BACKGROUND
a number of studies have considered whether changes in mrna concentration are reflected by similar changes in protein abundance e.g.
<cit> .
typically, poor correspondence has been reported between transcript and protein levels, and in some cases, little or no correlation at all e.g.
<cit> .
given the complexities of protein expression, and the many stages at which it may be influenced and/or actively controlled, this is perhaps to be expected.
analogies can be drawn with studies that aim to compare microarray data generated using different platforms.
these have highlighted the importance of filtering to remove data with poor signal to noise ratio, and the need to restrict comparisons to those pairs of reporters that target the same transcript location  <cit> .
transcript location is particularly important because approximately 50% of all human genes are predicted to be alternately spliced  <cit> .
in many cases, different exons from the same gene will be represented in the spliced mrna complement of a cell at different abundances.
most microarrays  typically offer only a single reporter per gene, and target locations differ between platforms.
cross mappings have generally been performed simply by seeking reporters annotated to the same gene identifier.
the alternative, of restricting analyses only to those reporter-pairs that target the same transcript location, results in a significant proportion of the data being lost.
a new generation of arrays have been developed with sufficient feature density to target every known and predicted exon in the human, mouse or rat genomes  <cit> .
these 'exon' arrays offer a choice of probeset location when performing comparative studies, and allow cross-mapping to be performed with high precision, while still preserving much of the gene-level array data  <cit> .
numerous studies have also observed that differences in, for example, sample preparation, hybridization conditions and binding affinities can all have a significant influence on measured expression level.
this makes direct comparisons between raw intensities challenging, although good correspondence is reported when comparisons are made using ratio data, e.g.
<cit> .
we investigated whether similar considerations could be applied to quantitative mass spectrometry proteomics data generated using the itraq reagent system  <cit> .
itraq  is an extension of other approaches to protein identification , in which a protein sample is fragmented and then separated into distinct peptides by liquid chromatography .
peptides are then analysed using mass spectrometry to further fragment them and measure the mass to charge ratio for each resultant ion.
in silico comparison of the resulting ion signature against a database generated from known protein sequences is then used to identify each peptide, and thus their originating proteins.
itraq, like other similar approaches such as icat and silac  <cit> , adds an additional labelling step following the initial fragmentation in which a chemical tag is attached to each peptide.
the aim is to label every peptide in an experimental sample with a molecule of known mass  and use the relative abundance of these reporter groups to determine the relative abundance of each peptide across samples.
the mass spectrometry system is set up such that each  peptide is analysed separately.
multiple samples are compared in a single ms run, each labelled with a different sized reporter group.
in addition, each tag contains a balance group, designed such that the labels used for each sample have the same overall mass.
this has the consequence that all instances of a given peptide are processed at the same time by the mass spectrometer, irrespective of the experimental sample from which they originated, and has been shown to lead to improved accuracy  <cit> .
when the labelled peptide is processed in the mass spectrometer it fragments, resulting in a series of ions corresponding to the fragmented peptide  and an additional set of peaks corresponding to the reporter tags for each sample.
by determining the relative intensities of these reporter peaks, the relative abundance of a given peptide can be compared between samples.
since the major focus of this paper is to consider whether the increased precision offered by exon arrays can be used in combination with proteomics data in order to improve the quality of the mappings between the two data sources, a biological system was chosen in which relatively high correlation between protein and transcript data might be expected, thus making it possible to establish a baseline level of correlation in a well-controlled system.
in previous work using cell-lines in equilibrium, for example  <cit> , relatively high correspondence  has been reported.
here, two cell lines in steady state were also chosen.
they provide not only a ready source of high quality rna and protein , but also a system in dynamic equilibrium in which a major source of biological variation is removed.
the aim was to generate a well-controlled dataset with which to investigate and evaluate different data-integration approaches.
RESULTS
protein and mrna samples were extracted from the human breast cancer cell line mcf <dig>  and mcf10a, a non-tumourigenic human breast epithelial cell line.
material was processed according to manufacturers' recommendations .
these cell lines have been used repeatedly in a variety of validation studies, e.g.
<cit> , and the microarray data have been previously validated using real time pcr and by comparison between array types  <cit> .
microarray data were pre-processed using rma  <cit>  in bioconductor  <cit> ; proteomics data using proteinpilot and progroup .
in this section, mappings between the proteomics data and hgu133plus <dig> arrays and mappings between the proteomics data and exon arrays are both considered.
an overview of the approaches is shown in figure  <dig>
transcript to protein comparison
in order to compare the method against current approaches, we first analysed the proteomics data alongside microarray data produced by hybridizing the rna to hgu133plus <dig> arrays .
protein level summaries were generated using proteinpilot/progroup to search against the celera database .
swiss prot identifiers, provided as part of the database annotation, were mapped to the array data using genecruiser  <cit> .
