BACKGROUND
micrornas  are non-coding rnas of approximately  <dig> nucleotides  in length that regulate gene expression through translational inhibition or mrna degradation  <cit> .
mirnas have been shown to play an important role in a wide variety of biological processes such as apoptosis  <cit> , cell proliferation  <cit>  and carcinogenesis  <cit> .
currently there are approximately  <dig>  mirnas from  <dig> species in mirbase, an online database and repository for mirnas  <cit> .
computational mirna target prediction is a key component in predicting mirna action.
although mirnas are ~22nt in length, it has been shown that the ~6nt 5' mirna 'seed' region is the most crucial component for recognising and binding to target sites in the 3'utrs of genes  <cit> .
most mirna target prediction programs exploit this complementarity as well as the fact that true sites tend to be conserved between related species.
targetscan, pictar and miranda all use cross species conservation and different ways of measuring seed complementarity in their prediction algorithms  <cit> .
more recently there have been several prediction methods based on filtering or processing the above databases e.g.
mirtif  <cit>  and nbmirtar  <cit> .
gene expression microarrays are widely used to measure mrna gene expression levels in biological material.
when differences are observed between two conditions or between and experiment and a control, most of these differences are likely to be due to differences in transcriptional activity.
some differences, however, may also be due to the actions of mirnas.
clearly, if a mirna acts through translational repression, then you do not expect to see this reflected in differences in the mrna levels of its targets.
however, the effects of mirna directed mrna degradation may be detectable through changes in the expression of mirna target genes.
this has been exploited recently to analyse mrna gene expression datasets to predict mirna activity.
the basic principle is to search for overrepresentation of mirna target sites in sets of genes which are down regulated  <cit> .
in each case they linked gene expression data and mirna target predictions.
arora and simpson  <cit>  used a combination of three different statistical tests to detect mirna signatures from gene expression data, the wilcoxon rank sum test, the 'rank ratio test'  <cit> , and the absolute expression t-test.
they used these tests to identify tissue specific mirnas in both human and mouse, based primarily around targetscan predictions.
cheng and li  <cit> , use an enrichment score, where a ranked vector of genes is compared to a ranked vector of degenerated binding score profiles in which mirna target prediction binding scores , above and below a certain threshold are set to  <dig> and  <dig> respectively.
this is similar to the gene set enrichment algorithm   <cit> .
they identified the activity enhancement of mirnas that were transfected into hela cells and showed that their method performed better then gsea and the wilcoxon test.
in this paper we describe the use of a multivariate statistical technique called co-inertia analysis   <cit>  that can be used to link gene expression data and mirna target predictions from multiple programs to associate mirnas with particular diseases or conditions.
this is a simple yet highly effective approach that allows us to simultaneously analysis whole microarray datasets and databases of mirna target predictions, and visualise the data in linked two dimensional plots.
this allows us to visually identify mirnas that are associated with particular groups in the data.
the analysis can be supervised using a discriminant technique called between groups analysis   <cit>  to focus on groups of arrays that are of a priori interest.
this approach is especially useful as there is no requirement for the filtering of gene expression data or the generation of gene lists or clusters.
the method can take an entire microarray dataset and cross reference/integrate it with mirna prediction databases without the use of user defined thresholds.
cia can be used in a supervised mode where we specify groups in advance.
it can also be used for data exploration in an unsupervised mode.
this is useful in cases where the samples show great heterogeneity or are poorly characterised, as happens, for example, in many cancer related datasets.
in this paper we use cia to predict mirna activity in three different gene expression microarray datasets.
the first is a papillary thyroid carcinoma  dataset  <cit> , where both mrna microarray and mirna expression data for the same tissues, were available.
this allows us to compare our predicted mirnas against those that are actually observed to be highly expressed in the tissue.
the second is an in-house dataset where we measured gene expression in mouse macrophages from wild type versus mal knockout mice, after treatment with lipopolysaccharide.
the results were confirmed by directly measuring the levels of predicted mirnas, using rt-pcr.
the third dataset was that used by arora and simpson  <cit>  to demonstrate their mirna prediction method.
