPubMedView ArticleGoogle Scholar
- Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G: A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci USA. 2004, 101 (16): 6062-6067. 10.1073/pnas.0400782101.PubMed CentralPubMedView ArticleGoogle Scholar
- Kuo WP, Jenssen TK, Butte AJ, Ohno-Machado L, Kohane IS: Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics. 2002, 18 (3): 405-412. 10.1093/bioinformatics/18.3.405.PubMedView ArticleGoogle Scholar
- Michiels S, Koscielny S, Hill C: Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005, 365: 488-492. 10.1016/S0140-6736(05)17866-0.PubMedView ArticleGoogle Scholar
- Miklos GLG, Maleszka R: Microarray reality checks in the context of a complex disease. Nat Biotechnology. 2004, 22: 615-621. 10.1038/nbt965.View ArticleGoogle Scholar
- Tan PK, Downey TJ, Spitznagel ELJ, Xu P, Fu D, Dimitrov DS, Lempicki RA, Raaka BM, Cam MC: Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 2003, 31: 5676-5684. 10.1093/nar/gkg763.PubMed CentralPubMedView ArticleGoogle Scholar
- Huminiecki L, Lloyd AT, Wolfe KH: Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases. BMC Genomics. 2003, 4: 31-10.1186/1471-2164-4-31.PubMed CentralPubMedView ArticleGoogle Scholar
- Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA. 2004, 101 (25): 9309-9314. 10.1073/pnas.0401994101.PubMed CentralPubMedView ArticleGoogle Scholar
- Boyer LA, Lee TI, Cole MF, Johnstone SE, Levine SS, Zucker JP, Guenther MG, Kumar RM, Murray HL, Jenner RG: Core transcriptional regulatory circuitry in human embryonic stem cells. Cell. 2005, 122: 947-956. 10.1016/j.cell.2005.08.020.PubMed CentralPubMedView ArticleGoogle Scholar
- Futschik ME, Chaurasia G, Tschaut A, Russ J, Babu MM, Herzel H: Functional and transcriptional coherency of modules in the human protein interaction network. Journal of Integrative Bioinformatics. 2007, 4 (3): 76-10.2390/biecoll-jib-2007-76.Google Scholar
- Juric D, Sale S, Hromas RA, Yu R, Wang Y, Duran GE, Tibshirani R, Einhorn LH, Sikic BI: Gene expression profiling differentiates germ cell tumors from other cancers and defines subtype-specific signatures. Proc Natl Acad Sci USA. 2005, 102: 17763-17768. 10.1073/pnas.0509082102.PubMed CentralPubMedView ArticleGoogle Scholar
- Yanai I, Korbel JO, Boue S, McWeeney SK, Bork P, Lercher MJ: Similar gene expression profiles do not imply similar tissue functions. Trends in Genetics. 2006, 22: 132-138. 10.1016/j.tig.2006.01.006.PubMedView ArticleGoogle Scholar
- Young JI, Hong EP, Castle JC, Crespo-Barreto J, Bowman AB, Rose MF, Kang D, Richman R, Johnson JM, Berget S: Regulation of RNA splicing by the methylation-dependent transcriptional repressor methyl-CpG binding protein 2. Proc Natl Acad Sci USA. 2005, 102: 17551-17558. 10.1073/pnas.0507856102.PubMed CentralPubMedView ArticleGoogle Scholar
- Schadt EE, Edwards SW, GuhaThakurta D, Holder D, Ying L, Svetnik V, Leonardson A, Hart KW, Russell A, Li G: A comprehensive transcript index of the human genome generated using microarrays and computational approaches. Genome Biology. 2004, 5: R73-10.1186/gb-2004-5-10-r73.PubMed CentralPubMedView ArticleGoogle Scholar
- Shyamsundar R, Kim YH, Higgins JP, Montgomery K, Jorden M, Sethuraman A, Rijn van de M, Botstein D, Brown PO, Pollack JR: A DNA microarray survey of gene expression in normal human tissues. Genome Biology. 2005, 6: R22-10.1186/gb-2005-6-3-r22.PubMed CentralPubMedView ArticleGoogle Scholar