<dig> proteins were identified in this search .
<dig> proteins were not mapped, either because they did not have an associated identifier  or because no matching probeset was found .
in total,  <dig> proteins were mapped to  <dig> probesets, via  <dig> genes; figure  <dig>  for these data, r =  <dig> , p value of significance <  <dig> × 10- <dig>  similar results were observed when mapping via genbank  <cit>  accession and by searching other protein databases .
this analysis represents the current state of the art, in which a single reporter  is used for the majority of transcripts, peptide data are summarised to provide a single value per protein, and mappings between the protein and the transcript data performed at the level of individual transcripts and proteins.
in order to investigate whether exon arrays could be used to enhance the quality of an integrated mrna/proteomics analysis, a separate array study in which the same rna was hybridized to affymetrix exon  <dig> st genechip®  arrays was also considered.
data were pre-processed using rma, as before.
high correspondence between these arrays and the hgu133plus <dig> arrays described above has been previously reported, demonstrating the intrinsic compatibility of both sets of array data  <cit> .
probeset to peptide comparison
since exon arrays aim to provide a probeset in every known and predicted exon in the entire genome, they raise the possibility of performing a much more fine grained analysis than has been previously possible.
this might be done by mapping individual peptides to the appropriate exon array probeset, using an annotation database to assign peptides to their originating exons, rather than by simply performing transcript and protein level summaries and mapping based on sequence id.
to investigate this, a peptide-level analysis was conducted.
a sequence search, combined with ensembl  <cit> , was first used to position peptides within their candidate proteins, after which they were aligned relative to the genome, and mapped to their originating exons using x:map and exonmap  <cit> .
x:map and exonmap were then used to provided mappings to the exon array probesets.
a peptide and probeset where mapped if they were found to originate from  the same exon.
another source of error in microarray studies arises because certain probes can hybridize to multiple targets  <cit> .
an analogous situation can occur with peptides that match multiple places within the proteome.
data were thus filtered to remove probesets and peptides that may have originated from multiple locations in the genome  <cit> .
from an initial set of  <dig>  peptides,  <dig>  remained after filtering for poor signal to noise ratio, and  <dig>  after filtering for non-specificity.
all peptides mapped to at least one exon, yielding  <dig>  individual exons from  <dig> unique genes.
when array mappings were performed,  <dig> of these exons mapped to  <dig>  probesets,  <dig>  of which were found by x:map to target only a single location in the genome.
fold changes were computed for the remaining microarray and proteomics data, and their pearson correlation found: r =  <dig>  p <  <dig> × 10- <dig> ; figure  <dig>  the difference in r  was also found to be statistically significant: p =  <dig> , suggesting that exon arrays not only support mappings at the level of individual peptides but also lead to a marginal increase in the overall correspondence in the integrated dataset.
where multiple probesets , and/or multiple peptides, are mapped to the same exon, these were averaged .
probeset to peptide comparison
a similar analysis was performed using the hgu133plus <dig> data.
probesets were mapped if they matched the genome within  <dig>  nucleotides up- or downstream of a peptide.
using this, less stringent mapping, only  <dig> peptides were successfully mapped to a corresponding hgu133plus <dig> probeset r =  <dig> ; p <  <dig> × 10-16; figure  <dig>  an exon level mapping, as before, yielded only  <dig> successful peptide-matches, and as the size of the margin for an acceptable match increases, r decreases as expected .
these results demonstrate that although the approach is successful with plus <dig> array data it is only practical with high coverage arrays such as the exon  <dig> st array; the relative paucity of probesets on the plus <dig> arrays means that very few are co-localized with a peptide, with the result that the majority of peptides are lost from the analysis.
transcript to protein comparison
we also considered the suitability of protein level summaries generated by averaging the individual fold-changes for all peptides matching to a given protein, as is commonly used to represent protein expression.
we compared these values to their corresponding transcript level averages produced from the exon array data .
although figure  <dig> shows high correlation between protein and transcript, many of the measurements also show high variance .
this is unlikely to be due simply to noise or technical artefacts, since there is highly significant correlation between individual probeset and peptide measurements, confirming their reliability .
it is likely therefore, that much of this variation is a consequence of real biological events, such as alternative splicing.
it is thus difficult to find a meaningful biological interpretation for protein or transcript level averages, since they mask a significant amount of underlying information that cannot be adequately represented as a single number.
they should therefore be interpreted with care, even though correlation between them is high.
