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Logo of bmcbioiBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Bioinformatics
BMC Bioinformatics. 2005; 6: 112.
Published online May 6, 2005. doi:  10.1186/1471-2105-6-112
PMCID: PMC1127109

Confirmation of human protein interaction data by human expression data



With microarray technology the expression of thousands of genes can be measured simultaneously. It is well known that the expression levels of genes of interacting proteins are correlated significantly more strongly in Saccharomyces cerevisiae than those of proteins that are not interacting. The objective of this work is to investigate whether this observation extends to the human genome.


We investigated the quantitative relationship between expression levels of genes encoding interacting proteins and genes encoding random protein pairs. Therefore we studied 1369 interacting human protein pairs and human gene expression levels of 155 arrays. We were able to establish a statistically significantly higher correlation between the expression levels of genes whose proteins interact compared to random protein pairs. Additionally we were able to provide evidence that genes encoding proteins belonging to the same GO-class show correlated expression levels.


This finding is concurrent with the naive hypothesis that the scales of production of interacting proteins are linked because an efficient interaction demands that involved proteins are available to some degree. The goal of further research in this field will be to understand the biological mechanisms behind this observation.


Gene expression data [1-3] and protein interaction data [4] are two types of data produced in the bioinformatics field. We investigated whether human gene expression levels of interacting protein pairs show a higher degree of dependence than those of random protein pairs. To date, such studies have only been performed in lower organisms like S. cerevisiae [5,6], in a comparative study using bacteriophage T7 and S. cerevisiae [7], and in C. elegans [8].

The first global evidence that genes with similar expression profiles are likely to encode interacting proteins has been provided in a study on S. cerevisiae by Ge et al. [5]. They compared the probability of interaction between proteins encoded by genes that belong to common expression profiling clusters with the probability of interaction between proteins encoded by genes that belong to different clusters. They found that proteins from the intra-group genes are more than five times as likely to interact with each other as proteins from the inter-group genes.

Tornow et al. [6] used superparamagnetic clustering to integrate protein interaction and expression data from independent experiments in S. cerevisiae and revealed hypothetical functional protein modules. Grigoriev [7] demonstrated the similarity of expression patterns for a pair of genes and interaction of the proteins they encode for both the bacteriophage T7 and in S. cerevisiae. He found the mean correlation coefficients of gene expression profiles between interacting proteins to be significantly higher than those between random protein pairs.

Recently Li et al. [8] analysed the transcriptome and interactome data of C. elegans and discovered that the correlation is lower than expected from observations in yeast.

A study by Jansen et al. [9] links gene expression on a genomic scale with protein-protein interaction in S. cerevisiae. They showed that while the subunits of the permanent protein complexes do indeed share significant correlation in their RNA expression, the correlated expression is relatively poor in detecting transient interactions. In a comprehensive study about S. cerevisiae conducted by Kemmeren et al. [10], up to 71% of the biologically verified interactions could be validated with the gene co-expression approach. Integration of expression and interaction data is thus a way to improve the confidence of protein-protein interaction data generated by high-throughput technologies. Kemmeren et al. [11] see enormous challenges in large genomes (orders of magnitude larger than S. cerevisiae) because of poor annotation, non-standardised gene names, and more complex interactions with the environment.


Expression levels of genes encoding interacting proteins are correlated more strongly

Using five publicly available human expression datasets (Table (Table1)1) and 1369 human interacting protein pairs we compared the correlation of expression of genes encoding interacting proteins (empirical distribution) with the correlation of random protein pairs (background distribution). Figure Figure11 shows that the distribution of empirical correlations is slightly shifted to the right compared to the distribution of correlations in the case of random protein pairs. This result implies that in our data interacting proteins are preferentially encoded by coregulated genes.

Figure 1
Empirical and background distribution of correlation values. For each interaction pair and for each dataset we calculated the correlation of expression levels of genes encoding interacting protein pairs. The graph shows slightly higher correlation values ...
Table 1
Information on expression datasets. The study includes a total of 155 arrays from five datasets. Each dataset has been published not earlier than 2003 and includes at least 20 arrays and 30 000 spots.

