Volume 10 Supplement 1

## Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009)

- Research
- Open Access

# Predicting microRNA targets in time-series microarray experiments via functional data analysis

- Brian J Parker
^{1}Email author and - Jiayu Wen
^{1}

**10 (Suppl 1)**:S32

https://doi.org/10.1186/1471-2105-10-S1-S32

© Parker and Wen; licensee BioMed Central Ltd. 2009

**Published:**30 January 2009

## Abstract

### Background

MicroRNA (miRNA) target prediction is an important component in understanding gene regulation. One approach is computational: searching nucleotide sequences for miRNA complementary base pairing. An alternative approach explored in this paper is the use of gene expression profiles from time-series microarray experiments to aid in miRNA target prediction. This requires distinguishing genuine targets from genes that are secondarily down-regulated as part of the same regulatory module. We use a functional data analytic (FDA) approach, FDA being a subfield of statistics that extends standard multivariate techniques to datasets with predictor and/or response variables that are functional.

### Results

In a miR-124 transfection experiment spanning 120 hours, for genes with measurably down-regulated mRNA, exploratory functional data analysis showed differences in expression profiles over time between directly and indirectly down-regulated genes, such as response latency and biphasic response for direct miRNA targets. For prediction, an FDA approach was shown to effectively classify direct miR-124 targets from time-series microarray data (accuracy 88%; AUC 0.96), providing better performance than multivariate approaches.

### Conclusion

Exploratory FDA analysis can reveal interesting aspects of dynamic microarray miRNA studies. Predictive FDA models can be applied where computational miRNA target predictors fail or are unreliable, e.g. when there is a lack of evolutionary conservation, and can provide posterior probabilities to provide additional confirmatory evidence to validate candidate miRNA targets computationally predicted using sequence information. This approach would be applicable to the investigation of other miRNAs and suggests that dynamic microarray studies at a higher time resolution could reveal further details on miRNA regulation.

## Keywords

- Linear Discriminant Analysis
- miRNA Target
- Functional Data Analysis
- miRNA Target Prediction
- Functional Principal Component Analysis

## Background

MicroRNAs (miRNAs) are a class of small non-coding RNAs that are found in both plants and animals. They are known to play important roles in gene regulatory networks by post-transcriptionally regulating the expression of other genes. These miRNAs target other transcripts by forming near-perfect or imperfect base pairings. Such formations silence genes either by mRNA cleavage or translational repression [1].

Computational sequence-based methods have been developed to predict miRNA direct targets. As animal mRNAs have imperfect complementary base pairing to their targets and as they are short in length, most such approaches first search for a perfect 7-mer seed in the 5' end of miRNAs that match to their targets. Searching for such small motifs can lead to high false positive rates and so additional tests such as conservation filters are typically applied [2]. However, although many miRNA targets are conserved in related species, restricting the analysis only to recognizably conserved targets would exclude miRNAs that have recently diverged or changed rapidly. Also, only a small number of such predicted targets have been experimentally validated.

Although animal miRNAs were originally believed to primarily translationally suppress gene expression, they have also been found to lead to mRNA destabilization or degradation. MiRNA transfection microarray experiments capable of detecting such effects at the mRNA level of targets have shown that a large number of transcripts are down-regulated by over-expression of miRNAs [3]. Such high-throughput expression data provide a promising way to assist in miRNA target prediction.

The mRNA expression changes after miRNA transfection could be a result of miRNAs directly targeting these messages (*direct targets* in the sequel), or the mRNA of genes could also be indirectly down-regulated if they are a part of a miRNA-mediated regulatory module controlled by the direct miRNA targets (*indirect targets* in the sequel) [3]. In this case, these indirectly regulated genes' response is causally related to the directly regulated genes in the corresponding module. As such, indirect targets would be expected to respond with delayed expression changes over time relative to the direct targets.

