Last updated: 2019-11-21

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Knit directory: IITA_2019GS/

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File Version Author Date Message
Rmd bfffb51 wolfemd 2019-11-21 Publish the first set of analyses and files for IITA 2019 GS,
Rmd 25ad02e wolfemd 2019-11-21 Start workflowr project.
Rmd dcc94b9 wolfemd 2019-11-21 Start workflowr project.

Purpose of this section will be for background, summary, notes and future directions re: IITA GS-related analyses conducted in 2019.

Standardization ToDo’s

  1. PrepareTrainingData
  • The raw data for IITA trials is >500Mb, far to big for GitHub. How to share?
  • Group and select trials to analyze. Manual creation / curation of the variable TrialType, and selection of trials is tedious. Upgrading meta-data on DB and making decisions about which trials to download in the first place could alleviate this. Would have downstream consequences for the code, which would need fixing.
  • Traits and TraitAbbreviations. Preselection of traits and DB-automated abbreviations would eliminate this manual step.
  • Assign genos to phenos. Currently, requires alot of user (my) input, in the form of external flat files that I have put together over time. The database meta-information needs be added and the download functionality put in place to explicitly match DNA-samples to plots in downloaded trial data.
  • PerArea calculations. Improvements to meta-information on the DB are still needed to ensure the correct plot spacing and sizes are used to compute fresh root yields correctly. Using max(NOHAV) from each trial, at pressent.
  • Season-wide mean disease. Currently depends on which traits and months-after-planting are in the dataset. Solvable with changes in future R code.
  • A few trials have variants on the most common / consensus locationName, so I have to fix them.
  • Detect experimental designs. My code to detect designs is standardized, and doesn’t require user input. However, the need to do an ad hoc procedure here could be eliminated by changes on the DB and by breeding programs QC of data. User input is needed, or at least, I did a bunch of customization to the data at this point, which maybe could be avoided
  • TO DO / FUTURE DIRECTIONS:
    • Add trial level curation here and/or
    • Add outlier detection and removal and/or
    • Standardized optimization of model for each trait
  1. StageI_GetBLUPs
  2. StageII_CheckPredictionAccuracy
  • Dosage matrices and kinship matrices are too large for GitHub. What is the current best-practice for sharing those?