DeepLiver/DeepLiver_Zonation

Authors: Carmen Bravo , Stein Aerts

License: Other / Non-commercial (see LICENSE.txt)

Contributed by: Carmen Bravo , Stein Aerts

Cite as: Bravo
González-Blas
Carmen.
(2022).
Enhancer
grammar
of
liver
cell
types
and
hepatocyte
zonation
states.
https://doi.org/10.1101/2022.12.08.519575

Type: None

Postprocessing: None

Trained on: Accessible genomic sites in hepatocytes in the mouse liver grouped based on the zonation patterns (generally accessible, pericentral or periportal).

Source files

Specialized deep learning model to predict zonated region accessibility patterns across hepatocytes in the mouse liver.

Create a new conda environment with all dependencies installed
kipoi env create DeepLiver/DeepLiver_Zonation
source activate kipoi-DeepLiver__DeepLiver_Zonation
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Test the model
kipoi test DeepLiver/DeepLiver_Zonation --source=kipoi
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Make a prediction
kipoi get-example DeepLiver/DeepLiver_Zonation -o example
kipoi predict DeepLiver/DeepLiver_Zonation \
  --dataloader_args='{"intervals_file": "example/intervals_file", "fasta_file": "example/fasta_file"}' \
  -o '/tmp/DeepLiver|DeepLiver_Zonation.example_pred.tsv'
# check the results
head '/tmp/DeepLiver|DeepLiver_Zonation.example_pred.tsv'
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Create a new conda environment with all dependencies installed
kipoi env create DeepLiver/DeepLiver_Zonation
source activate kipoi-DeepLiver__DeepLiver_Zonation
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Get the model
import kipoi
model = kipoi.get_model('DeepLiver/DeepLiver_Zonation')
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Make a prediction for example files
pred = model.pipeline.predict_example(batch_size=4)
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Use dataloader and model separately
# Download example dataloader kwargs
dl_kwargs = model.default_dataloader.download_example('example')
# Get the dataloader and instantiate it
dl = model.default_dataloader(**dl_kwargs)
# get a batch iterator
batch_iterator = dl.batch_iter(batch_size=4)
for batch in batch_iterator:
    # predict for a batch
    batch_pred = model.predict_on_batch(batch['inputs'])
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Make predictions for custom files directly
pred = model.pipeline.predict(dl_kwargs, batch_size=4)
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Get the model
library(reticulate)
kipoi <- import('kipoi')
model <- kipoi$get_model('DeepLiver/DeepLiver_Zonation')
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Make a prediction for example files
predictions <- model$pipeline$predict_example()
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Use dataloader and model separately
# Download example dataloader kwargs
dl_kwargs <- model$default_dataloader$download_example('example')
# Get the dataloader
dl <- model$default_dataloader(dl_kwargs)
# get a batch iterator
it <- dl$batch_iter(batch_size=4)
# predict for a batch
batch <- iter_next(it)
model$predict_on_batch(batch$inputs)
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Make predictions for custom files directly
pred <- model$pipeline$predict(dl_kwargs, batch_size=4)
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Get the docker image
Not available yet
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Get the full sized docker image
Not available yet
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Get the activated conda environment inside the container
Not available yet
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Test the model
Not available yet
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Make prediction for custom files directly
Not available yet
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Install apptainer
https://apptainer.org/docs/user/main/quick_start.html#quick-installation-steps
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Make prediction for custom files directly
Not available yet
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Schema

Inputs

List of numpy arrays

Name: None

    Shape: (500, 4) 

    Doc: DNA sequence

Targets

Single numpy array

Name: topic

    Shape: (3,) 

    Doc: Zonation Prediction (0:General, 1:Pericentral, 2:Periportal)

Dataloader

Defined as: .

Doc: Data-loader returning one-hot encoded sequences given genome intervals

Authors: Carmen Bravo

Type: None

License: MIT

Arguments

intervals_file : intervals file bed3

fasta_file : Reference genome FASTA file path.

ignore_targets (optional): if True, don't return any target variables

Model dependencies
conda:
  • python=3.7
  • numpy==1.19.5
  • h5py==2.10.0
pip:
  • tensorflow>=1.15.0
  • protobuf==3.20
Dataloader dependencies
conda:
  • python=3.7
  • bioconda::pybedtools
  • bioconda::pysam
  • bioconda::pyfaidx
  • numpy
  • pandas
pip:
  • kipoiseq