Inferring Correlation Networks from Genomic Survey Data

High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and…

• UD
  U.S. Department of Energy

  Awards: -AC02-05CH11231, Contract No. DE-AC02-05CH11231, 05CH11231, No. DE-AC02-05CH11231, AC02-05CH11231, DE-AC02, DE-AC02-05CH11231, DE-AC02-
• OO
  Office of Science

  Awards: AC02-05CH11231, -AC02-05CH11231, DE-AC02, No. DE-AC02-05CH11231, Contract No. DE-AC02-05CH11231
• BA
  Biological and Environmental Research

  Awards: 05CH11231, No. DE-AC02-05CH11231, Contract No. DE-AC02-05CH11231, DE-AC02-05CH11231, AC02-05CH11231
• LB
  Lawrence Berkeley National Laboratory

  Awards: DE-AC02-05CH11231, No. DE-AC02-05CH11231, Contract No. DE-AC02-05CH11231, 05CH11231, AC02-05CH11231