BACKGROUND
we performed a joint analysis of  <dig> matched array comparative genomics  and transcriptomics human cancer data sets; that is, experiments in which acgh and transcriptomics arrays have been run on the same samples.
we obtained inter-chromosomal acting regulatory relationships inferred from these data sets.
by regulatory relationship we mean either a direct relationship, of a transcription factor on its target gene, or a very indirect one, through a pathway containing many intermediate regulatory steps.
for tumour samples, acgh microarrays compare gene copy numbers in the dna extracted from the cells under investigation to the gene copy numbers in normal control cells, in order to detect gene deletions or gene amplifications .
typically, the dna is extracted from a tumour sample containing many cells, which may exhibit different alterations in copy number.
so for each gene the measured change in copy number is an average for all the cells in the sample and will, in general, be fractional rather than integer.
the gene expression experiments also utilise microarrays, but measure the abundance of mrna.
reviews of matched experiments, their analysis and uses can be found in huang et al.
<cit>  and lahti et al.
<cit> .
the way in which copy numbers of genes in cancer samples are often greatly disrupted works like a natural gene amplification/deletion experiment, so when transcriptomics data is also available for the same samples, then information can be gleaned on how changes in a gene’s copy number affects that gene’s expression.
in addition, but more challenging, the data can be used to investigate whether the change in a gene’s copy number, and concomitant change in expression, affects the expression of other genes, hence inferring regulatory relationships.
predicting regulatory relationships from this type of data faces multiple obstacles.
the signal is quite weak, due to the noise in the data, and the complexity of the biology involved presents a number of additional problems.
there can be a high degree of non-linearity in the relationship between copy number and expression, in fact gene amplification in some circumstances may actually reduce the expression of that gene  <cit> .
one cause of non-linearity in regulatory relationships is that amplification or deletion of genes can result in alternative regulatory pathways being activated.
a further problem for the analysis is that the results for a potential regulator can be confounded by coamplified/codeleted genes situated in the same region of the genome, and it is for this reason that we concentrate on inter-chromosomal acting regulatory relationships.
many genes that have altered copy number in one cancer type are found to have altered copy number in other cancer types  <cit> , so combining data sets from multiple cancer types should help reinforce any information within the data on regulator-target relationships.
the  <dig> data sets used for the inference are comprised of several types of human cancer and a total of  <dig> samples.
heterogeneity between cancer types does mitigate any improvement, however we have shown experimentally in goh et al.
<cit>  and computationally in newton & wernisch  <cit>  that there is useful signal on regulatory relationships within the data.
inferences from a meta-analysis of matched data sets were presented in newton & wernisch  <cit>  for a few potential regulators whereas the present analysis makes available the results for all potential regulators in the matched data sets.
the complete results of the meta-analysis are presented in the metamatched database .
methods
datatable  <dig> details of the  <dig> data sets used in the meta-analysis
n
p
a
a
b
b
geo = gene expression omnibus data set reference , n = number of samples, p = number of matched probes, ahttp://www.cangem.org/, b
http: //cbio.mskcc.org/public/lung_array_data/, cexpression data in arrayexpress : e-tabm- <dig>  e-mtab-161
the acgh data was location and scale normalized using the median and mad, as was the expression data.
the acgh and expression probes were mapped by the gene names of probes to give the maximum number of probes with corresponding acgh and expression profiles.
if necessary probe gene names were converted from synonyms to standard gene names using the database of the hugo gene nomenclature committee   <cit> .
if there was more than one probe for any gene name then the median value of the probes was taken to represent that gene name.
note that the acgh data was not thresholded so that, in general, fractional rather than integer acgh values were used in the analysis.
fractional variations in copy number occur because of the heterogeneity of the cancer samples being studied.
by using matched acgh and expression profiles we eliminated the effects of a sample’s heterogeneity considering that both sets of data were affected equally.
analysis
introduction
full details of the algorithm used and a diagram illustrating the steps involved in the analysis can be found in newton & wernisch  <cit>  and in goh et al.
<cit> , where the code, written in the r statistical environment  <cit> , can also be found.
here we provide a summary of the analysis methods used.
we use a relatively straight-forward method based on correlations which provides a robust method for analysing relationships amongst large amounts of data of unknown complexities.
more sophisticated network inference methods are generally much more susceptible to noise and heterogeneity between data sets.
the great strength of our simple approach is that it avoids the confounding that can occur when expression data alone is used in the analysis.
we define a ‘regulating gene’ as one whose up or down expression change has a direct or indirect effect on the up or down regulation of a ‘target gene’.
primary candidates for regulating genes are genes having significant correlated changes in their mrna expression levels following copy number alterations.
potential target genes of a regulating gene are those genes with significant correlation between the expression changes of the target gene and the acgh profile of the regulating gene.
we first describe the methods adopted for identifying genes worth investigating as potential regulators.
we then describe how we identify potential regulator-target relationships for these genes.
we use spearman correlation throughout the analysis.
identifying potential regulators
in order to identify genes worth investigating as potential regulators we focus on genes that have a high correlation between their copy number and their gene expression.
in the first instance,  <dig> spearman rank correlations , and their p-values for being greater than zero, were calculated for each gene .
these  <dig> correlation p-values were combined for each gene into a single p-value statistic using fisher’s method .
in order not to rely on any statistical assumptions we obtained a null distribution of combined p-value statistics through permutation of gene identifiers .
the resulting p-values for each gene were finally corrected for multiple testing by the benjamini-hochberg  method, to give a false discovery rate  for each gene based on its acgh/expression correlations in the  <dig> data sets.
in the following the benjamini-hochberg adjusted p-values are referred to as adjusted p-values and are now fdr values rather than p-values in the sense of a type i error.
we were also interested in how many, and which, of the  <dig> data sets indicated an acgh/expression correlation.
this was assessed for each of the genes using an arbitrary threshold of  <dig>  on a gene’s  <dig> correlation p-values .
to generate the null distribution, 5· <dig> permutations of gene identifiers were generated for each data set and the above procedure, using fisher’s method, for obtaining combined p-value statistics repeated.