RESULTS
for each dataset we used cia to simultaneously analyse mrna gene expression data and predicted mirna target sites in the 3' utrs of the same genes.
the starting point is two tables: one table of gene expression values for g genes from n "samples"  and one table giving the counts of predicted target sites for m mirnas in the same g genes.
these tables  are analysed using non-symmetric correspondence analysis  and linked using cia.
the cia analysis gives us diagrams which can be visually inspected and show the relationships between gene expression differences in different "samples" and how these relate to differences in the occurrence of mirna target sites.
this is unsupervised and is used for data exploration and visualisation purposes.
the analysis can be made supervised by applying between group analysis  which takes class information and a nsc analysis and finds axes or vectors that best discriminate pre-assigned groups by maximising the between group variance.
we use this technique to automate the analysis by specifying a predetermined split in the microarray samples such as between those from normal and cancer tissue, and so identify putative regulating mirnas associated with the split.
the result of a bga analysis on  <dig> groups is a ranked list of mirnas.
papillary thyroid cancer dataset
we first applied cia to find mirnas associated with papillary thyroid cancer .
he et al  <cit>  produced mrna and mirna gene expression data using microarrays from ptc and adjacent unaffected tissue for  <dig> patients.
cia was applied to the  <dig> microarrays , and the associated mirna/gene frequency tables.
to simplify the comparison between our prediction technique and the experimental data, only those mirnas present on the mirna microarray, were analysed .
the full analysis including all mirnas predicted by the target prediction software are available in additional file 2: 'results of the cia for ptc using all available mirnas'.
this includes mirnas that were not tested by he et al.
<cit>  but may be potential novel mirnas involved in ptc.
axes  <dig> and  <dig> of the resultant cia for the gene expression microarrays using the targetscan gene/mirna frequency table can be seen in figure  <dig>
in figure 1a we see the ptc and the normal thyroid tissues highlighted in blue and red respectively.
as you would expect the normal samples are more homogeneous, than the ptc samples.
figure 1b, shows the mirna motifs associated with this split in the data.
the most extreme motifs along each axis are labelled and named.
motifs that are in the opposite orientation  as the ptc array samples are associated with ptc.
this is counterintuitive, but reflects the way in which mirnas usually negatively regulate gene expression.
genes that are upregulated in normal tissue may contain predicted binding sites for mirnas upregulated in ptc.
in other words, these genes are subsequently down regulated in the presence of these mirnas.
those mirnas highlighted in blue  are predicted to be upregulated in ptc, from visual inspection of the plots.
in order to systematically identify the mirnas specifically associated with the split we are interested in , we performed a supervised analysis of the data, combining cia and bga.
informally this is the equivalent of plotting a line between the centres of the two groups in figure 1a and then finding the equivalent line in the lower panel  and plotting the mirna motifs along this.
this is done automatically by bga and produces a single vector with the co-ordinate of every mirna.
this procedure was repeated for each of the  <dig> mirna/gene frequency tables, one for each of the  <dig> mirna prediction programs.
this returns  <dig> lists of mirna motifs that are ranked based on the motifs association with ptc.
as each of these programs has distinct characteristics, they returned different lists of motifs.
to allow for this, we only considered those motifs which ranked highly  with two or more of the programs.
the mirnas which are also identified by the mirna microarray are highlighted in bold
the above analysis was performed using computationally predicted target sites.
these predictions are noisy with a high false positive and false negative rate.
in order to test our approach with experimentally verified mirna targets we also applied cia to data from mirecords  <cit> .this is a resource which contains relatively small numbers of high quality experimentally confirmed mirna targets.
mirecords currently contains target information for  <dig> mirnas across  <dig> human genes.
although this is not the only database for experimentally verified mirna targets it is comprehensive, well curated and comparable to other databases such as tarbase  <cit> .
again this was used to find mirnas associated with ptc.
axes  <dig> and  <dig> of the resultant cia can be seen in figure  <dig>  in figure 2a we can see a plot of the  <dig> samples.
the plot is similar to figure 1a in that the more homogeneous normal samples are clustered together , while the ptc samples  are more scattered.
figure 2b shows the mirnas associated with the split in the data.
to the right of the figure, along the horizontal axis, we can see mir- <dig> and mir- <dig> highlighted in blue.
again these motifs are in the opposite orientation relative to the origin of the ptc samples in figure 2a, and are predicted to be upregulated in ptc.
performing a supervised analysis on this dataset using bga confirms that mir- <dig> and mir- <dig> are the mirnas most highly associated with ptc.
this analysis confirms that cia is capable of integrating both predicted and experimentally confirmed mirna target information with gene expression data.
ideally it would be preferable to focus on experimental rather than predicted mirna data, but at present this is not feasible as currently available mirna target data are limited.
for example, there are no confirmed targets of mir- <dig> in mirecords.
this is the most highly upregulated mirna in the ptc data set, and therefore we would only be able to detect this mirna using target predictions.
lps treated macrophages dataset
in this dataset, bone marrow derived macrophages from wild type  and mal knockout  mice were treated with lipopolysaccharide .
mal  is part of the toll-like receptor  signalling pathway, a key signalling molecule of the innate immune response  <cit> .
the innate immune response is triggered in the presence of the gram-negative bacterial product lps.