Time-series microarray experiments that repeatedly measure gene expression simultaneously for multiple genes over a time period can capture temporal patterns and dependency of gene expression changes. A recent study by Wang and Wang [4] applied sequence-based target prediction to identify targets and then used a time-series miRNA transfection microarray analysis to experimentally validate the predictions. Another study used a gene set enrichment-like score to measure expression changes at different times [5]. However, these methods treated expression changes over time points as separate observations and therefore did not fully utilize the information on time dependency of gene expression changes. In this paper we aim to explore the differences between the expression profiles of genes that are direct miRNA targets and indirectly targeted genes, including classification of genes based on their profile. We use functional data analysis (FDA) to utilize the higher level structure due to the data being a set of finite samples from a continuous and presumably smooth curve.

Functional data analysis [6–9] is a subfield of statistics applicable when the predictor or response variable can be treated as a set of samples from a function, rather than simply as a feature vector. It is, in essence, the extension of standard multivariate analysis of finite dimensional vector spaces to infinite dimensional function spaces (e.g. reproducing kernel Hilbert spaces (RKHS)) using a functional analytic approach. FDA entails interpolating and smoothing the set of function samples using some appropriate set of basis functions (e.g. splines), kernel smoothing, or regularization. Multivariate procedures such as principal component, regression and classification analyses typically involve an inner product (or some form of metric) defined on the vector space: for the reconstructed continuous function, the conventional multivariate dot product, $\u3008x,y\u3009={\displaystyle \sum _{i=1}^{n}{x}_{i}{y}_{i}}$, can be replaced by the corresponding functional inner product, $\u3008x,y\u3009={\displaystyle {\int}_{0}^{T}x(t)y(t)dt}$, for time interval *T*, in the corresponding multivariate analysis method. For example, standard PCA can be extended to functional PCA (FPCA) [6, 10] as follows. Conventional multivariate PCA finds the axes of maximum variance in the data by solving the eigenequation *Vξ* = *λξ* for variance – covariance matrix *V* = *n*^{-1} *X'X*, where *X* is the (mean centred) *n* × *p* data matrix, with *n* samples and *p* features; and *λ* and *ξ* are the corresponding eigenvalue and eigenvector. In functional PCA, the equivalent eigenequation is generalized to *Vξ* = *λξ* where *ξ* is now an eigenfunction and *V* is the covariance operator defined by: *Vx*(*s*) = ∫*v*(*s*, *t*)*x*(*t*)*dt* = ⟨(*v*(*s*, ·), *x*⟩; where the variance-covariance function (the functional extension of the variance-covariance matrix) $v(s,t)={n}^{-1}{\displaystyle \sum _{i=1}^{n}{x}_{i}(s){x}_{i}(t)}$. So, in summary, FPCA can be expressed as the eigenanalysis of the covariance operator *V*, defined by using the covariance function *v* as the kernel of an integral transform, and this formulation of the eigenequation in terms of inner products, ⟨*v*(*s*, ·), *ξ*⟩ = *λξ*, can be applied to either multivariate or functional data, with the respective definition of inner product, where *s* ∈ *T* for function domain *T* for the functional case, and index set *T* = 1,..., *p* in the multivariate case. In most cases, this expression can be computed quickly using only matrix expressions utilizing the sampled data points and a matrix of inner products between basis functions, avoiding estimation of the integral [10]. Similarly, most other standard multivariate data analyses such as canonical correlation, cluster, regression and classification analysis can be extended to functional versions.

In this paper, we use FPCA for initial exploratory analysis and a nonparametric functional data analysis (NPFDA) approach [11] for prediction. The advantage of this functional data analytic approach is that the prior knowledge that the data is generated by a continuous smooth dynamics allows for: accurate estimates of derivatives which can then be used in the analysis; effective noise reduction through curve smoothing; applicability to data with irregular time sampling schedules. Such functional data analytic approaches have been previously applied to the analysis of temporal microarray expression data e.g. to cluster time-series gene expression data [12] and to classify yeast cell-cycle gene expression profiles and Dictyostelium cell-type [13]. In this paper, we analyze miRNA-124 transfection time-series microarray expression data using FDA and show that such expression change differences can be modeled as different time activity curves to effectively predict miRNA direct targets and distinguish them from indirect targets.

