Background: Gene co-expression network analyses have become a central approach for the systems-level analysis of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since some genes are able to up- or down-regulate other genes, it is important to explicitly consider both positive and negative correlations when constructing a gene co-expression network. Additionally, there has been a growing interest in the systematic comparison of multiple networks to identify deferentially changed links. Typically, such analyses are focused on the comparison of networks or data from two different conditions. Results: Here, we present an R package for calculating the weighted topological overlap (wTO), that explicitly addresses the sign of wTO values . The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series , without replicates. Additionally, our R package incorporates a novel method for calculating a consensus network (CN) from two or more networks . To visualize the resulting networks, the R packagecontains a visualization tool which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of 20,000 genes in under two hours. Conclusion: In this work, we developed an R package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL-2 Open Source License URL
Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. Here, we present an R package for calculating the wTO, that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks . The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL-2 Open Source License URL
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Additionally, there has been a growing interest in the systematic comparison of multiple networks to identify deferentially changed links. Typically, such analyses are focused on the comparison of networks or data from two different conditions. Results:
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weighted topological overlap (wTO), that
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(without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated
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. To visualize the resulting networks, the R packagecontains a visualization tool which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of 20,000 genes in under two hours. Conclusion:
into our R package. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set.
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