Abstract
BackgroundIt is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. Therefore, this knowledge is used only as a second step, after data are just clustered according to their expression patterns. Thus, it could be very useful to be able to improve the clustering of biological data by incorporating prior knowledge into the cluster formation itself, in order to enhance the biological value of the clusters.ResultsA novel training algorithm for clustering is presented, which evaluates the biological internal connections of the data points while the clusters are being formed. Within this training algorithm, the calculation of distances among data points and neurons centroids includes a new term based on information from well-known metabolic pathways. The standard self-organizing map (SOM) training versus the biologically-inspired SOM (bSOM) training were tested with two real data sets of transcripts and metabolites from Solanum lycopersicum and Arabidopsis thaliana species. Classical data mining validation measures were used to evaluate the clustering solutions obtained by both algorithms. Moreover, a new measure that takes into account the biological connectivity of the clusters was applied. The results of bSOM show important improvements in the convergence and performance for the proposed clustering method in comparison to standard SOM training, in particular, from the application point of view.ConclusionsAnalyses of the clusters obtained with bSOM indicate that including biological information during training can certainly increase the biological value of the clusters found with the proposed method. It is worth to highlight that this fact has effectively improved the results, which can simplify their further analysis.The algorithm is available as a web-demo at http://fich.unl.edu.ar/sinc/web-demo/bsom-lite/. The source code and the data sets supporting the results of this article are available at http://sourceforge.net/projects/sourcesinc/files/bsom.
Highlights
It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge
In the biology field, clustering is implemented under the guilt-by-association principle [1], that is to say, the assumption that compounds involved in a biological process behave under the control of the same regulatory networks [2]
We present a novel training algorithm that combines biological similarities derived from metabolic pathways information and demonstrate that its application improves the quality of the clustering
Summary
It is a common practice in bioinformatics to validate each group returned by a clustering algorithm through manual analysis, according to a-priori biological knowledge. This procedure helps finding functionally related patterns to propose hypotheses for their behavior and the biological processes involved. This knowledge is used only as a second step, after data are just clustered according to their expression patterns. For example in [11] SOM clustering was used for the analysis of Arabidopsis thaliana datasets, helping in the hypothesis validation of a metabolic mechanism responding to sulfur deficiency. SOMs have been recently proposed for the integration and knowledge discovery of coordinated variations in transcriptomics and metabolomics data [12], and a software tool for SOM application has been designed to give support to the data mining task of datasets derived from different databases, providing user-friendly interface and several visualization tools easy to understand by non-expert users [13]
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