## Methods

### miRNA transfection dataset

We retrieved the miRNA-124 transfection time-series microarray expression dataset from the GEO database [14] (accession no. GDS2657). The dataset contains mRNA expression profiles generated by over-expressing miRNA-124 duplexes into human HepG2 cells in comparison to negative controls at times 4, 8, 16, 24, 32, 72 and 120 hours, using Affymetrix microarrays (U133Plus2; RMA-normalized). miRNA-124 is known to be highly expressed in several tissues including brain and kidney, but is not highly expressed in the cell line used [3, 4]. Starting with all genes provided on the U133Plus2 microarray, we first excluded those genes showing low overall expression levels in the control samples as these genes would be unlikely to be able to clearly show down-regulation after miRNA transfection. Only probes where the expression levels over at least half of all time points of the control samples were greater than the median expression level of control samples were retained for further analysis. Expression values were averaged for those genes with corresponding multiple probes.

We next identified genes showing substantial evidence for down-regulation of mRNAs. Genes with over 1.4-fold under-expression (corresponding to a log_{2} expression fold change (FC) of less than -0.5) for at least one time point were considered to be down-regulated genes. We subsequently restricted our analysis to these down-regulated genes in this study (from this down-regulated set, we also excluded a further 19 very noisy genes which had some time points showing greater than 1.3-fold over-expression).

### miRNA target prediction

We obtained two pre-computed target gene sets for miR-124 that were predicted by TargetScanS [15] and PITA [16]. The TargetScanS pre-computed target set for miR-124 contains 289 down-regulated genes with 7- and 8-mer sites conserved across several mammalian species and 384 genes with non-conserved sites. The PITA pre-computed target set for miR-124 contains 198 down-regulated genes with 7- and 8-mer conserved sites and 236 genes with non-conserved sites (a conservation score of higher and lower than 0.9 was considered as the conserved and nonconserved targets, respectively [16]). To construct a control set of genes that were not predicted to be miRNA-124 targets, we further excluded genes showing even weak sites. To do so, we first extracted annotated 3'UTR sequences of genes from Ensemble [17] using Biomart [18]. miRNA target predictions were then performed on the 3'UTRs using PITA [19] with a relaxed parameter setting: a seed of length 6 to 8 bps and allowing G::U pairing in 7- and 8-mer seeds.

### Construction of datasets

- (1)
*Direct-target training set*: we used the existing computational miRNA target predictors described above to construct a relatively high confidence direct-target training set. As current computational target predictors can vary in their effectiveness [20], to increase confidence we required both predictors to agree. We obtained 157 down-regulated genes that were predicted by both predictors with 7- and 8-mer seed sites and that were found to be evolutionarily conserved in several other species by both predictors. - (2)
*Indirect-target training set*: we selected the down-regulated genes that had annotated 3'UTRs but in which no target sites could be found by either predictor and even no weak sites could be found by using PITA prediction with relaxed parameters. From these genes, a set of size identical to the direct-target training set was randomly sampled to form the indirect-target training set.

- (1)
*Non-conserved direct-target test set*: we combined the down-regulated genes that were predicted from either of the predictors, yet that had no evidence of conservation. This set consisted of 424 genes and presumably would be enriched for direct targets. - (2)
*Indirect-target test set*: we constructed another independent dataset which met the same criteria as the indirect-target training set but excluding genes that had been used for training the model. This set consisted of 372 genes.

### Functional data analysis

We used FPCA to perform exploratory data analysis. To fit a smooth function to the discrete sampled data, we used a set of 9 B-spline basis functions of order 4 (for cubic smoothing splines). Knots were located at the data points. Additional regularized smoothing (*λ* = 0.01) was applied (second derivative roughness penalty).