mal-deficient mice are known to be defective in tlr <dig> and tlr <dig> signalling but show normal signalling with other members of the tlr family  <cit> .
the aim of this experiment was the identification of mirnas whose activation is independent of mal i.e.
is upregulated in both malko and wt macrophages after exposure to lps.
in total there were four replicates of lps treated wt arrays, and four replicates of lps treated malko arrays.
as with the thyroid cancer dataset, supervised cia was performed using bga for each of the  <dig> gene/mirna frequency tables, on the two datasets to identify mal-independent mirnas.
this returns  <dig> lists of mirnas that are ranked based on their association with lps treated cells.
the lists vary depending on the individual characteristics of the prediction programs used.
the resulting axes are shown in figure  <dig> for the wt dataset and figure  <dig> for the malko dataset.
the motifs are ranked based on their association with lps treated macrophages.
these plots illustrate the overlap between the wt and the malko results i.e.
those mirnas that are mal independent.
they also demonstrate the variation between the different mirna target prediction programs.
again we selected those motifs which ranked highly  with two or more of the programs, as being most likely to be true results.
the results can be seen in table  <dig> for both datasets and are highlighted in blue in figures  <dig> and  <dig>  table two only contains those mirnas that are mal independent, and gives the ranking for each of these mirnas.
from this table mir- <dig> and mir- <dig> were selected for experimental confirmation of their induction by lps using quantitative rt-pcr.
these mirnas were chosen as they were predicted to be upregulated in both malko and wt by four of the programs, and they had a very low average ranking in the two datasets.
the consistently low ranking across all four programs in both malko and wt cells suggested that these predictions were the most likely to be correct and were therefore chosen as the most suitable mirnas for further experimental confirmation.. in figure  <dig> we can see that mir- <dig> is  <dig> fold and  <dig> fold upregulated in wt and malko lps treated macrophages respectively.
this is based on the expression of mir- <dig> relative to time zero, in macrophages extracted from mal-deficient mice, after being treated with lps for two hours.
mir- <dig> has been previously shown to be an lps inducible mirna  <cit> , and this analysis confirms that its induction is also mal independent.
figure  <dig> shows that mir- <dig> is  <dig> fold and  <dig> fold upregulated in wt and malko macrophages respectively.
the upregulation of the mirna in this case is not as strong as with mir- <dig> but is still consistent with the hypothesis that this mirna is mal independent.
experimentally verified mirnas are in bold
comparison with arora and simpson
previously arora and simpson developed an approach to detect the effect of particular mirnas on their target mrna gene expression levels  <cit> .
as with our approach, they used gene expression data and publicly available mirna target prediction data.
their analyses were based primarily around targetscan target predictions .
they used three statistical tests to detect mirna signatures from gene expression data, the wilcoxon rank sum test, the 'rank ratio test'  <cit> , and the absolute expression t-test.
they used these techniques for the identification of tissue specific mirnas, using data from a number of sources including geo series gse <dig>  <cit> .
this dataset comprised  <dig> microarrays spanning  <dig> normal human tissues.
arora and simpson focused on a comparison of  <dig> tissue types using one representative microarray from each tissue type .
extensive literature mining was used to identify evidence of tissue specific expression profiles of the mirnas.
this evidence included cloning, northern hybridization and expressed sequence tag mapping  <cit> .
we applied unsupervised cia to the same  <dig> tissues used by arora and simpson  <cit> : midbrain, heart atrium, kidney medulla, liver, lung, ovary, skeletal muscle, and testis.
in total there were  <dig> microarrays, as we included all microarrays from each tissue in our analysis rather than one representative microarray.
these were combined with the  <dig> mirna/gene frequency tables in the unsupervised cia.
axes  <dig> and  <dig> of the resultant cia for the pictar5way table can be seen in figure 7a.
visual inspection of this plot reveals a number of interesting splits in the data.
in figure 7a, the skeletal muscle  is separated from the other tissues along the vertical axis.
figure 7b, shows the mirnas associated with each tissue type.
as with the ptc, it is the motifs that are in the opposite orientation  as the skeletal muscle, that are associated with skeletal muscle.
this can be seen in the case of mir-1/ <dig>  located in the centre bottom of figure 7b.
this is in the opposite orientation relative to the origin and is a known muscle specific mirna  <cit> .
this mirna was not identified by arora and simpson as being associated with skeletal muscle, but was associated with heart tissue .
another major split in this data, is that of the midbrain tissue  along the horizontal axis.
again it is the mirnas in figure 7b that are in the opposite direction relative to the origin that are associated with brain tissue.
these include mir-196a/b and mir-29a/c.
these mirnas were also identified by arora and simpson, although only mir-29a/c has been previously characterised in brain tissue  <cit> .