in practice only a minority of genes are present in all  <dig> data sets.
in general a gene will be present in less than  <dig> data sets, hence we generated  <dig> null distributions for n combined p-values, n from  <dig> to  <dig>
altogether there were  <dig>  genes considered worth investigating as potential regulators , having significant correlation  between copy number profile and gene expression in at least one of the data sets.
predicting regulator-target relationships
after we had identified genes worth investigating as potential regulators we looked for potential target genes of these regulators.
expression changes of a potential target gene must correlate highly with its regulating gene’s acgh profile.
only inter-chromosomal acting relationships were investigated.
the correlation tests were similar to those in the previous section to find potential regulators but with three additions.
firstly, we tested separately the two alternative hypotheses: that the correlation of a regulator-target pair is greater than zero and that the correlation is less than zero, and we generated separate null distributions for the two conditions.
secondly, for each potential regulator only those data sets were included in the analysis for which that regulator had a significant self acgh/expression correlation.
thirdly, since we were only interested in inter-chromosomal acting relationships the null distributions were derived using potentially inter-chromosomal acting gene pairs.
just because a gene appears in a regulator’s list of predicted targets, does not mean that regulator is the most probable regulator for that target.
therefore, for each potential regulator, any predicted inter-chromosomal acted targets were removed if the data indicated an alternative, more probable gene as its regulator.
this procedure was found to be important, reducing the number of predicted targets in most cases.
there are however two different criteria that could be used to denote a gene as the most probable regulator of a target gene.
most obviously the lowest adjusted p-value from the meta-analysis could be used as the criterion.
however there are examples where the regulator with the lowest meta-analysis adjusted p-value only has a significant correlation with the target in one of the data sets, when there are other potential regulators that do have higher meta-analysis adjusted p-values, but have significant correlations with the target in more data sets.
in this analysis we provide results for both criteria for selecting the most probable regulator, the lowest adjusted p-value from the meta-analysis and the greatest number of data sets with significant correlations.
co-citations
co-citation analysis was performed using the bioconductor  <cit>  package org.hs.eg.db  <cit>   and functions from the package cocitestats  <cit> .
org.hs.eg.db links a gene identifier to pubmed identifiers  <cit>  for the papers in pubmed which cite the corresponding gene.
the analysis restricts the results to papers which contain fewer than  <dig> genes, in order to exclude papers that cite very many genes.
whilst the version  <dig> .
<dig> of org.hs.eg.db was used for the work presented in this paper the cocitation content of the database will be updated at each new release of org.hs.eg.db.
the r package igraph  <cit>  was used to generate network graphs showing how cocitations link genes in a target list.
gene ontology annotations
gene ontology   <cit>  annotations for gene lists comprising the regulator and its predicted target were found using the bioconductor packages gostats  <cit>  and go.db  <cit>  .
again the go annotations will be updated for each new release of go.db.
RESULTS
metamatched database
the complete results of the meta-analysis can be found in the metamatched database of inferred regulatory relationships, available at http://sysbio.mrc-bsu.cam.ac.uk/metamatched.
the database contains entries for  <dig>  potential regulatory genes .
the entry for a gene can be found through a search box on the main page.
one thousand four hundred thirty of the potential regulators have significant predicted targets , and these genes are listed as links on the main page.
additional file  <dig> summarises the results for the  <dig> regulators in a spreadsheet.
the website can also be used to access information in the database on  <dig>  genes as targets.
when a particular gene is searched for from the main page, links are provided to information on the gene as a regulator  and information on the gene as a target.
a web page for a gene as a regulator gives summary statistics for the gene, namely the number of predicted target genes activated and repressed by the regulator, the number of regulator-target pairs which have cocitations, the total number of papers which cocite at least two genes from a list containing the predicted targets and the regulator, and the statistical significance of finding this number of cocites.
the predicted target genes are listed on the web page and there are links to spreadsheets giving more detailed information on each target’s relationship with the regulator, for example, the number of datasets, and in which datasets, the relationship is significant.
the papers which cite both the regulator and a target are listed on the web page with their pubmed links.
the papers which cocite genes in the target list are given in a spreadsheet linked to from the web page.
if some of the target list are cocited then a network graph will be displayed showing how cocitations link the genes in the target list .
there is also a link to a page with go annotations of the target list.
further details of the database can be found in additional file  <dig>
consistent coamplification or codeletion of neighbouring potential regulators coupled with the inherent noise in the data can lead to ambiguity in the database as to which of the regulators is actually regulating a particular target gene.
so the results files contain columns giving the ‘best’ regulator in the database for each target based on the criterion of minimum p-value and the ‘best’ regulator in the database based on the criterion of significance in the most number of data sets.
in addition each gene has a web page containing information in the database on the gene as a target.
this page contains spreadsheets giving all the significant regulators of the target gene in question.
a web page for a gene as a target gives the predicted regulator of the target, for both activation and repression, and based on both the criterion of lowest adjusted p-value, and on the criterion of significance in the most number of data sets.
adjusted p-values are given for each.
primarily due to coamplification/codeletion of genes in the genomic region of the actual regulator there may be a number of significant predicted regulators, and the number of significant predicted regulators is also given on the web page.
details of all the significant predicted regulators for the target can be found in a spreadsheet linked to on the page.
the number of the significant predicted regulators cocited in at least one paper with this target is also given and the papers are listed on the web page.
statistical synopsis
number of targets
altogether  <dig> potential regulators had at least one predicted target.
this is out of  <dig>  genes which have significant self acgh/expression correlation in at least one data set.
there are a total of  <dig>  predicted regulator-target pairs in the database.
the number of predicted targets for a regulator ranged from  <dig> to  <dig>  the mean value being  <dig>
fig.