The NPFDA approach was used for classification. The first derivative of the expression profile was effectively used as the predictor variable, with a boolean (two-class) response variable. The NPFDA used B-spline basis functions (with 7 knots) to produce smooth first derivative estimates. Performance evaluation was by 10 × stratified 10-fold cross-validation (CV). Major parameters were determined via nested CV separately for each fold; other parameter settings were set to their defaults or as appropriate for the data size. To evaluate the discriminability of the classes, the Receiver Operating Characteristics (ROC) curves were calculated as well as the associated area under the ROC curve (AUC).

R code implementing the analysis is available from the authors.

## Results and discussion

### Exploratory data analysis with functional principal components analysis (FPCA)

### Classification using nonparametric FDA (NPFDA)

As noted above, the gene expression curves show a large variance in overall fold change in the first principal component of the functional PCA. A key preprocessing step in FDA is registration of the curves to remove unimportant amplitude or other variations. Prior to classification, therefore, each curve was standardized to have a mean (log_{2}) fold change of -1. Thus, average fold change differences between the direct and indirect target sets were normalized away, and the shape of the response curves alone was effectively used to distinguish the direct targets from the indirect targets. Using curve shape information only would be expected to provide a more robust predictor than also relying on the absolute fold change as a feature, as it varies substantially between genes. An NPFDA discrimination model was trained on the direct and indirect target training sets (see Methods). The performance was compared with linear discriminant analysis (LDA) as an example of a standard multivariate analysis.

*p <*0.01; McNemar test). Figure 7 shows the ROC curves for NPFDA and standard LDA: NPFDA shows a substantial improvement over multivariate LDA. For comparison, the discriminability of each time point treated separately as a univariate feature was examined: the expression level across genes at each time point was used directly for ranking to compute the corresponding AUC. The best discriminating time point was at 24 hours with an AUC of 0.91.

Classification performance using NPFDA and LDA

Accuracy (standard error) | AUC (standard error) | |
---|---|---|

NPFDA | 88% (0.019) | 0.96 (0.012) |

LDA | 83% (0.022) | 0.88 (0.020) |

*>*0.9 gives a set of 203 new non-conserved miRNA target predictions (cf. 76 conserved targets predicted in [4]).

Figure 9(b) shows the bottom predictions of the non-conserved direct target test set with a posterior probability of less than 0.2 (size 44). These curves appear to match the indirect target profile seen in the training set indicating that they are likely false positives of the computational predictors. Figure 9(c) shows the time series plots for the top predictions for the indirect-target test set (black set in figure 8) with a posterior probability of less than 0.2 (size 262). These curves match the expected indirect target expression change profile. It also appears that there are at least two subgroups amongst these indirect targets, one with a much longer response time.

## Conclusion

In this study, we presented an FDA analysis of the differences in expression profiles between direct and indirect miRNA targets in a miR-124 miRNA transfection experiment. An exploratory FDA analysis showed differences in response latency, with direct targets showing an immediate down-regulation whereas indirect targets typically showed an approximately 32 hour delay. Also, direct miRNA target curves show evidence of a biphasic, two-component response with an initial early decrease presumably due to direct effects of the miRNA on mRNA, and a later component matching the response of the indirect targets, possibly due to secondary effects.

These time profile differences can be utilized for classification, and in the prediction analysis we showed that FDA can provide very good discrimination, substantially better than a standard multivariate analysis, as FDA utilizes the prior knowledge that the biological process of regulation generates a smoothly varying time profile. Such a predictive model would be especially useful in cases where computational approaches are less reliable e.g. lack of evolutionary conservation. Further, this approach can be used to provide additional confirmatory evidence (posterior probabilities) for computationally predicted miRNA targets and so improve computational miRNA target prediction. This approach would be applicable to the investigation of other miRNAs and these results suggest that dynamic microarray studies at a higher time resolution could reveal further details on miRNA regulation.

