Phylodynamic paramater and model inference using pretrained deep neural networks
Project description
Phylodeep
Phylodeep is a python library for parameter estimation and model selection from phylogenetic trees, based on deep learning.
Installation
Use the package pip to install phylodeep.
Usage
We recommend to perform a priori model adequacy first, to assess whether the input data resembles well the simulations on which the neural networks were trained.
###Python
import phylodeep
from phylodeep import BD, BDEI, BDSS, SUMSTATS, FULL
path_to_tree = './tests/Zurich.trees'
# set presumed sampling probability
sampling_proba = 0.20
# a priori check
model_BD_vs_BDEI = phylodeep.checkdeep(path_to_tree, model=BDSS)
# model selection
model_BDEI_vs_BD_vs_BDSS = phylodeep.modeldeep(path_to_tree, sampling_proba, vector_representation=FULL)
# the selected model is BDSS
# parameter inference
param_BDSS = phylodeep.paramdeep(path_to_tree, sampling_proba, model=BDSS, vector_representation=FULL,
ci_computation=True)
###Command line
# we use here a tree of 200 tips
# a priori model adequacy check: highly recommended
checkdeep -t ./tests/Zurich.trees -m BD -o BD_model_adequacy.png
checkdeep -t ./tests/Zurich.trees -m BDEI -o BDEI_model_adequacy.png
checkdeep -t ./tests/Zurich.trees -m BDSS -o BDSS_model_adequacy.png
# model selection
modeldeep -t ./tests/Zurich.trees -p 0.25 -v CNN_FULL_TREE -o model_selection.csv
# parameter inference
paramdeep -t ./tests/Zurich.trees -p 0.25 -m BDSS -v CNN_FULL_TREE -o HIV_Zurich_BDSS_CNN.csv
paramdeep -t ./tests/Zurich.trees -p 0.25 -m BDSS -v FFNN_SUMSTATS -o HIV_Zurich_BDSS_FFNN_CI.csv -c
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