in order to systematically identify tissue specific mirnas we compared individual tissues against the rest e.g.
the  <dig> skeletal muscle microarrays vs. the rest.
as before, this supervised cia was performed using bga, and consistency between mirna prediction programs was required.
table  <dig> summaries the mirnas we identified, and the overlap between our analysis and that of arora and simpson, as well as information on available experimental data.
as can be seen from the table, there is a great deal of overlap between the two datasets.
for example, a known muscle specific mirna, mir-1/ <dig>  was identified by both techniques in the heart.
however, only our technique identified mir-1/ <dig> in skeletal muscle, while we did not identify brain specific mir- <dig>  a direct comparison between the two approaches is difficult as we used more recent mirna target predictions and a greater number of microarrays were tested .
however, approximately a quarter of the predictions overlap between the two approaches with our approach identifying approximately 15% less mirnas.
also for both of the techniques roughly half the results are supported by literature review .
this suggests that although neither approach is exhaustive they are complementary.
y represents mirnas predicted by cia.
* represents those mirnas also predicted by arora and simpson.
those mirnas in bold were not incorporated into targetscan version  <dig>  .
the numbers are the references from available experimental data.
references:  <dig>  gu et al.,  <cit> ,  <dig>  sempere et al.,  <cit> ,  <dig>  lagos-quint et al.,  <cit> ,  <dig>  sathyan et al.,  <cit> ,  <dig>  hua et al.,  <cit> ,  <dig>  chan et al.,  <cit> ,  <dig>  gregory et al.,  <cit> ,  <dig>  nakada et al.,  <cit> ,  <dig>  anglicheau et al.,  <cit> ,  <dig>  ladeiro et al.,  <cit> ,  <dig>  lui et al.,  <cit> ,  <dig>  raponi et al.,  <cit>   <dig>  navarro et al.,  <cit> ,  <dig> lagos-quint et al.,  <cit> ,  <dig>  naraba et al.,  <cit> ,  <dig>  yu et al.,  <cit> ,  <dig>  nomura et al.,  <cit> ,  <dig>  baskerville et al.
<cit> .
discussion
in this paper we describe a method for inferring the action of mirnas by integrating the information provided by mirna target prediction programs with mrna gene expression data.
this method can be used to predict the activity of a mirna when there is down regulation of multiple potential target genes for that mirna.
a drawback of this and similar approaches is that it assumes that mrna degradation instead of translation repression is the primary mode of action of the mirnas.
this will only be true for some mirnas.
mirnas which act through repression will be undetectable using our methodology.
instead, the action of the mirna might be inferred through correlating genomic locus with co-expression of nearby genes  <cit> .
a further drawback of the current method is that it relies on predictions of target sites in potential target mrnas.
such predictions will contain false positives as well as false negatives and it is often difficult to strike a balance between sensitivity and specificity.
nonetheless, we have shown the current method does provide good quality predictions of mirna activity.
in the case of ptc, the method was able to predict mirnas which had been experimentally measured as showing significant differential expression.
in the case of the mal knockout data set, we were able to verify predicted mirnas using rt-pcr.
in the third dataset, we obtained comparable results to the method of arora and simpson  <cit>  whose method has already been partly validated, experimentally.
a major advantage of this method is that no pre-processing of gene expression data, such as the generation of clusters or gene lists, is required.
this is in marked contrast to most other methods such as those based on gene set enrichment analysis   <cit> .
the method can take an entire microarray data set and cross reference/integrate it with mirna prediction databases.
we also use multiple mirna target gene predictions.
this allows us to use consistency across target prediction programs and so minimise the effect of weaknesses in any one program, while maximising the number of mirnas that can be investigated.
the congruence between the sets of predicted targets of mirna sets can often be weak, particularly between targetscan/pictar vs. miranda.
even the confirmed targets from mirecords  <cit>  may often be context specific due to tissue specific expression and 3' utr splice variants.
methods in the past have often used just one of the predicted databases  <cit>  and so it may be assumed the results would vary depending on the choice of mirna target prediction software used.
by using multiple mirna target gene predictions we can use consistency across target prediction programs to maximise the reproducibility of the analysis, while retaining a large proportion of the mirnas.
creighton et al  <cit>  also developed a technique that facilitated the use of multiple target prediction algorithms, to associate mirnas with gene expression profiles of interest.
however, they required the user to specify a predefined list of genes, while our approach does not require any pre-processing of gene expression data.
the method can also be used to visualise and analyse multiple groups of arrays.
in the data sets that we tested in this paper, we only compared two groups at a time, in order to compare the method with other methods.
the visualisations are powerful as they allow simple graphical representations of highly complex datasets and the relationships between them.