- Lee JK, Bussey KJ, Gwadry FG, Reinhold W, Riddick G, Pelletier SL, Nishizuka S, Szakacs G, Annereau J-P, Shankavaram U: Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells. Genome Biology. 2003, 4: R82-10.1186/gb-2003-4-12-r82.PubMed CentralPubMedView ArticleGoogle Scholar
- Brenner M, Johnson AB, Boespflug-Tanguy O, Rodriguez D, Goldman JE, Messing A: Mutations in GFAP, encoding glial fibrillary acidic protein, are associated with Alexander disease. Nature genetics. 2001, 27 (1): 117-120. 10.1038/83679.PubMedView ArticleGoogle Scholar
- Schmechel D, Marangos PJ, Zis AP, Brightman M, Goodwin FK: Brain endolases as specific markers of neuronal and glial cells. Science. 1978, 199 (4326): 313-315. 10.1126/science.339349.PubMedView ArticleGoogle Scholar
- Delanghe JR, Langlois MR: Hemopexin: a review of biological aspects and the role in laboratory medicine. Clinica chimica acta; international journal of clinical chemistry. 2001, 312 (1-2): 13-23. 10.1016/S0009-8981(01)00586-1.PubMedView ArticleGoogle Scholar
- Carter SL, Eklund AC, Mecham BH, Kohane IS, Szallasi Z: Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements. BMC bioinformatics. 2005, 6: 107-10.1186/1471-2105-6-107.PubMed CentralPubMedView ArticleGoogle Scholar
- Neerincx PB, Casel P, Prickett D, Nie H, Watson M, Leunissen JA, Groenen MA, Klopp C: Comparison of three microarray probe annotation pipelines: differences in strategies and their effect on downstream analysis. BMC proceedings. 2009, 3 (Suppl 4): S1-10.1186/1753-6561-3-s4-s1.PubMed CentralPubMedView ArticleGoogle Scholar
- Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J: Independence and reproducibility across microarray platforms. Nature methods. 2005, 2 (5): 337-344. 10.1038/nmeth757.PubMedView ArticleGoogle Scholar
- Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A: Reverse engineering of regulatory networks in human B cells. Nature genetics. 2005, 37 (4): 382-390. 10.1038/ng1532.PubMedView ArticleGoogle Scholar
- Chaurasia G, Malhotra S, Russ J, Schnoegl S, Hanig C, Wanker EE, Futschik ME: UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome. Nucleic Acids Res. 2009, D657-660. 10.1093/nar/gkn841. 37 DatabaseGoogle Scholar
- Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19 (2): 185-193. 10.1093/bioinformatics/19.2.185.PubMedView ArticleGoogle Scholar
- Futschik ME, Crompton T: Model selection and efficiency testing for normalization of cDNA microarray data. Genome Biology. 2004, 5: R60-10.1186/gb-2004-5-8-r60.PubMed CentralPubMedView ArticleGoogle Scholar
- Futschik ME, Crompton T: OLIN: optimized normalization, visualization and quality testing of two-channel microarray data. Bioinformatics. 2005, 21: 1724-1726. 10.1093/bioinformatics/bti199.PubMedView ArticleGoogle Scholar
- Ferrari F, Bortoluzzi S, Coppe A, Sirota A, Safran M, Shmoish M, Ferrari S, Lancet D, Danieli GA, Bicciato S: Novel definition files for human GeneChips based on GeneAnnot. BMC Bioinformatics. 2007, 8: 446-10.1186/1471-2105-8-446.PubMed CentralPubMedView ArticleGoogle Scholar
- Futschik ME, Herzel H: Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis. Bioinformatics. 2008, 24 (8): 1063-1069. 10.1093/bioinformatics/btn072.PubMedView ArticleGoogle Scholar
- Zhou X, Kao M-CJ, Wong WH: Transitive functional annotation by shortest path analysis of gene expression data. Proc Natl Acad Sci USA. 2002, 99: 12783-12788. 10.1073/pnas.192159399.PubMed CentralPubMedView ArticleGoogle Scholar
- Efron B, Tibshirani R: Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol. 2002, 23: 70-86. 10.1002/gepi.1124.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.