<dig> scatter plot showing, for the  <dig> regulators in the database, the number of predicted targets against the number of data sets the regulator shows significant self acgh/expression correlation.
plot annotated with some of the regulators with large numbers of targets and/or data sets: med <dig> - mediator complex interacts with dna-binding gene-specific transcription factors to modulate transcription, azin <dig> - inhibits antizyme-dependent ornithine decarboxylase degradation by binding to antizyme, ywhaz - adapter protein implicated in the regulation of a large spectrum of signaling pathways, mrps <dig> - mitochondrial ribosomal protein, pogz - zinc finger protein found to interact with the transcription factor sp <dig>  hsbp <dig> - overexpression represses the transactivation activity of hsf <dig>  pts - biosynthesis of tetrahydrobiopterin an essential cofactor and regulator of various enzyme activities, terf2ip - acts both as a regulator of telomere function and as a transcription regulator, zbtb <dig> - zinc finger and btb domain containing  <dig>  may be involved in transcriptional regulation, wrb - receptor, zcchc <dig> - zinc finger, cchc domain containing  <dig>  nucleic acid binding, cxcl <dig> - belongs to cytokine family which encode proteins involved in immunoregulatory processes, syt <dig> - possibly mediates calcium-dependent regulation of membrane trafficking in synaptic transmission, gnl3l - essential for ribosomal pre-rrna processing and cell proliferation, hoxd <dig> - belongs to the homeobox family of genes which encode transcription factors, mns <dig> - may play a role in the control of meiotic division
fig.
<dig> histograms of the frequency of the number of predicted targets for the regulators in the database, for activation and repression
target clusters
there is evidence that some of the predicted targets for some regulators form spatial clusters, that is, they are found in close proximity on a chromosome.
for each regulator in the data set, if the regulator had more than one predicted target located on a particular chromosome, we took the mid point of each target and recorded the distance between adjacent targets.
we repeated the analysis but replacing the predicted targets by the same number of genes selected at random from the same chromosome, and we repeated the randomisation analysis  <dig> times.
figure  <dig> shows the results.
all the recorded distances have been pooled and divided into  <dig>  mb bins and the number of entries in each bin counted.
the figure shows a boxplot of the counts in each bin from the  <dig> sets of randomised data, the boxplot whiskers marking the extreme values.
superimposed on the graph are the counts for each bin from the actual data.
the figure shows that there are more predicted targets located closer together than you would expect to find at random, suggesting that some of the targets are forming locational clusters.
fig.
<dig> plot showing the number of counts in  <dig>  mb bins when the genomic separation of target genes are analysed.
the boxplot shows the results for random selections of genes  and the dots mark the values obtained from the actual data
chromosomefig.
<dig> number of regulators in the database located on each chromosome  a number of regulators b density of regulators per mbase c number of regulators/number of known genes on chromosome
fig.
<dig> number of predicted targets in the database located on each chromosome  a number of targets b density of targets per mbase c number of targets/number of known genes on chromosome
cocitations
of the  <dig> regulators in the database  <dig> % have cocitations with at least one of their predicted targets.
of the  <dig>  predicted regulator-target pairs in the database  <dig> % have at least one cocitation;  <dig> cocitations being the maximum for any regulator-target pair.
the low percentage of regulator-target pairs that have co-citations probably reflects the current relative paucity of evidence for the function of genes, with only a small percentage of genes having direct experimental evidence for their function .
as well as looking for regulator-target cocitations we looked at cocitations between the genes in a list of targets predicted for a regulator.
for example pogz has cocitations with two of its predicted targets , but in addition, many of the  <dig> predicted targets are cocited together in papers.
the maximum number of targets cocited in one paper is  <dig> but there are many different combinations of targets cocited in a total of  <dig> papers so altogether  <dig> % of the  <dig> targets are cocited with at least one other target.
we ran a resampling to see whether this proportion of cocitations in a random list of genes of the same length could occur by chance and found it had a p-value of  <dig> .
for terf2ip, of the  <dig> predicted targets  <dig> % are cocited with at least one other target in at least one paper, with a p-value of  <dig> .
analysing the target lists of all regulators,  <dig>  of the  <dig> regulators had cocitations of some of their target list genes and  <dig> of these were significant .
figure  <dig> shows a scatter plot of target list cocitation adjusted p-values  for all the  <dig> regulators against the number of data sets in which the regulator shows significant self acgh/expression correlation .
the plot has been annotated with some of the regulators with the most significant cocited target lists and the largest number of data sets.
the database gives a network graph for each regulator showing how cocitations in the literature, where they exist, link together the genes in the target list .
the figure in additional file  <dig> shows an example for the regulator pogz.
fig.
<dig> scatter plot of target list cocitation adjusted p-values  for all the  <dig> regulators in the database against the number of data sets in which the regulator shows significant self acgh/expression correlation.
the plot has been annotated with some of the regulators with the most significant cocited target lists and the largest number of data sets
activation/repression
there is a marked difference in the number of regulators repressing targets compared with the number activating targets .
altogether  <dig> regulators only activate targets but  <dig> regulators only repress targets;  <dig> regulators both repress and activate targets.
there are in total  <dig> regulator-target pairs showing activation and  <dig>  regulator-target pairs showing repression in the database.
an extra  <dig> % on the number of regulator-target pairs demonstrating activation are demonstrating repression.
this suggests that inter-chromosomal acting regulatory relationships causing repression are more common than those causing activation.
intuitively this would seem plausible given the importance of feedback control in maintaining the stability of complex systems.
there is some evidence in the summary statistics from the database which suggests the effect is a true effect.
dividing the regulators into three groups, those which are predicted to activate and repress , those that are predicted to only activate  and those that are predicted only to repress  and examining the cocitations of the genes in the regulators’ target lists.
for the  <dig> regulators which are predicted to activate and repress,  <dig> have cocitations of their activation target list and  <dig> of their repression target list.
if a large number of the repression relationships were artefact then we would not expect similar numbers of lists to cocite.
for the  <dig> regulators predicted only to activate  <dig>  have cocitation of target lists compared to  <dig>  of the  <dig> regulators predicted only to repress.
again if many of the repression relationships were artefact then we would not expect similar percentages to have cocitations of target lists.
other aspects of the results also show a consistency between activation and repression, for example, analysing the data for target clusters, as described in a previous section, but analysing activation and repression targets separately, gives very similar results .
also examining the number of regulators in the activation and repression lists which are known to be transcription factors ,  <dig> % of the  <dig> regulators which purely activate are tfs and  <dig>  % of the  <dig> regulators which purely repress are tfs.
discussion and
CONCLUSIONS
we have predicted inter-chromosomal regulator-target relationships from  <dig> publicly available matched acgh/expression data sets for  <dig> potential regulators.
<dig> of these are known to be involved in transcription regulation, although our definition of regulatory relationship extends beyond the direct relationship of transcription factor on a target to encompass very indirect relationships, through a pathway containing many intermediate regulatory steps.
there is evidence that some of the targets of regulators are clustered by genomic location.
the cocitations found for many of the target lists lends support to the predictions contained in the database.
the most striking observation from the results is the difference in the number of relationships involving activation and repression, and we present statistics from the database that suggests this bias is a true effect.
it seems plausible that a complex dynamic system would require a preponderance of repression over activation in order to provide feedback control and maintain stability.
there are however a number of other possible reasons for the observation.
one potential cause of course is that it is an artefact of the analysis, however using spearman correlation, with separate positive and negative null distributions, should avoid the introduction of any bias.
another hypothesis would be that amplification of some genes may be causing a sufficient elevation in the concentration of their transcript to result in excessive non-specific binding to the genome, disrupting and reducing the expression of genes not normally regulated by these amplified genes.
we could also postulate that amplification of the dna of a gene may result in elevated expression of the gene’s mrna, but the sequence of the transcript is in some way deviant resulting in incorrect translation.
in this way genes, which appear to be amplified, with correlated increases in expression, would actually have reduced levels of their proteins to activate targets, thus giving the impression of repression.
furthermore the difference in the numbers of activation and repression relationships could reflect differences in how the cell responds to the genomic disruption commonly found in cancer, rather than representing actual differences in levels of activation and repression in a non-disrupted cell.
finally, due to noise and tissue heterogeneity, the meta-analysis is only picking out a fraction of all regulatory relationships, so there is the possibility that the activation/repression bias may only occur in these relatively strong and more ubiquitous relationships selected by the analysis.
the results are available in the metamatched database.
we anticipate that the predictions contained in the database should be useful in constructing networks of regulatory relationships, informing experiments and perhaps in helping to predict downstream effects of drugs on their targets.
it should be noted that although cancer data sets are being used, this analysis is unlikely to pick out oncogenes which are consistently amplified or deleted in cancer samples.
if a potential regulator is amplified or deleted in all the samples in a data set, and by the same amount, it probably will not have a high self acgh/expression correlation.
the genes with the highest self acgh/expression correlation will be those that show a wide variation of copy number between samples in any given data set, with concomitant changes in expression.
it is interesting to note that there are genes which do show this wide variation of copy number between samples in multiple data sets.
one gene, med <dig>  has significant self acgh/expression correlation in  <dig> datasets, and there are  <dig> regulators with significant self acgh/expression correlation in  <dig> or more data sets.
perhaps these genes are located in genomic regions which are prone to disruption in cancer cells, but this disruption occurs erratically.
another reason might be that they occur in genomic regions which are consistently disrupted, but in later stages of the cancer development, and the data sets contain samples from a range of stages.
if this is the case then some of these regulators may have an oncogenic role in later phases of the disease.
whereas self acgh/expression correlation can be consistent over many of the data sets, regulator-target correlations are significant in fewer data sets.
this is partly due to noise in the experiments, but also suggests the relationships are rather specific to tissue type and pathology, and can be obscured by biological phenomena such as pathway remodelling and epigenomic effects.
it is interesting to note that the regulator-target relationships identified in this study are likely to be gene regulatory relationships which are particularly susceptible to copy number disruption.
they are not relationships which are protected from such disruption by alternative pathways and other buffering mechanisms.
this may be important if any of these regulators do have an oncogenic role in later stages of cancer development.
consistent coamplification or codeletion of neighbouring regulators and noise in the data can lead to ambiguity in the results as to which of the regulators is regulating a particular target gene.
however in the database we do provide information beyond the best predictions, indicating what alternatives are suggested by the data.
the results are of course constrained by the probes available on the arrays used in the matched experiments, further relationships are likely, but hidden by the absence of appropriate probes on the arrays.
it is not impossible that some of the predicted relationships in this study have arisen through a confounding factor as part of one of the many little understood or as yet unknown genetic mechanisms.
for example there is now evidence that histone modification can promote copy number variation  <cit> .
if a histone modification was causing copy number variation in a regulator gene and the root cause of this histone modification was also affecting the expression of a second gene on a separate chromosome then this analysis might identify the second gene as an inter-chromosomal target of the regulator .
similarly there is some evidence from protozoa that copy number can be affected by rna-mediated epigenetic effects  <cit> , which suggests another potential route for confounding if this was found to occur in the human genome.
so as well as detecting inter-chromosomal relationships arising from direct interactions, or indirect ones through pathways, the analysis may be picking up far more complex and subtle genomic effects, which cannot be isolated with our current state of knowledge.
there are a number of possible avenues for further work that could be pursued with this data.
we chose to structure the meta-analysis to highlight gene relationships which are found in the maximum number of data sets.
alternative approaches emphasising sensitivity over specificity are possible and these could be assessed using target list cocitations.
similarly using pearson correlation might reveal relationships not evident using spearman correlation.
having established that there is useful signal in the data using a relatively simple but robust statistical approach it would be interesting to explore more complex methods such as maximal information-based nonparametric exploration statistics  <cit> , designed to cope with non-linearities in the data being analysed.
we plan to include further matched acgh/expression experiments as and when they become publicly available.
we will update the cocitations and go annotations at each release of the r packages org.hs.eg.db and go.db, and it will be interesting to see how cocitation support for the predictions changes with time.
additional files
additional file  <dig>
spreadsheet summarizing the results for all  <dig> regulators in the database with significant predicted targets.
additional file  <dig>
detailed description of metamatched database content.
additional file  <dig>
network representation of the cocitations of the target genes predicted for one regulator, pogz.
any two genes in the network are connected if they are cocited together in at least one publication.
competing interests
the authors declare that they have no competing interests.
authors’ contributions
the authors contributed equally to the publication.
both authors read and approved the final manuscript.

 we performed a joint analysis of matched array comparative genomics and transcriptomics human cancer data sets; that is, experiments in which acgh and transcriptomics arrays have been run on the same samples. we obtained inter-chromosomal acting regulatory relationships inferred from these data sets. by regulatory relationship we mean either a direct relationship, of a transcription factor on its target gene, or a very indirect one, through a pathway containing many intermediate regulatory steps. for tumour samples, acgh microarrays compare gene copy numbers in the dna extracted from the cells under investigation to the gene copy numbers in normal control cells, in order to detect gene deletions or gene amplifications. typically, the dna is extracted from a tumour sample containing many cells, which may exhibit different alterations in copy number. so for each gene the measured change in copy number is an average for all the cells in the sample and will, in general, be fractional rather than integer. the gene expression experiments also utilise microarrays, but measure the abundance of mrna. reviews of matched experiments, their analysis and uses can be found in huang et al. and lahti et al.. the way in which copy numbers of genes in cancer samples are often greatly disrupted works like a natural gene amplification/deletion experiment, so when transcriptomics data is also available for the same samples, then information can be gleaned on how changes in a gene’s copy number affects that gene’s expression. in addition, but more challenging, the data can be used to investigate whether the change in a gene’s copy number, and concomitant change in expression, affects the expression of other genes, hence inferring regulatory relationships. predicting regulatory relationships from this type of data faces multiple obstacles. the signal is quite weak, due to the noise in the data, and the complexity of the biology involved presents a number of additional problems. there can be a high degree of non-linearity in the relationship between copy number and expression, in fact gene amplification in some circumstances may actually reduce the expression of that gene. one cause of non-linearity in regulatory relationships is that amplification or deletion of genes can result in alternative regulatory pathways being activated. a further problem for the analysis is that the results for a potential regulator can be confounded by coamplified/codeleted genes situated in the same region of the genome, and it is for this reason that we concentrate on inter-chromosomal acting regulatory relationships. many genes that have altered copy number in one cancer type are found to have altered copy number in other cancer types, so combining data sets from multiple cancer types should help reinforce any information within the data on regulator-target relationships. the data sets used for the inference are comprised of several types of human cancer and a total of samples. heterogeneity between cancer types does mitigate any improvement, however we have shown experimentally in goh et al. and computationally in newton & wernisch that there is useful signal on regulatory relationships within the data. inferences from a meta-analysis of matched data sets were presented in newton & wernisch for a few potential regulators whereas the present analysis makes available the results for all potential regulators in the matched data sets. the complete results of the meta-analysis are presented in the metamatched database. methods datatable details of the data sets used in the meta-analysis n p a a b b geo = gene expression omnibus data set reference, n = number of samples, p = number of matched probes, ahttp://www.cangem.org/, b http: //cbio.mskcc.org/public/lung_array_data/, cexpression data in arrayexpress: e-tabm- e-mtab-161 the acgh data was location and scale normalized using the median and mad, as was the expression data. the acgh and expression probes were mapped by the gene names of probes to give the maximum number of probes with corresponding acgh and expression profiles. if necessary probe gene names were converted from synonyms to standard gene names using the database of the hugo gene nomenclature committee. if there was more than one probe for any gene name then the median value of the probes was taken to represent that gene name. note that the acgh data was not thresholded so that, in general, fractional rather than integer acgh values were used in the analysis. fractional variations in copy number occur because of the heterogeneity of the cancer samples being studied. by using matched acgh and expression profiles we eliminated the effects of a sample’s heterogeneity considering that both sets of data were affected equally. analysis introduction full details of the algorithm used and a diagram illustrating the steps involved in the analysis can be found in newton & wernisch and in goh et al., where the code, written in the r statistical environment, can also be found. here we provide a summary of the analysis methods used. we use a relatively straight-forward method based on correlations which provides a robust method for analysing relationships amongst large amounts of data of unknown complexities. more sophisticated network inference methods are generally much more susceptible to noise and heterogeneity between data sets. the great strength of our simple approach is that it avoids the confounding that can occur when expression data alone is used in the analysis. we define a ‘regulating gene’ as one whose up or down expression change has a direct or indirect effect on the up or down regulation of a ‘target gene’. primary candidates for regulating genes are genes having significant correlated changes in their mrna expression levels following copy number alterations. potential target genes of a regulating gene are those genes with significant correlation between the expression changes of the target gene and the acgh profile of the regulating gene. we first describe the methods adopted for identifying genes worth investigating as potential regulators. we then describe how we identify potential regulator-target relationships for these genes. we use spearman correlation throughout the analysis. identifying potential regulators in order to identify genes worth investigating as potential regulators we focus on genes that have a high correlation between their copy number and their gene expression. in the first instance, spearman rank correlations, and their p-values for being greater than zero, were calculated for each gene. these correlation p-values were combined for each gene into a single p-value statistic using fisher’s method. in order not to rely on any statistical assumptions we obtained a null distribution of combined p-value statistics through permutation of gene identifiers. the resulting p-values for each gene were finally corrected for multiple testing by the benjamini-hochberg method, to give a false discovery rate for each gene based on its acgh/expression correlations in the data sets. in the following the benjamini-hochberg adjusted p-values are referred to as adjusted p-values and are now fdr values rather than p-values in the sense of a type i error. we were also interested in how many, and which, of the data sets indicated an acgh/expression correlation. this was assessed for each of the genes using an arbitrary threshold of on a gene’s correlation p-values. to generate the null distribution, 5· permutations of gene identifiers were generated for each data set and the above procedure, using fisher’s method, for obtaining combined p-value statistics repeated. in practice only a minority of genes are present in all data sets. in general a gene will be present in less than data sets, hence we generated null distributions for n combined p-values, n from to altogether there were genes considered worth investigating as potential regulators, having significant correlation between copy number profile and gene expression in at least one of the data sets. predicting regulator-target relationships after we had identified genes worth investigating as potential regulators we looked for potential target genes of these regulators. expression changes of a potential target gene must correlate highly with its regulating gene’s acgh profile. only inter-chromosomal acting relationships were investigated. the correlation tests were similar to those in the previous section to find potential regulators but with three additions. firstly, we tested separately the two alternative hypotheses: that the correlation