Python code and IPython notebooks accompanying our paper.biorxiv ncomms
.
├── figures
│ └── figure2a.png
├── input_data
│ └── y10k_hybrids_Yield.hdf5
├── notebooks
│ ├── results1.ipynb
│ ├── results2ab.ipynb
│ ├── results2c.ipynb
│ ├── results2d.ipynb
│ ├── results2e.ipynb
│ └── results2f.ipynb
├── output
│ └── README.md
├── README.md
├── REQUIREMENT.txt
└── y10k_prediction_methods
├── BLUP.py
├── data_import.py
├── dependence.py
├── helper_functions.py
├── __init__.py
├── LMM.py
├── midparent.py
├── MRF.py
├── MTLMM.py
├── QTL_fitting.py
└── train_and_test_sets.py
train_and_test_sets.py
- code for partitioning individuals into four sets, as shown in Figure 3aBLUP.py
- fitting the BLUP modelQTL_fitting.py
- constructing and fitting the QTL modelLMM.py
- constructing and fitting the LMM and LMM+P modelsMTLMM.py
- fitting the multi-trait LMMMRF.py
- fitting the mixed random forest
This notebook was used to produce the results for Figure 2. All models were fitted using Limix.