A novel deep learning framework for transcription regulators prediction via integraing large-scale epigenomic data.
Project description
TRAPT
TRAPT is a novel deep learning framework for transcription regulators prediction via integraing large-scale epigenomic data.
Usage
First, download library:
Second, install TRAPT:
conda create --name TRAPT python=3.7
conda activate TRAPT
pip install TRAPT
Run TRAPT using a case:
import os
from TRAPT.TRAPT import Args, RP_Matrix, runTRAPT
# library path
library = 'library'
# input file path
input = 'ESR1@DataSet_01_111_down500.txt'
# output file path
output = 'output/ESR1@DataSet_01_111_down500'
rp_matrix = RP_Matrix(library)
args = Args(input, output)
os.system(f'mkdir -p {output}')
runTRAPT([rp_matrix, args])
Detail
# Constructing TR-RP matrix
python3 CalcTRRPMatrix.py library
# Constructing H3K27ac-RP matrix
python3 CalcSampleRPMatrix.py H3K27ac library
# Constructing ATAC-RP matrix
python3 CalcSampleRPMatrix.py ATAC library
# Reconstruct TR-H3K27ac adjacency matrix
python3 DLVGAE.py H3K27ac library
# Reconstruct TR-ATAC adjacency matrix
python3 DLVGAE.py ATAC library
# Prediction (TR-H3K27ac)-RP matrix
python3 CalcTRSampleRPMatrix.py H3K27ac library
# Prediction (TR-ATAC)-RP matrix
python3 CalcTRSampleRPMatrix.py ATAC library
# TRAPT predicts TR activity
python3 TRAPT.py library input output
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
TRAPT-0.0.5.tar.gz
(12.2 kB
view hashes)
Built Distribution
TRAPT-0.0.5-py3-none-any.whl
(14.8 kB
view hashes)