Analyzing miRNA co-expression networks to explore TF-miRNA regulation.
BACKGROUND
Current microRNA (miRNA) research in progress has engendered rapid accumulation of expression data evolving from microarray experiments. Such experiments are generally performed over different tissues belonging to a specific species of metazoan. For disease diagnosis, microarray probes are also prepared with tissues taken from similar organs of different candidates of an organism. Expression data of miRNAs are frequently mapped to co-expression networks to study the functions of miRNAs, their regulation on genes and to explore the complex regulatory network that might exist between Transcription Factors (TFs), genes and miRNAs. These directions of research relating miRNAs are still not fully explored, and therefore, construction of reliable and compatible methods for mining miRNA co-expression networks has become an emerging area. This paper introduces a novel method for mining the miRNA co-expression networks in order to obtain co-expressed miRNAs under the hypothesis that these might be regulated by common TFs.


RESULTS
Three co-expression networks, configured from one patient-specific, one tissue-specific and a stem cell-based miRNA expression data, are studied for analyzing the proposed methodology. A novel compactness measure is introduced. The results establish the statistical significance of the sets of miRNAs evolved and the efficacy of the self-pruning phase employed by the proposed method. All these datasets yield similar network patterns and produce coherent groups of miRNAs. The existence of common TFs, regulating these groups of miRNAs, is empirically tested. The results found are very promising. A novel visual validation method is also proposed that reflects the homogeneity as well as statistical properties of the grouped miRNAs. This visual validation method provides a promising and statistically significant graphical tool for expression analysis.


CONCLUSIONS
A heuristic mining methodology that resembles a clustering motivation is proposed in this paper. However, there remains a basic difference between the mining method and a clustering approach. The heuristic approach can produce priority modules (PM) from an miRNA co-expression network, by employing a self-pruning phase, which are analyzed for statistical and biological significance. The mining algorithm minimizes the space/time complexity of the analysis, and also handles noise in the data. In addition, the mining method reveals promising results in the unsupervised analysis of TF-miRNA regulation.
Bandyopadhyay
Sanghamitra
S
Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. sanghami@isical.ac.in
Bhattacharyya
Malay
M
D000465:Algorithms; D016000:Cluster Analysis; D003196:Computer Graphics; D030561:Databases, Nucleic Acid; D017143:Fuzzy Logic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D015233:Models, Statistical; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000235:genetics / Q000378:metabolism; D013234:Stem Cells / Q000378:metabolism; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
D035683:MicroRNAs; D014157:Transcription Factors