Spectral consensus strategy for accurate reconstruction of large biological networks

Background: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches. Results: We propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem. Conclusions: The Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations

Additional file 1 of Spectral consensus strategy for accurate reconstruction of large biological networks

Contains complementary demonstrations as well as supplementary evaluations for the SCS approach. Specifically, Section 1 provides Propositions and associated sketches of proof that support our method. Complementary evaluations of the SCS first steps, namely SCS-spectral and SCS-learn, are given in Section 2. We also provide in Section 3 complementary evaluations of the SCS last step, named SCS-consensus. Execution time comparisons are given in Section 4. Supplementary statistics on the application to human gut microbiota close this Additional file 1. (PDF 606 kb