CONCLUSIONS
in this study we have shown how cia can be used to integrate gene expression data and mirna target prediction data from multiple sources to predict mirnas associated with particular diseases or conditions.
although this is not the first attempt to predict mirna expression profiles using gene expression data, we believe that it is complementary to other currently available methods, and would be a useful addition to the field.
the method allows clear visualisation and data exploration of complex datasets.
it can be used in a supervised or un-supervised mode and can detect the activity of mirnas which have been experimentally measured as being expressed.

 micrornas are non-coding rnas of approximately nucleotides in length that regulate gene expression through translational inhibition or mrna degradation. mirnas have been shown to play an important role in a wide variety of biological processes such as apoptosis, cell proliferation and carcinogenesis. currently there are approximately mirnas from species in mirbase, an online database and repository for mirnas. computational mirna target prediction is a key component in predicting mirna action. although mirnas are ~22nt in length, it has been shown that the ~6nt 5' mirna'seed' region is the most crucial component for recognising and binding to target sites in the 3'utrs of genes. most mirna target prediction programs exploit this complementarity as well as the fact that true sites tend to be conserved between related species. targetscan, pictar and miranda all use cross species conservation and different ways of measuring seed complementarity in their prediction algorithms. more recently there have been several prediction methods based on filtering or processing the above databases e.g. mirtif and nbmirtar. gene expression microarrays are widely used to measure mrna gene expression levels in biological material. when differences are observed between two conditions or between and experiment and a control, most of these differences are likely to be due to differences in transcriptional activity. some differences, however, may also be due to the actions of mirnas. clearly, if a mirna acts through translational repression, then you do not expect to see this reflected in differences in the mrna levels of its targets. however, the effects of mirna directed mrna degradation may be detectable through changes in the expression of mirna target genes. this has been exploited recently to analyse mrna gene expression datasets to predict mirna activity. the basic principle is to search for overrepresentation of mirna target sites in sets of genes which are down regulated. in each case they linked gene expression data and mirna target predictions. arora and simpson used a combination of three different statistical tests to detect mirna signatures from gene expression data, the wilcoxon rank sum test, the'rank ratio test', and the absolute expression t-test. they used these tests to identify tissue specific mirnas in both human and mouse, based primarily around targetscan predictions. cheng and li, use an enrichment score, where a ranked vector of genes is compared to a ranked vector of degenerated binding score profiles in which mirna target prediction binding scores, above and below a certain threshold are set to and respectively. this is similar to the gene set enrichment algorithm. they identified the activity enhancement of mirnas that were transfected into hela cells and showed that their method performed better then gsea and the wilcoxon test. in this paper we describe the use of a multivariate statistical technique called co-inertia analysis that can be used to link gene expression data and mirna target predictions from multiple programs to associate mirnas with particular diseases or conditions. this is a simple yet highly effective approach that allows us to simultaneously analysis whole microarray datasets and databases of mirna target predictions, and visualise the data in linked two dimensional plots. this allows us to visually identify mirnas that are associated with particular groups in the data. the analysis can be supervised using a discriminant technique called between groups analysis to focus on groups of arrays that are of a priori interest. this approach is especially useful as there is no requirement for the filtering of gene expression data or the generation of gene lists or clusters. the method can take an entire microarray dataset and cross reference/integrate it with mirna prediction databases without the use of user defined thresholds. cia can be used in a supervised mode where we specify groups in advance. it can also be used for data exploration in an unsupervised mode. this is useful in cases where the samples show great heterogeneity or are poorly characterised, as happens, for example, in many cancer related datasets. in this paper we use cia to predict mirna activity in three different gene expression microarray datasets. the first is a papillary thyroid carcinoma dataset, where both mrna microarray and mirna expression data for the same tissues, were available. this allows us to compare our predicted mirnas against those that are actually observed to be highly expressed in the tissue. the second is an in-house dataset where we measured gene expression in mouse macrophages from wild type versus mal knockout mice, after treatment with lipopolysaccharide. the results were confirmed by directly measuring the levels of predicted mirnas, using rt-pcr. the third dataset was that used by arora and simpson to demonstrate their mirna prediction method.  