discussion
one of the major aims of this study was to perform a technically replicated experiment in which many sources of biological variation were controlled and thus to establish a baseline level of correlation between proteomics and microarray data.
thus the comparison was between two cell lines, allowing significant quantities of high quality rna and protein to be produced, and, since each cell line was in a steady state, a major source of biological variation to be controlled  <cit> .
this strategy is reflected in the high correspondence seen, not only in the hgu133plus <dig>  but also the exon array comparisons, and is also a reflection of the quality of the data produced by both the itraq and the affymetrix systems.
as mentioned in the introduction, previous work with steady state cell line data <cit> , has also reported substantial, albeit lower, correlation , using different technology .
here we find significantly higher correlation .
clearly, these results should be interpreted with some caution given the differences between the two studies, which include differences in the platforms used to perform the assays, the informatics approaches used to integrate the data and the biological system under investigation.
however, the data presented here do show that high correlation between proteomics and microarray data can be observed in steady state cell line data.
in addition, exon arrays make it possible to show for the first time that it is feasible to use individual peptide ratios and compare them successfully to their corresponding exon array probeset by mapping via a genome annotation database.
this is particularly important given the fact that at least half of the human genome is alternatively spliced.
the substantial correlation found at the peptide-exon level, the high degree of variation observed within each individual data point at the transcript-protein level and the large amount of alternate splicing known to occur in the genome, together raise some substantial questions about the validity of averaged protein or transcript level summaries taken across the length of a 'gene'.
a criticism that can be made of quantitative proteomics approaches is that, in contrast to transcriptomics, it is not possible with current technology to fully characterize the entire proteome in a complex organism such as homo sapiens.
however, the field is undergoing rapid development and it is likely that these issues will continue to be resolved as the technology progresses.
the strategy described here relies on mapping reporters from the different platforms  to one another via the genome.
the approach is, therefore, relatively generic, and could equally be applied to other quantitative techniques.
CONCLUSIONS
these findings suggest that much of the variation found between proteomics and microarray data occurs at least in part as a consequence of the data analysis techniques and the high granularity to which such studies have, until recently, been limited.
when data processing takes into account the importance of alternative splicing, probeset location, issues of peptide/probeset selectivity and signal to noise ratio, a surprising degree of correlation is observed in the data.
where differences do remain, these are more likely to represent biological rather than technical effects, thus enhancing our ability to identify and pursue genes for which protein expression is modulated post-transcription.

 a number of studies have considered whether changes in mrna concentration are reflected by similar changes in protein abundance e.g.. typically, poor correspondence has been reported between transcript and protein levels, and in some cases, little or no correlation at all e.g.. given the complexities of protein expression, and the many stages at which it may be influenced and/or actively controlled, this is perhaps to be expected. analogies can be drawn with studies that aim to compare microarray data generated using different platforms. these have highlighted the importance of filtering to remove data with poor signal to noise ratio, and the need to restrict comparisons to those pairs of reporters that target the same transcript location. transcript location is particularly important because approximately 50% of all human genes are predicted to be alternately spliced. in many cases, different exons from the same gene will be represented in the spliced mrna complement of a cell at different abundances. most microarrays typically offer only a single reporter per gene, and target locations differ between platforms. cross mappings have generally been performed simply by seeking reporters annotated to the same gene identifier. the alternative, of restricting analyses only to those reporter-pairs that target the same transcript location, results in a significant proportion of the data being lost. a new generation of arrays have been developed with sufficient feature density to target every known and predicted exon in the human, mouse or rat genomes. these'exon' arrays offer a choice of probeset location when performing comparative studies, and allow cross-mapping to be performed with high precision, while still preserving much of the gene-level array data. numerous studies have also observed that differences in, for example, sample preparation, hybridization conditions and binding affinities can all have a significant influence on measured expression level. this makes direct comparisons between raw intensities challenging, although good correspondence is reported when comparisons are made using ratio data, e.g.. we investigated whether similar considerations could be applied to quantitative mass spectrometry proteomics data generated using the itraq reagent system. itraq is an extension of other approaches to protein identification, in which a protein sample is fragmented and then separated into distinct peptides by liquid chromatography. peptides are then analysed using mass spectrometry to further fragment them and measure the mass to charge ratio for each resultant ion. in silico comparison of the resulting ion signature against a database generated from known protein sequences is then used to identify each peptide, and thus their originating proteins. itraq, like other similar approaches such as icat and silac, adds an additional labelling step following the initial fragmentation in which a chemical tag is attached to each peptide. the aim is to label every peptide in an experimental sample with a molecule of known mass and use the relative abundance of these reporter groups to determine the relative abundance of each peptide across samples. the mass spectrometry system is set up such