SiSp

Authors: Lara Urban

License: MIT

Contributed by: Lara Urban

Postprocessing: None

Trained on: Line joxm_1 from single donor in HipSci (Kilpinen 2016) using scM&T-seq. The cassette exons were split into training, validation and test set (60%, 20%, 20%).

Source files

The SiSp model predicts splicing patterns based on a genomic sequence (800bp) at the center of the alternative exon of a cassette exon. It takes methylation into account by handling methylated cytosine as 5th base, and operates on a single-cell level.

Create a new conda environment with all dependencies installed

kipoi env create SiSp
source activate kipoi-SiSp

Test the model

kipoi test SiSp --source=kipoi

Make a prediction

kipoi get-example SiSp -o example
kipoi predict SiSp \
  --dataloader_args='{"anno_file": "example/anno_file", "fasta_file": "example/fasta_file", "meth_file": "example/meth_file", "target_file": "example/target_file"}' \
  -o '/tmp/SiSp.example_pred.tsv'
# check the results
head '/tmp/SiSp.example_pred.tsv'

Create a new conda environment with all dependencies installed

kipoi env create SiSp
source activate kipoi-SiSp

Get the model

import kipoi
model = kipoi.get_model('SiSp')

Make a prediction for example files

pred = model.pipeline.predict_example(batch_size=4)

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'])

Make predictions for custom files directly

pred = model.pipeline.predict(dl_kwargs, batch_size=4)

Get the model

library(reticulate)
kipoi <- import('kipoi')
model <- kipoi$get_model('SiSp')

Make a prediction for example files

predictions <- model$pipeline$predict_example()

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)

Make predictions for custom files directly

pred <- model$pipeline$predict(dl_kwargs, batch_size=4)

Get the docker image

docker pull kipoi/kipoi-docker:sharedpy3keras2tf2-slim

Get the full sized docker image

docker pull kipoi/kipoi-docker:sharedpy3keras2tf2

Get the activated conda environment inside the container

docker run -it kipoi/kipoi-docker:sharedpy3keras2tf2-slim

Test the model

docker run kipoi/kipoi-docker:sharedpy3keras2tf2-slim kipoi test SiSp --source=kipoi

Make prediction for custom files directly

# Create an example directory containing the data
mkdir -p $PWD/kipoi-example 
# You can replace $PWD/kipoi-example with a different absolute path containing the data 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:sharedpy3keras2tf2-slim \
kipoi get-example SiSp -o /app/example 
docker run -v $PWD/kipoi-example:/app/ kipoi/kipoi-docker:sharedpy3keras2tf2-slim \
kipoi predict SiSp \
--dataloader_args='{'anno_file': '/app/example/anno_file', 'fasta_file': '/app/example/fasta_file', 'meth_file': '/app/example/meth_file', 'target_file': '/app/example/target_file'}' \
-o '/app/SiSp.example_pred.tsv' 
# check the results
head $PWD/kipoi-example/SiSp.example_pred.tsv

Install apptainer

https://apptainer.org/docs/user/main/quick_start.html#quick-installation-steps

Make prediction for custom files directly

kipoi get-example SiSp -o example
kipoi predict SiSp \
--dataloader_args='{"anno_file": "example/anno_file", "fasta_file": "example/fasta_file", "meth_file": "example/meth_file", "target_file": "example/target_file"}' \
-o 'SiSp.example_pred.tsv' \
--singularity 
# check the results
head SiSp.example_pred.tsv

Schema

Inputs

Single numpy array

Name: seq

Shape: (800, 5)

Doc: DNA sequence

Targets

Single numpy array

Name: targets

Shape: (1,)

Doc: Probability of exluded exon in a cassette exon setting

Dataloader

Defined as: .

Doc: The SiSp model predicts splicing patterns based on a genomic sequence of 800bp centered on the alternative exon. It takes methylation into account and operates on a single-cell level.

Authors: Lara Urban

Type: PreloadedDataset

License: MIT

Arguments

anno_file : gtf file with chr, start, end and orientation of an exon/a gene

fasta_file : reference genome sequence

meth_file : bedGraph file with single-base methylation information

target_file (optional): path to the targets (.csv) file

Model dependencies

conda:

python=3.8
numpy
pandas
pip=22.0.4
keras=2.8
tensorflow=2.8

pip:

h5py
protobuf==3.20

Dataloader dependencies

conda:

bioconda::pysam=0.17
bioconda::tabix=1.11
python=3.8
numpy
pandas

pip: