title: Spectral consensus strategy for accurate reconstruction of large biological networks.
abstract: 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.
authors.affiliation: Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France.
authors.forename: Séverine
authors.initials: S
authors.lastname: Affeldt
authors.affiliation: Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France.|Sorbonne Universités, UPMC University Paris 6, UMR S U1166 NutriOmics Team, Paris, 75013, France.|UMR S U1166 Nutriomics Team, INSERM, Paris, 75013, France.
authors.forename: Nataliya
authors.initials: N
authors.lastname: Sokolovska
authors.affiliation: Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France.
authors.forename: Edi
authors.initials: E
authors.lastname: Prifti
authors.affiliation: Integromics, Institute of Cardiometabolism and Nutrition, ICAN, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, 75013, France. jean-daniel.zucker@ird.fr.|Sorbonne Universités, UPMC University Paris 6, UMR S U1166 NutriOmics Team, Paris, 75013, France. jean-daniel.zucker@ird.fr.|IRD, UMI 209, UMMISCO, IRD France Nord, Bondy, F-93143, France. jean-daniel.zucker@ird.fr.
authors.forename: Jean-Daniel
authors.initials: JD
authors.lastname: Zucker
mesh_terms: D000465:Algorithms; D001419:Bacteria; D019295:Computational Biology / Q000379:methods*; D000069196:Gastrointestinal Microbiome; D006801:Humans; D000069558:Unsupervised Machine Learning
keywords: Community-based method; High-dimensional data; Microbiota; Network reconstruction; Spectral theory