Moore Notes 7 6 15

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Group Call

  • Participants: Katie, Josh
  • Progress report submitted
  • ShotMAP paper submitted
  • Josh: Tara Oceans - preliminary analyses
    • Spatial autocorrelation measured by Morans I statistic
      • About 3% show autocorrelation
      • Example of a KEGG Orthology Group that shows autocorrelation (map)
      • Distribution of I values in broad functional categories: plot
      • Distribution of statistical significance with Bonferroni correction: p-values using MCMC simulations
        • Patrick: Try FDR correction
    • Methods for quantifying KO abundance
      • Fragment recruitment to assembly based database (Tara Oceans) versus classification to a protein database
        • Patrick: How about first assembly, then genomic protein db? Stephen: humann2 does this with genome db then genomic protein db
      • Tom: compare to RPKG values with AGS normalization
      • Patrick/Stephen: What about abundance? Or absolute abundance (i.e., concentration)? Josh: flow cytometry data
      • Unmapped reads can create or destroy spatial autocorrelation (look at classification rates)
    • Comparing to taxonomic distributions
      • Sarah's methods
      • Patrick has Dongying's PD metric for all KOs
    • KOs versus modules
      • Katie: aggregate data for module before modeling, computing autocorrelation
      • Stephen: look at humann paper (e.g., minPath)
    • Stephen: Look at gene content and strain level variation
      • Presence / absence analysis
  • Pollard lab summer intern: Kit Tse
    • Spatial autocorrelation
  • Next call in two weeks: Stephen
    • Metaquery tool
    • Strain level variation