Using mutual information as a measure of dependence the shift observed in figure figure11 has almost vanished (figure (figure2).2). The difference between empirical and background distribution of the medians is 0.04 in the correlation case and <0.01 in the mutual information case. This observation suggests that correlation as a measure of dependence is more suitable than mutual information when analyzing dependencies between expression levels of interacting proteins. In the Methods section we give a possible interpretation of this observation.

Figure 2
Distribution of mutual information. For each interaction pair and for each dataset we calculated the mutual information of expression levels of genes encoding interacting protein pairs. The graph shows empirical and background distribution to be very ...

Using each dataset separately we tested the hypothesis that correlation between expression of genes encoding interacting protein pairs is not higher than correlation between expression of genes encoding random protein pairs. The detailed algorithms are given in the Methods section. For four out of five analysed datasets this hypothesis is rejected at a significance level α = 0.05. This means that in these four datasets the correlation of expression levels of genes which encode interacting proteins is statistically significantly higher than the correlation of expression levels in genes which encode random pairs of proteins.

Increased p-values by use of mutual information instead of correlation

Using mutual information instead of correlation as a measure of dependence between gene expression levels leads to increased p-values for each of the five datasets. Thus the significance results of the analysis with correlation as dependence measure do not hold when using mutual information as dependence measure. This may be caused by the fact that most dependencies between expression levels are linear or close to linear and not parabolic which would preferentially be discovered by the mutual information measure. Because the correlation coefficient seems to be the more appropriate measure of dependence for this analysis we do not discuss mutual information in the following.

Expression of genes involved in different biological processes

Assigning GO-classes to the 1369 protein interactions as described in the Methods section below, for each dataset we analysed the expression levels of genes encoding interacting protein pairs both belonging to the same GO-class. Each of the figures figures3,3, ,4,4, ,5,5, ,6,6, ,77 contains the box-and-whisker plots and the p-values of the twenty GO-classes that yield the most significant results for the respective dataset and of the GO-class biological process. Our method is different from the methods used by the authors that generated the datasets that we analysed. Thus our results cannot be compared directly with theirs. However, we feel that it is still useful to point out the similarity of some of our findings with the observations of these authors.

Figure 3
Correlations and p-values of the expression dataset from Chi. The diagram contains the contains the box-and-whisker plots and the p-values of the twenty GO-classes that yield the most significant results for the respective dataset and of the GO-class ...
Figure 4
Correlations and p-values of the expression dataset from Higgins. Analogous to figure 3
Figure 5
Correlations and p-values of the expression dataset from Pathan. Analogous to figure 3
Figure 6
Correlations and p-values of the expression dataset from Zhang. Analogous to figure 3
Figure 7
Correlations and p-values of the expression dataset from Zhao. Analogous to figure 3

In the following we refer to the different datasets by the number they have received in table table11.

Figure Figure33 shows that in dataset 1 mainly genes included in the GO-classes cell cycle, cell growth, and cell proliferation are highly correlated. All in all only few GO-classes show low p-values which, in principle, agrees with the observations of Chi et al. [12] that siRNA-mediated gene silencing leads to only small variations in the gene expression pattern.

In figure figure4,4, describing our results of the second expression dataset of Higgins et al. [13], many genes included in immune response and inflammatory response like interleukins, chemokine receptors, and chemokine ligands are highly correlated. This is an indication that the expression of chemokines and chemokine receptors is spatially and temporally restricted not only in the developing human kidney as reported in Gröne et al. [14] but also in the fully developed kidney.

Figure Figure55 shows our results of the GO-expression analysis of the third dataset. In this dataset Pathan et al. [15] found genes that are involved in bacterial infection to be significantly upregulated in blood after exposure to meningococci. We found the expression of genes that are involved in the response to (pathogenic) bacteria to be highly correlated which is in concurrence with the findings of Pathan et al. [15].