that each peptide is analysed separately. multiple samples are compared in a single ms run, each labelled with a different sized reporter group. in addition, each tag contains a balance group, designed such that the labels used for each sample have the same overall mass. this has the consequence that all instances of a given peptide are processed at the same time by the mass spectrometer, irrespective of the experimental sample from which they originated, and has been shown to lead to improved accuracy. when the labelled peptide is processed in the mass spectrometer it fragments, resulting in a series of ions corresponding to the fragmented peptide and an additional set of peaks corresponding to the reporter tags for each sample. by determining the relative intensities of these reporter peaks, the relative abundance of a given peptide can be compared between samples. since the major focus of this paper is to consider whether the increased precision offered by exon arrays can be used in combination with proteomics data in order to improve the quality of the mappings between the two data sources, a biological system was chosen in which relatively high correlation between protein and transcript data might be expected, thus making it possible to establish a baseline level of correlation in a well-controlled system. in previous work using cell-lines in equilibrium, for example, relatively high correspondence has been reported. here, two cell lines in steady state were also chosen. they provide not only a ready source of high quality rna and protein, but also a system in dynamic equilibrium in which a major source of biological variation is removed. the aim was to generate a well-controlled dataset with which to investigate and evaluate different data-integration approaches.  protein and mrna samples were extracted from the human breast cancer cell line mcf and mcf10a, a non-tumourigenic human breast epithelial cell line. material was processed according to manufacturers' recommendations. these cell lines have been used repeatedly in a variety of validation studies, e.g., and the microarray data have been previously validated using real time pcr and by comparison between array types. microarray data were pre-processed using rma in bioconductor; proteomics data using proteinpilot and progroup. in this section, mappings between the proteomics data and hgu133plus arrays and mappings between the proteomics data and exon arrays are both considered. an overview of the approaches is shown in figure transcript to protein comparison in order to compare the method against current approaches, we first analysed the proteomics data alongside microarray data produced by hybridizing the rna to hgu133plus arrays. protein level summaries were generated using proteinpilot/progroup to search against the celera database. swiss prot identifiers, provided as part of the database annotation, were mapped to the array data using genecruiser. proteins were identified in this search. proteins were not mapped, either because they did not have an associated identifier or because no matching probeset was found. in total, proteins were mapped to probesets, via genes; figure for these data, r =, p value of significance < × 10- similar results were observed when mapping via genbank accession and by searching other protein databases. this analysis represents the current state of the art, in which a single reporter is used for the majority of transcripts, peptide data are summarised to provide a single value per protein, and mappings between the protein and the transcript data performed at the level of individual transcripts and proteins. in order to investigate whether exon arrays could be used to enhance the quality of an integrated mrna/proteomics analysis, a separate array study in which the same rna was hybridized to affymetrix exon st genechip® arrays was also considered. data were pre-processed using rma, as before. high correspondence between these arrays and the hgu133plus arrays described above has been previously reported, demonstrating the intrinsic compatibility of both sets of array data. probeset to peptide comparison since exon arrays aim to provide a probeset in every known and predicted exon in the entire genome, they raise the possibility of performing a much more fine grained analysis than has been previously possible. this might be done by mapping individual peptides to the appropriate exon array probeset, using an annotation database to assign peptides to their originating exons, rather than by simply performing transcript and protein level summaries and mapping based on sequence id. to investigate this, a peptide-level analysis was conducted. a sequence search, combined with ensembl, was first used to position peptides within their candidate proteins, after which they were aligned relative to the genome, and mapped to their originating exons using x:map and exonmap. x:map and exonmap were then used to provided mappings to the exon array probesets. a peptide and probeset where mapped if they were found to originate from the same exon. another source of error in microarray studies arises because certain probes can hybridize to multiple targets. an analogous situation can occur with peptides that match multiple places within the proteome. data were thus filtered to remove probesets and peptides that may have originated from multiple locations in the genome. from an initial set of peptides, remained after filtering for poor signal to noise ratio, and after filtering for non-specificity. all peptides mapped to at least one exon, yielding individual exons from unique genes. when array mappings were performed, of these exons mapped to probesets, of which were found by x:map to target only a single location in the genome. fold changes were computed for the remaining microarray and proteomics data, and their pearson correlation found: r = p < × 10-; figure the difference in r was also found to be statistically significant: p =, suggesting that exon arrays not only support mappings at the level of individual peptides but also lead to a marginal increase in the overall correspondence in the integrated dataset. where multiple probesets, and/or multiple peptides, are mapped to the same exon, these were averaged. probeset to peptide comparison a similar analysis was performed using the hgu133plus data. probesets were mapped if they matched the genome within nucleotides up- or downstream of a peptide. using this, less stringent mapping, only peptides were successfully mapped to a corresponding hgu133plus probeset r =; p < × 10-16; figure an exon level mapping, as before, yielded only successful