for each dataset we used cia to simultaneously analyse mrna gene expression data and predicted mirna target sites in the 3' utrs of the same genes. the starting point is two tables: one table of gene expression values for g genes from n "samples" and one table giving the counts of predicted target sites for m mirnas in the same g genes. these tables are analysed using non-symmetric correspondence analysis and linked using cia. the cia analysis gives us diagrams which can be visually inspected and show the relationships between gene expression differences in different "samples" and how these relate to differences in the occurrence of mirna target sites. this is unsupervised and is used for data exploration and visualisation purposes. the analysis can be made supervised by applying between group analysis which takes class information and a nsc analysis and finds axes or vectors that best discriminate pre-assigned groups by maximising the between group variance. we use this technique to automate the analysis by specifying a predetermined split in the microarray samples such as between those from normal and cancer tissue, and so identify putative regulating mirnas associated with the split. the result of a bga analysis on groups is a ranked list of mirnas. papillary thyroid cancer dataset we first applied cia to find mirnas associated with papillary thyroid cancer. he et al produced mrna and mirna gene expression data using microarrays from ptc and adjacent unaffected tissue for patients. cia was applied to the microarrays, and the associated mirna/gene frequency tables. to simplify the comparison between our prediction technique and the experimental data, only those mirnas present on the mirna microarray, were analysed. the full analysis including all mirnas predicted by the target prediction software are available in additional file 2:'results of the cia for ptc using all available mirnas'. this includes mirnas that were not tested by he et al. but may be potential novel mirnas involved in ptc. axes and of the resultant cia for the gene expression microarrays using the targetscan gene/mirna frequency table can be seen in figure in figure 1a we see the ptc and the normal thyroid tissues highlighted in blue and red respectively. as you would expect the normal samples are more homogeneous, than the ptc samples. figure 1b, shows the mirna motifs associated with this split in the data. the most extreme motifs along each axis are labelled and named. motifs that are in the opposite orientation as the ptc array samples are associated with ptc. this is counterintuitive, but reflects the way in which mirnas usually negatively regulate gene expression. genes that are upregulated in normal tissue may contain predicted binding sites for mirnas upregulated in ptc. in other words, these genes are subsequently down regulated in the presence of these mirnas. those mirnas highlighted in blue are predicted to be upregulated in ptc, from visual inspection of the plots. in order to systematically identify the mirnas specifically associated with the split we are interested in, we performed a supervised analysis of the data, combining cia and bga. informally this is the equivalent of plotting a line between the centres of the two groups in figure 1a and then finding the equivalent line in the lower panel and plotting the mirna motifs along this. this is done automatically by bga and produces a single vector with the co-ordinate of every mirna. this procedure was repeated for each of the mirna/gene frequency tables, one for each of the mirna prediction programs. this returns lists of mirna motifs that are ranked based on the motifs association with ptc. as each of these programs has distinct characteristics, they returned different lists of motifs. to allow for this, we only considered those motifs which ranked highly with two or more of the programs. the mirnas which are also identified by the mirna microarray are highlighted in bold the above analysis was performed using computationally predicted target sites. these predictions are noisy with a high false positive and false negative rate. in order to test our approach with experimentally verified mirna targets we also applied cia to data from mirecords.this is a resource which contains relatively small numbers of high quality experimentally confirmed mirna targets. mirecords currently contains target information for mirnas across human genes. although this is not the only database for experimentally verified mirna targets it is comprehensive, well curated and comparable to other databases such as tarbase. again this was used to find mirnas associated with ptc. axes and of the resultant cia can be seen in figure in figure 2a we can see a plot of the samples. the plot is similar to figure 1a in that the more homogeneous normal samples are clustered together, while the ptc samples are more scattered. figure 2b shows the mirnas associated with the split in the data. to the right of the figure, along the horizontal axis, we can see mir- and mir- highlighted in blue. again these motifs are in the opposite orientation relative to the origin of the ptc samples in figure 2a, and are predicted to be upregulated in ptc. performing a supervised analysis on this dataset using bga confirms that mir- and mir- are the mirnas most highly associated with ptc. this analysis confirms that cia is capable of integrating both predicted and experimentally confirmed mirna target information with gene expression data. ideally it would be preferable to focus on experimental rather than predicted mirna data, but at present this is not feasible as currently available mirna target data are limited. for example, there are no confirmed targets of mir- in mirecords. this is the most highly upregulated mirna in the ptc data set, and therefore we would only be able to detect this mirna using target predictions. lps treated macrophages dataset in this dataset, bone marrow derived macrophages from wild type and mal knockout mice were treated with lipopolysaccharide. mal is part of the toll-like receptor signalling pathway, a key signalling molecule of the innate immune response. the innate immune response is triggered in the presence of the gram-negative bacterial product lps. mal-deficient mice are known to be defective in tlr and tlr signalling but show normal signalling with other members of the tlr family. the aim of this experiment was the identification of mirnas whose activation is independent of mal i.e. is upregulated in both malko and wt macrophages after exposure to lps. in total there were four replicates of lps treated wt arrays, and four replicates of lps treated malko arrays. as with the thyroid cancer dataset, supervised cia was performed using bga for each of the gene/mirna frequency tables, on the two datasets to identify mal-independent mirnas. this returns lists of mirnas that are ranked based on their association with lps treated cells. the lists