of a regulator-target pair is greater than zero and that the correlation is less than zero, and we generated separate null distributions for the two conditions. secondly, for each potential regulator only those data sets were included in the analysis for which that regulator had a significant self acgh/expression correlation. thirdly, since we were only interested in inter-chromosomal acting relationships the null distributions were derived using potentially inter-chromosomal acting gene pairs. just because a gene appears in a regulator’s list of predicted targets, does not mean that regulator is the most probable regulator for that target. therefore, for each potential regulator, any predicted inter-chromosomal acted targets were removed if the data indicated an alternative, more probable gene as its regulator. this procedure was found to be important, reducing the number of predicted targets in most cases. there are however two different criteria that could be used to denote a gene as the most probable regulator of a target gene. most obviously the lowest adjusted p-value from the meta-analysis could be used as the criterion. however there are examples where the regulator with the lowest meta-analysis adjusted p-value only has a significant correlation with the target in one of the data sets, when there are other potential regulators that do have higher meta-analysis adjusted p-values, but have significant correlations with the target in more data sets. in this analysis we provide results for both criteria for selecting the most probable regulator, the lowest adjusted p-value from the meta-analysis and the greatest number of data sets with significant correlations. co-citations co-citation analysis was performed using the bioconductor package org.hs.eg.db and functions from the package cocitestats. org.hs.eg.db links a gene identifier to pubmed identifiers for the papers in pubmed which cite the corresponding gene. the analysis restricts the results to papers which contain fewer than genes, in order to exclude papers that cite very many genes. whilst the version. of org.hs.eg.db was used for the work presented in this paper the cocitation content of the database will be updated at each new release of org.hs.eg.db. the r package igraph was used to generate network graphs showing how cocitations link genes in a target list. gene ontology annotations gene ontology annotations for gene lists comprising the regulator and its predicted target were found using the bioconductor packages gostats and go.db. again the go annotations will be updated for each new release of go.db.  metamatched database the complete results of the meta-analysis can be found in the metamatched database of inferred regulatory relationships, available at http://sysbio.mrc-bsu.cam.ac.uk/metamatched. the database contains entries for potential regulatory genes. the entry for a gene can be found through a search box on the main page. one thousand four hundred thirty of the potential regulators have significant predicted targets, and these genes are listed as links on the main page. additional file summarises the results for the regulators in a spreadsheet. the website can also be used to access information in the database on genes as targets. when a particular gene is searched for from the main page, links are provided to information on the gene as a regulator and information on the gene as a target. a web page for a gene as a regulator gives summary statistics for the gene, namely the number of predicted target genes activated and repressed by the regulator, the number of regulator-target pairs which have cocitations, the total number of papers which cocite at least two genes from a list containing the predicted targets and the regulator, and the statistical significance of finding this number of cocites. the predicted target genes are listed on the web page and there are links to spreadsheets giving more detailed information on each target’s relationship with the regulator, for example, the number of datasets, and in which datasets, the relationship is significant. the papers which cite both the regulator and a target are listed on the web page with their pubmed links. the papers which cocite genes in the target list are given in a spreadsheet linked to from the web page. if some of the target list are cocited then a network graph will be displayed showing how cocitations link the genes in the target list. there is also a link to a page with go annotations of the target list. further details of the database can be found in additional file consistent coamplification or codeletion of neighbouring potential regulators coupled with the inherent noise in the data can lead to ambiguity in the database as to which of the regulators is actually regulating a particular target gene. so the results files contain columns giving the ‘best’ regulator in the database for each target based on the criterion of minimum p-value and the ‘best’ regulator in the database based on the criterion of significance in the most number of data sets. in addition each gene has a web page containing information in the database on the gene as a target. this page contains spreadsheets giving all the significant regulators of the target gene in question. a web page for a gene as a target gives the predicted regulator of the target, for both activation and repression, and based on both the criterion of lowest adjusted p-value, and on the criterion of significance in the most number of data sets. adjusted p-values are given for each. primarily due to coamplification/codeletion of genes in the genomic region of the actual regulator there may be a number of significant predicted regulators, and the number of significant predicted regulators is also given on the web page. details of all the significant predicted regulators for the target can be found in a spreadsheet linked to on the page. the number of the significant predicted regulators cocited in at least one paper with this target is also given and the papers are listed on the web page. statistical synopsis number of targets altogether potential regulators had at least one predicted target. this is out of genes which have significant self acgh/expression correlation in at least one data set. there are a total of predicted regulator-target pairs in the database. the number of predicted targets for a regulator ranged from to the mean value being fig. scatter plot showing, for the regulators in the database, the number of predicted targets against the number of data sets the regulator shows significant self acgh/expression correlation. plot annotated with some of the regulators with large numbers of targets and/or data sets: med - mediator complex interacts with dna-binding gene-specific transcription factors to modulate transcription, azin - inhibits antizyme-dependent ornithine decarboxylase degradation by binding to antizyme, ywhaz - adapter protein implicated in the regulation of a large spectrum of signaling pathways, mrps - mitochondrial ribosomal protein, pogz - zinc finger protein found to interact with the transcription factor sp hsbp - overexpression represses the transactivation activity of hsf pts - biosynthesis of tetrahydrobiopterin an essential cofactor and regulator of various enzyme activities, terf2ip - acts both as a regulator of telomere function and as a transcription regulator, zbtb - zinc finger and btb domain containing may be involved in transcriptional regulation, wrb - receptor, zcchc - zinc finger, cchc domain containing nucleic acid binding, cxcl - belongs to cytokine family which encode proteins involved in immunoregulatory processes, syt - possibly mediates calcium-dependent regulation of membrane trafficking in synaptic transmission, gnl3l - essential for ribosomal pre-rrna processing and cell proliferation, hoxd - belongs to the homeobox family of genes which encode transcription factors, mns - may play a role in the control of meiotic division fig. histograms of the frequency of the number of predicted targets for the regulators in the database, for activation and repression target clusters there is evidence that some of the predicted targets for some regulators form spatial clusters, that is, they are found in close proximity on a chromosome. for each regulator in the data set, if the regulator had more than one predicted target located on a particular chromosome, we took the mid point of each target and recorded the distance between adjacent targets. we repeated the analysis but replacing the predicted targets by the same number of genes selected at random