Zhang et al. [16] analysed the changes in transcript abundance occurring during senescence in human fibroblasts, as compared with early passage proliferating cells or quiescent cells. Figure Figure66 shows the results of our analysis of their expression dataset. In agreement with their findings we observed a strong correlation of genes that relate to apoptosis and genes that relate to transcription, but in contrast to them, we could not find significant correlation of genes that are involved in the cell cycle regulation.

Zhao et al. [17] analysed the effects of methylseleninic acid on the transcriptional program of human prostate cancer cells. Corresponding to their observation of decreased expression of genes involved in all phases of the cell cycle lines that do not express androgen receptor protein we found significant correlation of genes involved in M phase, nuclear division, and mitosis. Figure Figure77 displays the correlations and p-values of the top twenty GO-classes we calculated for the fifth dataset. In consensus with the expectations genes encoding proteins that are involved in the cell cycle show the lowest p-values in our analysis.


This study investigates the relationship between two biological phenomena – gene expression and protein-protein interaction in H. sapiens – based on experimental data available in public databases. The study was prompted by the fact that in yeast and other lower non-mammalian organisms correlation is observed between expression levels of genes encoding interacting proteins. We were able to obtain convincing evidence of correlation using the Pearson's correlation coefficient but could not confirm these results when taking the mutual information as a measure of dependence. Using information on the GO-class to which both proteins of an interacting protein pair belong, we were able to find significant correlations of expression levels mostly in accordance to existing knowledge.

The results of our investigation lend additional credibility to the protein-protein interaction data used.

Once more interaction data are available, an analysis of the type presented here should be repeated including information on domains and phenotypes. For instance, one of the remaining open questions is whether the correlation of expression vectors of genes encoding interacting proteins with certain compared to random combinations of domains is statistically significantly different.

Larger interaction datasets will also provide the opportunity to analyse the question if genes encoding interacting proteins are located on the same chromosome or even in close neighbourhood to each other more often than expected when assuming a random order. This problem has been addressed recently by Hurst et al. [18].


In this study we observed a statistically significant correlation between expression of genes encoding interacting proteins in H. sapiens. This finding points towards a biological mechanism which coregulates the expression of such genes. Additionally it confirms the relevance of using gene expression data and interaction data in human genome analysis.


Gene expression data

For our study we used public datasets from the Stanford Microarray Database (SMD) [19]. This database includes much actual expression data from the same (cDNA microarray) platform, which is an important prerequisite for a well-founded analysis [20]. Datasets were selected by the following criteria:

• At least 20 000 clones per array

• At least 20 arrays per dataset

• Equal sets of measured clones per dataset

• Publication not earlier than 2003.

The following datasets were included in our study: Chi et al. [12] (human kidney cells), Higgins et al. [13] (normal tissue of kidney), Pathan et al. [15] (infection of blood cells), Zhang et al. [16] (gene transcription occurring during replicative senescence in human fibroblasts and mammary epithelial cells), and Zhao et al. [17] (effects of methylseleninic acid on the transcriptional program of prostate cancer LNCaP (Lymph Node Carcinoma of the Prostate) cells). The number of arrays ranges from 21 to 42 and the number of measured clones from 31 736 to 43 196. As expression level we used the binary logarithm of the normalised ratio of gene signal (channel 2) and reference signal (channel 1).

Protein-protein interaction data

As protein interaction database we used DIP [21] listing protein pairs that are known to interact with each other, because DIP allows the user to select interactions based on their species of origin (e.g. human). Interaction here means that two amino acid chains were experimentally identified to bind to each other. In September 2004 the database comprised 1369 human protein-protein interaction pairs including 51 self-interactions. These self-interactions were excluded from the analysis because the corresponding gene expression levels (which are two identical vectors) always have correlation 1.

