BIGS   BioInformatics Group Seville

RegNetC: Inferring Regression Networks as a plugin for Cytoscape 2.8.2

• RegNetC: a Cytoscape (2.8.2) plugin. The software is available here 

• METHODOLOGY RegNet

  Background
  Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.

  Results
  We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.

  Conclusions
  REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET.

  Schematic view of the proposed method.

  Inferring gene regression networks with model trees.
  Isabel A Nepomuceno-Chamorro, Jesus S Aguilar-Ruiz and Jose C Riquelme.
  BMC Bioinformatics 2010, 11:517

  A screenshot of the Cytoscape plugin SoftRegNet.

• APPLICATION DOWNLOADED (Linux/Windows)

  RegNet.zip: a zip file that includes Linux/Windows Cytoscape plugin and data example.
  IMPORTANT: For computationally expensive experiments, with huge datasets, experimeints can be performed on high-performance computing clusters using the command line interface. To download click here

• USAGE SUMMARY

  The .zip file contains the following:

  • \IN: a folder with the microarray datasets of Spellman and Cho for the budding yeast (Saccharomyces cerevisiae) cell cycle. These data were synchronized by three different methods: cdc15, cdc28, and alpha-factors. Therefore, these three gene expression data sets may be defined as statistically independent.
  • \OUT: folder with output files after running the software.
  • regnet.jar weka.jar: the cytoscape plugin.

• TO RUN THE APPLICATION

  Installation steps:

  • Copy regnet.jar and weka.jar into the 'plugin/' folder of Cytoscape.
  • Copy \IN \OUT folders into the folder of Cytoscape.

• MICROARRAY DATASETS

  The microarray dataset of Spellman [1] and Cho [2] for the budding yeast (Saccharomyces cerevisiae) cell-cycle has been selected. This dataset considers a set of twenty well-described genes (see Table 1 in [3]), which encode important proteins for cell-cycle regulation. These data were synchronized by three different methods: cdc15, cdc28, and alpha-factors. Therefore, these three gene expression data sets may be defined as statistically independent.

  [1] P. Spellman, G. Sherlock, M. Zhang, V. Iyer, K. Anders, M. Eisen, P. Brown, D. Botstein, and B. Futcher, "Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization", Mol Biol Cell 1998,, vol. 9, pp. 3273–3297, 1998.

  [2] R. Cho, M. Campbell, E. Winzeler, L. Steinmetz, A. Conway, L. Wodicka, T. Wolfsberg, A. Gabrielian, D. Landsman, D. Lockhart, and R. Davis, "A genomewide transcriptional analysis of the mitotic cell cycle", Mol Cell, vol. 2, pp. 65–73, 1998.

  [3] LA. Soinov, "Towards reconstruction of gene networks from expression data by supervised learning", Gen Biol 2003.