Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to…

• NS
  National Science Foundation

  Award: 1136818
• HH
  Howard Hughes Medical Institute
• GA
  Gordon and Betty Moore Foundation

  Award: GBMF3779
• NH
  National Human Genome Research Institute

  Awards: 3 R01 HG004872-03S2, R01 HG004872-03S2