Matching gene and protein identifiers

In order to determine the expression levels of genes encoding proteins that interact we have to know which proteins are encoded by which genes. Thus we have to match gene identifiers with protein identifiers. Specifically, we matched UniGene cluster IDs [22] from the expression files of the SMD [19] with Swiss-Prot accession numbers [23] (e.g. sp:Q07812), with PIR accession numbers [24] (e.g. pir:A47538), and with NCBI sequence identification numbers [25] (e.g. gi:539664) of the DIP files. As 'translator' we used a file called 'Hs.data' from the NCBI website [26] which contains the mentioned identifiers and the corresponding UniGene cluster IDs. In order to limit runtime, we refrained from applying sophisticated selection methods [27] where multiple matching occurred, but considered the first hit at all times. By using this approach, for 87% of the interacting protein pairs the genes encoding these proteins can be determined. In cases where this procedure was not successful, we used information from the Harvester website [28]. By this combination of methods the proportion increases from 87% to 94%. For many of these protein interactions no expression data of the encoding genes are available. Depending on the number of genes measured in the five expression data sets for at least 43% (dataset 4) and for up to 72% (dataset 3) of the proteins the corresponding gene expression levels can be determined. For evaluating the amount of dependence between the expression levels of two genes encoding interacting proteins, the expression of both genes has to be measured. Disregarding self-interactions this is the case in at least 10% (dataset 4) and up to 47% (dataset 3) out of 1369 interactions.

Pearson's correlation coefficient

Let (X1, Y1), (X2, Y2),..., (Xn, Yn) be the n pairs of expression levels of two random variables X (expression of first protein) and Y (expression of second protein). We wish to measure the degree to which X and Y are linearly dependent as opposed to being independent. The correlation then is defined by

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Object name is 1471-2105-6-112-i1.gif

Mutual information

Mutual information measures the mutual dependence of two variables based on information theory.

Two random variables, X and Y, with probability distributions pX(x) and pY(y) and the joint distribution pXY(x, y) are statistically independent if

pXY(x, y) = pX(xpY(y).     (2)

The mutual information

An external file that holds a picture, illustration, etc.
Object name is 1471-2105-6-112-i2.gif

quantifies the degree of dependence of X and Y using the distance between the joint distribution and the distribution in case of total independence. The mutual information becomes large if X and Y contain the same information.

Calculating the degree of dependence between expression levels of genes encoding interacting proteins for all datasets

In our analysis we used expression vectors each containing the expression levels of one gene from all arrays of a dataset extracted from the SMD [19]. For each dataset we determined the correlation of vectors each containing the expression levels of genes encoding two interacting proteins. For each interaction the median of the resulting set of five (one for each dataset) correlation coefficients was calculated. We used a permutation approach (with 10 000 permutations) to compare the empirical correlation and mutual information with the corresponding background distributions. In each permutation step we held the expression levels X of one protein fixed and permuted the interaction partners encoded by genes with expression levels Y. Thus for each permutation we got a new interaction dataset with random protein pairs. For each of these datasets we calculated the correlation values and their median as before for the original dataset.

We repeated this procedure using mutual information as measure of dependence. The distributions of correlation and mutual information are shown in figure figure11 and in figure figure2,2, respectively.

Calculating p-values for each dataset

As before we determined the correlation of vectors for each dataset, each vector containing the expression levels of genes encoding two interacting proteins. We also calculated these values for the permuted datasets (1000 permutations). To get more specific results, we did not use the median of the correlations or mutual information values here but performed the permutation approach for each dataset separately.

Denote with nperm the number of permutations and with nhigh the number of correlation and mutual information values higher than those in the original dataset. Then the estimated p-values are given by

p = nhigh/nperm.     (5)

The corresponding p-values are shown in table table22.

Table 2
P-values using correlation and mutual information as measure of dependence. The p-values describe the probability of obtaining a higher correlation (third column) or mutual information (fourth column) than the one observed, assuming that the expression ...