## Declarations

### Acknowledgements

Thanks to Anders Krogh for helpful discussions. This work was supported by the Danish National Science Foundation and Novo Nordisk Foundation.

This article has been published as part of *BMC Bioinformatics* Volume 10 Supplement 1, 2009: Proceedings of The Seventh Asia Pacific Bioinformatics Conference (APBC) 2009. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/10?issue=S1

## Authors’ Affiliations

## References

- Engels BM, Hutvagner G: Principles and effects of microRNA-mediated post-transcriptional gene regulation.
*Oncogene*2006, 25: 6163–6169. 10.1038/sj.onc.1209909View ArticlePubMedGoogle Scholar - Yoon S, De Micheli G: Computational identification of microRNAs and their targets.
*Birth Defects Res C Embryo Today*2006, 78: 118–128. 10.1002/bdrc.20067View ArticlePubMedGoogle Scholar - Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.
*Nature*2005, 433: 769–773. 10.1038/nature03315View ArticlePubMedGoogle Scholar - Wang X, Wang X: Systematic identification of microRNA functions by combining target prediction and expression profiling.
*Nucleic Acids Res*2006, 34: 1646–1652. 10.1093/nar/gkl068PubMed CentralView ArticlePubMedGoogle Scholar - Cheng C, Li LM: Inferring microRNA activities by combining gene expression with microRNA target prediction.
*PLoS ONE*2008, 3: e1989. 10.1371/journal.pone.0001989PubMed CentralView ArticlePubMedGoogle Scholar - Ramsay JO, Dalzell CJ: Some tools for functional data analysis.
*J R Stat Soc Ser B*1991, 53: 539–572.Google Scholar - Ramsay JO, Silverman BW:
*Functional data analysis*. first edition. Springer-Verlag, New York: Springer Series in Statistics; 1997.View ArticleGoogle Scholar - Ramsay JO, Silverman BW:
*Functional data analysis*. second edition. Springer-Verlag, New York: Springer Series in Statistics; 2005.Google Scholar - Hastie T, Tibshirani R, Friedman J:
*The elements of statistical learning*. Springer-Verlag, New York: Springer Series in Statistics; 2001.View ArticleGoogle Scholar - Besse P, Ramsay JO: Principal components analysis of sampled functions.
*Psychometrika*1986, 51: 285–311. 10.1007/BF02293986View ArticleGoogle Scholar - Ferraty F, Vieu P:
*Nonparametric functional data analysis*. Springer-Verlag, New York: Springer Series in Statistics; 2006.Google Scholar - Song JJ, Lee HJ, Morris JS, Kang S: Clustering of time-course gene expression data using functional data analysis.
*Comput Biol Chem*2007, 31: 265–274. 10.1016/j.compbiolchem.2007.05.006PubMed CentralView ArticlePubMedGoogle Scholar - Müller HG, Leng X: Classification using functional data analysis for temporal gene expression data.
*Bioinformatics*2005, 22: 68–76. 10.1093/bioinformatics/bti742PubMedGoogle Scholar - GEO[http://www.ncbi.nlm.nih.gov/geo/]
- TargetScanS[http://www.targetscan.org/]
- PITA[http://genie.weizmann.ac.il/pubs/mir07/index.html]
- Ensembl[http://www.ensembl.org/]
- BioMart[http://www.biomart.org/]
- Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E: The role of site accessibility in microRNA target recognition.
*Nat Genet*2007, 39: 1278–1284. 10.1038/ng2135View ArticlePubMedGoogle Scholar - Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP: The impact of microRNAs on protein output.
*Nature*2008, 455: 64–71. 10.1038/nature07242PubMed CentralView ArticlePubMedGoogle Scholar - Khanin R, Vinciotti V: Computational modeling of post-transcriptional gene regulation by microRNAs.
*J Comput Biol*2008, 15: 305–316. 10.1089/cmb.2007.0184View ArticlePubMedGoogle Scholar

## Copyright

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.