from the same chromosome, and we repeated the randomisation analysis times. figure shows the results. all the recorded distances have been pooled and divided into mb bins and the number of entries in each bin counted. the figure shows a boxplot of the counts in each bin from the sets of randomised data, the boxplot whiskers marking the extreme values. superimposed on the graph are the counts for each bin from the actual data. the figure shows that there are more predicted targets located closer together than you would expect to find at random, suggesting that some of the targets are forming locational clusters. fig. plot showing the number of counts in mb bins when the genomic separation of target genes are analysed. the boxplot shows the results for random selections of genes and the dots mark the values obtained from the actual data chromosomefig. number of regulators in the database located on each chromosome a number of regulators b density of regulators per mbase c number of regulators/number of known genes on chromosome fig. number of predicted targets in the database located on each chromosome a number of targets b density of targets per mbase c number of targets/number of known genes on chromosome cocitations of the regulators in the database % have cocitations with at least one of their predicted targets. of the predicted regulator-target pairs in the database % have at least one cocitation; cocitations being the maximum for any regulator-target pair. the low percentage of regulator-target pairs that have co-citations probably reflects the current relative paucity of evidence for the function of genes, with only a small percentage of genes having direct experimental evidence for their function. as well as looking for regulator-target cocitations we looked at cocitations between the genes in a list of targets predicted for a regulator. for example pogz has cocitations with two of its predicted targets, but in addition, many of the predicted targets are cocited together in papers. the maximum number of targets cocited in one paper is but there are many different combinations of targets cocited in a total of papers so altogether % of the targets are cocited with at least one other target. we ran a resampling to see whether this proportion of cocitations in a random list of genes of the same length could occur by chance and found it had a p-value of. for terf2ip, of the predicted targets % are cocited with at least one other target in at least one paper, with a p-value of. analysing the target lists of all regulators, of the regulators had cocitations of some of their target list genes and of these were significant. figure shows a scatter plot of target list cocitation adjusted p-values for all the regulators against the number of data sets in which the regulator shows significant self acgh/expression correlation. the plot has been annotated with some of the regulators with the most significant cocited target lists and the largest number of data sets. the database gives a network graph for each regulator showing how cocitations in the literature, where they exist, link together the genes in the target list. the figure in additional file shows an example for the regulator pogz. fig. scatter plot of target list cocitation adjusted p-values for all the regulators in the database against the number of data sets in which the regulator shows significant self acgh/expression correlation. the plot has been annotated with some of the regulators with the most significant cocited target lists and the largest number of data sets activation/repression there is a marked difference in the number of regulators repressing targets compared with the number activating targets. altogether regulators only activate targets but regulators only repress targets; regulators both repress and activate targets. there are in total regulator-target pairs showing activation and regulator-target pairs showing repression in the database. an extra % on the number of regulator-target pairs demonstrating activation are demonstrating repression. this suggests that inter-chromosomal acting regulatory relationships causing repression are more common than those causing activation. intuitively this would seem plausible given the importance of feedback control in maintaining the stability of complex systems. there is some evidence in the summary statistics from the database which suggests the effect is a true effect. dividing the regulators into three groups, those which are predicted to activate and repress, those that are predicted to only activate and those that are predicted only to repress and examining the cocitations of the genes in the regulators’ target lists. for the regulators which are predicted to activate and repress, have cocitations of their activation target list and of their repression target list. if a large number of the repression relationships were artefact then we would not expect similar numbers of lists to cocite. for the regulators predicted only to activate have cocitation of target lists compared to of the regulators predicted only to repress. again if many of the repression relationships were artefact then we would not expect similar percentages to have cocitations of target lists. other aspects of the results also show a consistency between activation and repression, for example, analysing the data for target clusters, as described in a previous section, but analysing activation and repression targets separately, gives very similar results. also examining the number of regulators in the activation and repression lists which are known to be transcription factors, % of the regulators which purely activate are tfs and % of the regulators which purely repress are tfs. discussion and  we have predicted inter-chromosomal regulator-target relationships from publicly available matched acgh/expression data sets for potential regulators. of these are known to be involved in transcription regulation, although our definition of regulatory relationship extends beyond the direct relationship of transcription factor on a target to encompass very indirect relationships, through a pathway containing many intermediate regulatory steps. there is evidence that some of the targets of regulators are clustered by genomic location. the cocitations found for many of the target lists lends support to the predictions contained in the database. the most striking observation from the results is the difference in the number of relationships involving activation and repression, and we present statistics from the database that suggests this bias is a true effect. it seems plausible that a complex dynamic system would require a preponderance of repression over activation in order to provide feedback control and maintain stability. there are however a number of other possible reasons for the observation. one potential cause of course is that it is an artefact of the analysis, however using spearman correlation, with separate positive and negative null distributions, should avoid the introduction of any bias. another hypothesis would be that amplification of some genes may be causing a sufficient elevation in the concentration of their transcript to result in excessive non-specific binding to the genome, disrupting and reducing the expression of genes not normally regulated by these amplified genes. we could also postulate that amplification of the dna of a gene may result in elevated expression of the gene’s mrna, but the sequence of the transcript is in some way deviant resulting in incorrect translation. in this way genes, which appear to be amplified, with correlated increases in expression, would actually have reduced levels of their proteins to activate targets, thus giving the impression of repression. furthermore the difference in the numbers of activation and repression relationships could reflect differences in how the cell responds to the genomic disruption commonly found in cancer, rather than representing actual differences in levels of activation and repression in a non-disrupted cell. finally, due to noise and tissue heterogeneity, the meta-analysis is only picking out a fraction of all regulatory relationships, so there is the possibility that the activation/repression bias may only occur in these relatively strong and more ubiquitous relationships selected by the analysis. the results are available in the metamatched database. we anticipate that the predictions contained in the database should be useful in constructing networks of regulatory relationships, informing experiments and perhaps in