GO analysis in each dataset

To further elucidate dependencies between expression levels in the five datasets we analysed for each dataset if genes encoding proteins within different GO-classes representing biological processes have correlated expression levels. Therefore, using QuickGO [29] we determined for each GO-class describing a biological process which of the 1369 interacting pairs include proteins, both of which belong to the respective biological process. We used these sets of interacting protein pairs to find biological processes that include protein pairs encoded by genes with highly correlated gene expression levels. By the use of a permutation test we compared the correlations of protein pairs belonging to a certain GO-class with the correlations of protein pairs not belonging to that GO-class. Analogous to the case without differentiation between GO-classes we can apply equation (5) again to get a p-value for each GO-class in each dataset (figure (figure3,3, ,4,4, ,5,5, ,6,6, ,7).7). We did not perform a correction procedure for multiple testing because the tested GO-classes often include very similar or even identical sets of interactions. Essential in this analysis is the ranking of the GO-classes.

Authors' contributions

AH developed and implemented the method, ran the calculations and prepared a draft of the paper. The other authors contributed to the development of the method, the interpretation of the results, and the refinement of the paper.


  • Brown PO, Botstein D. Exploring the new world of the genome with DNA microarrays. Nat Genet. 1999;21:33–7. doi: 10.1038/4462. [PubMed] [Cross Ref]
  • Lockhart DJ, Winzeler EA. Genomics, gene expression and DNA arrays. Nature. 2000;405:827–36. doi: 10.1038/35015701. [PubMed] [Cross Ref]
  • Young RA. Biomedical discovery with DNA arrays. Cell. 2000;102:9–15. doi: 10.1016/S0092-8674(00)00005-2. [PubMed] [Cross Ref]
  • Cho S, Park SG, Lee do H, Park BC. Protein-protein interaction networks: from interactions to networks. J Biochem Mol Biol. 2004;37:45–52. [PubMed]
  • Ge H, Liu Z, Church GM, Vidal M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet. 2001;29:482–6. doi: 10.1038/ng776. [PubMed] [Cross Ref]
  • Tornow S, Mewes HW. Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res. 2003;31:6283–9. doi: 10.1093/nar/gkg838. [PMC free article] [PubMed] [Cross Ref]
  • Grigoriev A. A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Res. 2001;29:3513–9. doi: 10.1093/nar/29.17.3513. [PMC free article] [PubMed] [Cross Ref]
  • Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T, Goldberg DS, Li N, Martinez M, Rual JF, Lamesch P, Xu L, Tewari M, Wong SL, Zhang LV, Berriz GF, Jacotot L, Vaglio P, Reboul J, Hirozane-Kishikawa T, Li Q, Gabel HW, Elewa A, Baumgartner B, Rose DJ, Yu H, Bosak S, Sequerra R, Fraser A, Mango SE, Saxton WM, Strome S, Van Den Heuvel S, Piano F, Vandenhaute J, Sardet C, Gerstein M, Doucette-Stamm L, Gunsalus KC, Harper JW, Cusick ME, Roth FP, Hill DE, Vidal M. A map of the interactome network of the metazoan C. elegans. Science. 2004;303:540–3. doi: 10.1126/science.1091403. [PMC free article] [PubMed] [Cross Ref]
  • Jansen R, Greenbaum D, Gerstein M. Relating whole-genome expression data with protein-protein interactions. Genome Res. 2002;12:37–46. doi: 10.1101/gr.205602. [PMC free article] [PubMed] [Cross Ref]
  • Kemmeren P, van Berkum NL, Vilo J, Bijma T, Donders R, Brazma A, Holstege FC. Protein interaction verification and functional annotation by integrated analysis of genome-scale data. Mol Cell. 2002;9:1133–43. doi: 10.1016/S1097-2765(02)00531-2. [PubMed] [Cross Ref]