vary depending on the individual characteristics of the prediction programs used. the resulting axes are shown in figure for the wt dataset and figure for the malko dataset. the motifs are ranked based on their association with lps treated macrophages. these plots illustrate the overlap between the wt and the malko results i.e. those mirnas that are mal independent. they also demonstrate the variation between the different mirna target prediction programs. again we selected those motifs which ranked highly with two or more of the programs, as being most likely to be true results. the results can be seen in table for both datasets and are highlighted in blue in figures and table two only contains those mirnas that are mal independent, and gives the ranking for each of these mirnas. from this table mir- and mir- were selected for experimental confirmation of their induction by lps using quantitative rt-pcr. these mirnas were chosen as they were predicted to be upregulated in both malko and wt by four of the programs, and they had a very low average ranking in the two datasets. the consistently low ranking across all four programs in both malko and wt cells suggested that these predictions were the most likely to be correct and were therefore chosen as the most suitable mirnas for further experimental confirmation.. in figure we can see that mir- is fold and fold upregulated in wt and malko lps treated macrophages respectively. this is based on the expression of mir- relative to time zero, in macrophages extracted from mal-deficient mice, after being treated with lps for two hours. mir- has been previously shown to be an lps inducible mirna, and this analysis confirms that its induction is also mal independent. figure shows that mir- is fold and fold upregulated in wt and malko macrophages respectively. the upregulation of the mirna in this case is not as strong as with mir- but is still consistent with the hypothesis that this mirna is mal independent. experimentally verified mirnas are in bold comparison with arora and simpson previously arora and simpson developed an approach to detect the effect of particular mirnas on their target mrna gene expression levels. as with our approach, they used gene expression data and publicly available mirna target prediction data. their analyses were based primarily around targetscan target predictions. they used three statistical tests to detect mirna signatures from gene expression data, the wilcoxon rank sum test, the'rank ratio test', and the absolute expression t-test. they used these techniques for the identification of tissue specific mirnas, using data from a number of sources including geo series gse. this dataset comprised microarrays spanning normal human tissues. arora and simpson focused on a comparison of tissue types using one representative microarray from each tissue type. extensive literature mining was used to identify evidence of tissue specific expression profiles of the mirnas. this evidence included cloning, northern hybridization and expressed sequence tag mapping. we applied unsupervised cia to the same tissues used by arora and simpson: midbrain, heart atrium, kidney medulla, liver, lung, ovary, skeletal muscle, and testis. in total there were microarrays, as we included all microarrays from each tissue in our analysis rather than one representative microarray. these were combined with the mirna/gene frequency tables in the unsupervised cia. axes and of the resultant cia for the pictar5way table can be seen in figure 7a. visual inspection of this plot reveals a number of interesting splits in the data. in figure 7a, the skeletal muscle is separated from the other tissues along the vertical axis. figure 7b, shows the mirnas associated with each tissue type. as with the ptc, it is the motifs that are in the opposite orientation as the skeletal muscle, that are associated with skeletal muscle. this can be seen in the case of mir-1/ located in the centre bottom of figure 7b. this is in the opposite orientation relative to the origin and is a known muscle specific mirna. this mirna was not identified by arora and simpson as being associated with skeletal muscle, but was associated with heart tissue. another major split in this data, is that of the midbrain tissue along the horizontal axis. again it is the mirnas in figure 7b that are in the opposite direction relative to the origin that are associated with brain tissue. these include mir-196a/b and mir-29a/c. these mirnas were also identified by arora and simpson, although only mir-29a/c has been previously characterised in brain tissue. in order to systematically identify tissue specific mirnas we compared individual tissues against the rest e.g. the skeletal muscle microarrays vs. the rest. as before, this supervised cia was performed using bga, and consistency between mirna prediction programs was required. table summaries the mirnas we identified, and the overlap between our analysis and that of arora and simpson, as well as information on available experimental data. as can be seen from the table, there is a great deal of overlap between the two datasets. for example, a known muscle specific mirna, mir-1/ was identified by both techniques in the heart. however, only our technique identified mir-1/ in skeletal muscle, while we did not identify brain specific mir- a direct comparison between the two approaches is difficult as we used more recent mirna target predictions and a greater number of microarrays were tested. however, approximately a quarter of the predictions overlap between the two approaches with our approach identifying approximately 15% less mirnas. also for both of the techniques roughly half the results are supported by literature review. this suggests that although neither approach is exhaustive they are complementary. y represents mirnas predicted by cia. * represents those mirnas also predicted by arora and simpson. those mirnas in bold were not incorporated into targetscan version. the numbers are the references from available experimental data. references: gu et al.,, sempere et al.,, lagos-quint et al.,, sathyan et al.,, hua et al.,, chan et al.,, gregory et al.,, nakada et al.,, anglicheau et al.,, ladeiro et al.,, lui et al.,, raponi et al., navarro et