helping to predict downstream effects of drugs on their targets. it should be noted that although cancer data sets are being used, this analysis is unlikely to pick out oncogenes which are consistently amplified or deleted in cancer samples. if a potential regulator is amplified or deleted in all the samples in a data set, and by the same amount, it probably will not have a high self acgh/expression correlation. the genes with the highest self acgh/expression correlation will be those that show a wide variation of copy number between samples in any given data set, with concomitant changes in expression. it is interesting to note that there are genes which do show this wide variation of copy number between samples in multiple data sets. one gene, med has significant self acgh/expression correlation in datasets, and there are regulators with significant self acgh/expression correlation in or more data sets. perhaps these genes are located in genomic regions which are prone to disruption in cancer cells, but this disruption occurs erratically. another reason might be that they occur in genomic regions which are consistently disrupted, but in later stages of the cancer development, and the data sets contain samples from a range of stages. if this is the case then some of these regulators may have an oncogenic role in later phases of the disease. whereas self acgh/expression correlation can be consistent over many of the data sets, regulator-target correlations are significant in fewer data sets. this is partly due to noise in the experiments, but also suggests the relationships are rather specific to tissue type and pathology, and can be obscured by biological phenomena such as pathway remodelling and epigenomic effects. it is interesting to note that the regulator-target relationships identified in this study are likely to be gene regulatory relationships which are particularly susceptible to copy number disruption. they are not relationships which are protected from such disruption by alternative pathways and other buffering mechanisms. this may be important if any of these regulators do have an oncogenic role in later stages of cancer development. consistent coamplification or codeletion of neighbouring regulators and noise in the data can lead to ambiguity in the results as to which of the regulators is regulating a particular target gene. however in the database we do provide information beyond the best predictions, indicating what alternatives are suggested by the data. the results are of course constrained by the probes available on the arrays used in the matched experiments, further relationships are likely, but hidden by the absence of appropriate probes on the arrays. it is not impossible that some of the predicted relationships in this study have arisen through a confounding factor as part of one of the many little understood or as yet unknown genetic mechanisms. for example there is now evidence that histone modification can promote copy number variation. if a histone modification was causing copy number variation in a regulator gene and the root cause of this histone modification was also affecting the expression of a second gene on a separate chromosome then this analysis might identify the second gene as an inter-chromosomal target of the regulator. similarly there is some evidence from protozoa that copy number can be affected by rna-mediated epigenetic effects, which suggests another potential route for confounding if this was found to occur in the human genome. so as well as detecting inter-chromosomal relationships arising from direct interactions, or indirect ones through pathways, the analysis may be picking up far more complex and subtle genomic effects, which cannot be isolated with our current state of knowledge. there are a number of possible avenues for further work that could be pursued with this data. we chose to structure the meta-analysis to highlight gene relationships which are found in the maximum number of data sets. alternative approaches emphasising sensitivity over specificity are possible and these could be assessed using target list cocitations. similarly using pearson correlation might reveal relationships not evident using spearman correlation. having established that there is useful signal in the data using a relatively simple but robust statistical approach it would be interesting to explore more complex methods such as maximal information-based nonparametric exploration statistics, designed to cope with non-linearities in the data being analysed. we plan to include further matched acgh/expression experiments as and when they become publicly available. we will update the cocitations and go annotations at each release of the r packages org.hs.eg.db and go.db, and it will be interesting to see how cocitation support for the predictions changes with time. additional files additional file spreadsheet summarizing the results for all regulators in the database with significant predicted targets. additional file detailed description of metamatched database content. additional file network representation of the cocitations of the target genes predicted for one regulator, pogz. any two genes in the network are connected if they are cocited together in at least one publication. competing interests the authors declare that they have no competing interests. authors’ contributions the authors contributed equally to the publication. both authors read and approved the final manuscript.
the way in which copy numbers of genes in cancer samples are often greatly disrupted works like a natural gene amplification/deletion experiment. the regulatory relationships being inferred are either direct relationships, of a transcription factor on its target, or indirect ones, through pathways containing intermediate steps. gene regulatory relationships can be inferred using matched array comparative genomics and transcriptomics data sets from cancer samples. we infer inter-chromosomal acting gene regulatory relationships from a meta-analysis of publicly available matched array comparative genomics and transcriptomics data sets in humans. we analyse the predictions in terms of cocitations, both publications which cite a regulator with any of its inferred targets and cocitations of any genes in a target list. we obtained statistically significant predictions of target genes for potential regulatory genes. the most striking observation from the results is the greater number of inter-chromosomal regulatory relationships involving repression compared to those involving activation. the complete results of the meta-analysis are presented in the database metamatched. there are now a large number of such data sets publicly available making a meta-analysis of the data possible. we anticipate that the predictions contained in the database will be useful in informing experiments and in helping to construct networks of regulatory relationships.
 gene regulatory relationships can be inferred using matched array comparative genomics and transcriptomics data sets from cancer samples. the way in which copy numbers of genes in cancer samples are often greatly disrupted works like a natural gene amplification/deletion experiment. there are now a large number of such data sets publicly available making a meta-analysis of the data possible.  we infer inter-chromosomal acting gene regulatory relationships from a meta-analysis of publicly available matched array comparative genomics and transcriptomics data sets in humans. we obtained statistically significant predictions of target genes for potential regulatory genes. the regulatory relationships being inferred are either direct relationships, of a transcription factor on its target, or indirect ones, through pathways containing intermediate steps. we analyse the predictions in terms of cocitations, both publications which cite a regulator with any of its inferred targets and cocitations of any genes in a target list.  the most striking observation from the results is the greater number of inter-chromosomal regulatory relationships involving repression compared to those involving activation. the complete results of the meta-analysis are presented in the database metamatched. we anticipate that the predictions contained in the database will be useful in informing experiments and in helping to construct networks of regulatory relationships. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords gene regulatory relationshipacghtranscriptomicscancermeta-analysisactivationrepressioncocitationsissue-copyright-statement© the author 2015 