  • Kemmeren P, Holstege FC. Integrating functional genomics data. Biochem Soc Trans. 2003;31:1484–7. [PubMed]
  • Chi JT, Chang HY, Wang NN, Chang DS, Dunphy N, Brown PO. Genomewide view of gene silencing by small interfering RNAs. Proc Natl Acad Sci U S A. 2003;100:6343–6. doi: 10.1073/pnas.1037853100. [PMC free article] [PubMed] [Cross Ref]
  • Higgins JP, Wang L, Kambham N, Montgomery K, Mason V, Vogelmann SU, Lemley KV, Brown PO, Brooks JD, van de Rijn M. Gene expression in the normal adult human kidney assessed by complementary DNA microarray. Mol Biol Cell. 2004;15:649–56. doi: 10.1091/mbc.E03-06-0432. [PMC free article] [PubMed] [Cross Ref]
  • Grone HJ, Cohen CD, Grone E, Schmidt C, Kretzler M, Schlondorff D, Nelson PJ. Spatial and temporally restricted expression of chemokines and chemokine receptors in the developing human kidney. J Am Soc Nephrol. 2002;13:957–67. [PubMed]
  • Pathan N, Hemingway CA, Alizadeh AA, Stephens AC, Boldrick JC, Oragui EE, McCabe C, Welch SB, Whitney A, O'Gara P, Nadel S, Relman DA, Harding SE, Levin M. Role of interleukin 6 in myocardial dysfunction of meningococcal septic shock. Lancet. 2004;363:203–9. doi: 10.1016/S0140-6736(03)15326-3. [PubMed] [Cross Ref]
  • Zhang H, Pan KH, Cohen SN. Senescence-specific gene expression fingerprints reveal cell-type-dependent physical clustering of up-regulated chromosomal loci. Proc Natl Acad Sci U S A. 2003;100:3251–6. doi: 10.1073/pnas.2627983100. [PMC free article] [PubMed] [Cross Ref]
  • Zhao H, Whitfield ML, Xu T, Botstein D, Brooks JD. Diverse effects of methylseleninic acid on the transcriptional program of human prostate cancer cells. Mol Biol Cell. 2004;15:506–19. doi: 10.1091/mbc.E03-07-0501. [PMC free article] [PubMed] [Cross Ref]
  • Hurst LD, Pal C, Lercher MJ. The evolutionary dynamics of eukaryotic gene order. Nat Rev Genet. 2004;5:299–310. doi: 10.1038/nrg1319. [PubMed] [Cross Ref]
  • Sherlock G, Hernandez-Boussard T, Kasarskis A, Binkley G, Matese JC, Dwight SS, Kaloper M, Weng S, Jin H, Ball CA, Eisen MB, Spellman PT. The Stanford Microarray Database. Nucleic Acids Res. 2001;29:152–5. doi: 10.1093/nar/29.1.152. 5 . [PMC free article] [PubMed] [Cross Ref]
  • Mah N, Thelin A, Lu T, Nikolaus S, Kuhbacher T, Gurbuz Y, Eickhoff H, Kloppel G, Lehrach H, Mellgard B, Costello CM, Schreiber S. A comparison of oligonucleotide and cDNA-based microarray systems. Physiol Genomics. 2004;16:361–70. doi: 10.1152/physiolgenomics.00080.2003. [PubMed] [Cross Ref]
  • Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004:D449–51. doi: 10.1093/nar/gkh086. [PMC free article] [PubMed] [Cross Ref]
  • UniGene http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene
  • The EMBL Nucleotide Sequence Database: User Manual Release http://www.ebi.ac.uk/embl/Documentation/User_manual/usrman.html#3.4.2
  • PIR FAQ http://pir.georgetown.edu/pirwww/dbinfo/faq.html
  • BioinformaticSequence Identifiers: GI number and Accession.Version http://www.ncbi.nlm.nih.gov/Sitemap/sequenceIDs.html
  • Index of ftp://ftp.ncbi.nih.gov/repository/UniGene/ ftp://ftp.ncbi.nih.gov/repository/UniGene/
  • Rahnenführer J, Domingues FS, Maydt J, Lengauer T. Calculating the statistical significance of changes in pathway activity from gene expression data. Stat Appl Genet Mol Biol. 2004;3 Article 16. [PubMed]
  • Bioinformatic Harvester EMBL Heidelberg http://harvester.embl.de/
  • QuickGO: GO Browser http://www.ebi.ac.uk/ego/

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