peptide-matches, and as the size of the margin for an acceptable match increases, r decreases as expected. these results demonstrate that although the approach is successful with plus array data it is only practical with high coverage arrays such as the exon st array; the relative paucity of probesets on the plus arrays means that very few are co-localized with a peptide, with the result that the majority of peptides are lost from the analysis. transcript to protein comparison we also considered the suitability of protein level summaries generated by averaging the individual fold-changes for all peptides matching to a given protein, as is commonly used to represent protein expression. we compared these values to their corresponding transcript level averages produced from the exon array data. although figure shows high correlation between protein and transcript, many of the measurements also show high variance. this is unlikely to be due simply to noise or technical artefacts, since there is highly significant correlation between individual probeset and peptide measurements, confirming their reliability. it is likely therefore, that much of this variation is a consequence of real biological events, such as alternative splicing. it is thus difficult to find a meaningful biological interpretation for protein or transcript level averages, since they mask a significant amount of underlying information that cannot be adequately represented as a single number. they should therefore be interpreted with care, even though correlation between them is high. discussion one of the major aims of this study was to perform a technically replicated experiment in which many sources of biological variation were controlled and thus to establish a baseline level of correlation between proteomics and microarray data. thus the comparison was between two cell lines, allowing significant quantities of high quality rna and protein to be produced, and, since each cell line was in a steady state, a major source of biological variation to be controlled. this strategy is reflected in the high correspondence seen, not only in the hgu133plus but also the exon array comparisons, and is also a reflection of the quality of the data produced by both the itraq and the affymetrix systems. as mentioned in the introduction, previous work with steady state cell line data, has also reported substantial, albeit lower, correlation, using different technology. here we find significantly higher correlation. clearly, these results should be interpreted with some caution given the differences between the two studies, which include differences in the platforms used to perform the assays, the informatics approaches used to integrate the data and the biological system under investigation. however, the data presented here do show that high correlation between proteomics and microarray data can be observed in steady state cell line data. in addition, exon arrays make it possible to show for the first time that it is feasible to use individual peptide ratios and compare them successfully to their corresponding exon array probeset by mapping via a genome annotation database. this is particularly important given the fact that at least half of the human genome is alternatively spliced. the substantial correlation found at the peptide-exon level, the high degree of variation observed within each individual data point at the transcript-protein level and the large amount of alternate splicing known to occur in the genome, together raise some substantial questions about the validity of averaged protein or transcript level summaries taken across the length of a'gene'. a criticism that can be made of quantitative proteomics approaches is that, in contrast to transcriptomics, it is not possible with current technology to fully characterize the entire proteome in a complex organism such as homo sapiens. however, the field is undergoing rapid development and it is likely that these issues will continue to be resolved as the technology progresses. the strategy described here relies on mapping reporters from the different platforms to one another via the genome. the approach is, therefore, relatively generic, and could equally be applied to other quantitative techniques.  these findings suggest that much of the variation found between proteomics and microarray data occurs at least in part as a consequence of the data analysis techniques and the high granularity to which such studies have, until recently, been limited. when data processing takes into account the importance of alternative splicing, probeset location, issues of peptide/probeset selectivity and signal to noise ratio, a surprising degree of correlation is observed in the data. where differences do remain, these are more likely to represent biological rather than technical effects, thus enhancing our ability to identify and pursue genes for which protein expression is modulated post-transcription. 
the study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance. using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with affymetrix exon array data at the level of individual exons. the approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome. previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. we conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited.
 previous studies comparing quantitative proteomics and microarray data have generally found poor correspondence between the two. we hypothesised that this might in part be because the different assays were targeting different parts of the expressed genome and might therefore be subjected to confounding effects from processes such as alternative splicing.  using a genome database as a platform for integration, we combined quantitative protein mass spectrometry with affymetrix exon array data at the level of individual exons. we found significantly higher degrees of correlation than have been previously observed. the study was performed using cell lines in equilibrium in order to reduce a major potential source of biological variation, thus allowing the analysis to focus on the data integration methods in order to establish their performance.  we conclude that part of the variation observed when integrating microarray and proteomics data may occur as a consequence both of the data analysis and of the high granularity to which studies have until recently been limited. the approach opens up the possibility for the first time of considering combined microarray and proteomics datasets at the level of individual exons and isoforms, important given the high proportion of alternative splicing observed in the human genome. 