al.,, lagos-quint et al.,, naraba et al.,, yu et al.,, nomura et al.,, baskerville et al.. discussion in this paper we describe a method for inferring the action of mirnas by integrating the information provided by mirna target prediction programs with mrna gene expression data. this method can be used to predict the activity of a mirna when there is down regulation of multiple potential target genes for that mirna. a drawback of this and similar approaches is that it assumes that mrna degradation instead of translation repression is the primary mode of action of the mirnas. this will only be true for some mirnas. mirnas which act through repression will be undetectable using our methodology. instead, the action of the mirna might be inferred through correlating genomic locus with co-expression of nearby genes. a further drawback of the current method is that it relies on predictions of target sites in potential target mrnas. such predictions will contain false positives as well as false negatives and it is often difficult to strike a balance between sensitivity and specificity. nonetheless, we have shown the current method does provide good quality predictions of mirna activity. in the case of ptc, the method was able to predict mirnas which had been experimentally measured as showing significant differential expression. in the case of the mal knockout data set, we were able to verify predicted mirnas using rt-pcr. in the third dataset, we obtained comparable results to the method of arora and simpson whose method has already been partly validated, experimentally. a major advantage of this method is that no pre-processing of gene expression data, such as the generation of clusters or gene lists, is required. this is in marked contrast to most other methods such as those based on gene set enrichment analysis. the method can take an entire microarray data set and cross reference/integrate it with mirna prediction databases. we also use multiple mirna target gene predictions. this allows us to use consistency across target prediction programs and so minimise the effect of weaknesses in any one program, while maximising the number of mirnas that can be investigated. the congruence between the sets of predicted targets of mirna sets can often be weak, particularly between targetscan/pictar vs. miranda. even the confirmed targets from mirecords may often be context specific due to tissue specific expression and 3' utr splice variants. methods in the past have often used just one of the predicted databases and so it may be assumed the results would vary depending on the choice of mirna target prediction software used. by using multiple mirna target gene predictions we can use consistency across target prediction programs to maximise the reproducibility of the analysis, while retaining a large proportion of the mirnas. creighton et al also developed a technique that facilitated the use of multiple target prediction algorithms, to associate mirnas with gene expression profiles of interest. however, they required the user to specify a predefined list of genes, while our approach does not require any pre-processing of gene expression data. the method can also be used to visualise and analyse multiple groups of arrays. in the data sets that we tested in this paper, we only compared two groups at a time, in order to compare the method with other methods. the visualisations are powerful as they allow simple graphical representations of highly complex datasets and the relationships between them.  in this study we have shown how cia can be used to integrate gene expression data and mirna target prediction data from multiple sources to predict mirnas associated with particular diseases or conditions. although this is not the first attempt to predict mirna expression profiles using gene expression data, we believe that it is complementary to other currently available methods, and would be a useful addition to the field. the method allows clear visualisation and data exploration of complex datasets. it can be used in a supervised or un-supervised mode and can detect the activity of mirnas which have been experimentally measured as being expressed. 
in this study we used predicted mirna-gene interactions to analyse mrna gene expression microarray data to predict mirnas associated with particular diseases or conditions. we applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. using a database of mirna target predictions from targetscan, targetscans, pictar4way pictar5way, and miranda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of mirnas associated with a specified split in samples. we describe a technique to integrate gene expression data and mirna target predictions from multiple sources. here we combine correspondence analysis, between group analysis and co-inertia analysis to determine which mirnas are associated with differences in gene expression levels in microarray data sets. they control gene expression by either inhibiting translation or inducing mrna degradation. micrornas are non-coding rnas that regulate gene expression by binding to the messenger rna of protein coding genes.
 micrornas are non-coding rnas that regulate gene expression by binding to the messenger rna of protein coding genes. they control gene expression by either inhibiting translation or inducing mrna degradation. a number of computational techniques have been developed to identify the targets of mirnas. in this study we used predicted mirna-gene interactions to analyse mrna gene expression microarray data to predict mirnas associated with particular diseases or conditions.  here we combine correspondence analysis, between group analysis and co-inertia analysis to determine which mirnas are associated with differences in gene expression levels in microarray data sets. using a database of mirna target predictions from targetscan, targetscans, pictar4way pictar5way, and miranda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of mirnas associated with a specified split in samples. we applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. in each case we were able to identified mirnas of biological importance.  we describe a technique to integrate gene expression data and mirna target predictions from multiple sources. 