Single cell 3D genome modeling tools developed in the Bioinformatics and Machine Learning Lab
The directory contains the code used to run the experiments and our own models for the paper
Model_Weights: Trained weights of all the models used in the paper.
Models: Pytorch implementation of models used in experiments.
ProcessData: The Raw Data, Data Loaders, and preprcoessing scripts.
Pretrain: Scripts used to run experiments and to analyze experiemnt outputs.
TrainingYourData: Scripts used to training the data by yourself.
Utils: Scripts used for loss function and analyze outputs.
The Cooler file dataset for Human cells with GEO number GSE130711 can be get from https://salkinstitute.app.box.com/s/fp63a4j36m5k255dhje3zcj5kfuzkyj1 or more detailed Human single-cell data at https://salkinstitute.app.box.com/s/fp63a4j36m5k255dhje3zcj5kfuzkyj1/folder/82405563291
The Cooler file format dataset for Drosophila was obtained from GEO with code GSE131811 can be get from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131811
The processed and raw data of all 20 single-cell Hi-C data of Drosophila can be got at Zenodo: https://doi.org/10.5281/zenodo.10535486
ScHiCedsr is written in Python3 and uses the Pytorch module. The dependencies can be installed by the following command:
# create conda environment
conda env create -f ScHiCEDRN.yml
# active the environment
conda active ScHiCEDRN# First step create folder for the datasets, the 'Datasets' folder should be at the same level as 'TrainingYourData' Folder
mkdir -p Datasets/Human
# or
mkdir -p Datasets/Drosophila
# Second download the dataset to Datasets/Human or Datasets/Drosophila the by the given link
# Third check the download files' extension, if not .mcool extension, you should zoomify the files to get the resolution you want
cooler zoomify --balance filename.cool
# Fourth name the zoomified file to the customized name as bellow
mv fiilename.mcool cell1_name.mcool
# note: you can replace the numerical number as any interger you want, and change the 'name' as you want.If you want to do some prediction based on the pretrained ScHiCEDRN, you can follow the command below:
# The optional parameters are used as the same way in training process.
python ScHiCEDRN_infer.py -g [boolean_value] -e [epoch_number] -b [batch_size] -n [cell_number] -l [cell_line] -p [percentage]If you want to retrain your dataset, YOU can run ScHiCEDRN by the following command:
# First step enter the following folder
cd ./TrainingYourData
# Second step check the envirment whetther it is active, if not active the envirment
conda activate ScHiCEDRN.yml
# Third step run the training scripts
python ScHiCEDRN_train.py -g [boolean_value] -e [epoch_number] -b [batch_size] -n [cell_number] -l [cell_line] -p [percentage]Optional Parameters:
-g, --gan Choose the model you want to use, '1' means gan that you will use ScHiCEDRN gan model to train, '0' indicates you will use ScHiCEDRN to train.
-e, --epoch How many epoches that you want to train.
-b, --batch_size The batch size you want to use in you model.
-n, --celln Cell number in the dataset you want to feed in you model.
-l, --celline Which cell line you want to choose for your dataset, default is 'Human', you should choose one name in ['Human', 'Dros']
-p, --percent The downsampling ratio for the raw dataset, it should be equal or larger than 0.02 but not larger than 1.0, '1.0' means the input data without any downsampling.
This project is covered under the MIT License.
Yanli Wang, Zhiye Guo, & Jianlin Cheng. (2023). Single-cell Hi-C data Enhance-ment with Deep Residual and Generative Adversarial Networks. (Bioinformatics).
Yanli Wang
Deparment of Computer Science
University of Missouri
Columbia, MO 65211, USA
Email: [email protected]
Jianlin (Jack) Cheng, PhD
William and Nancy Thompson Distinguished Professor
Department of Electrical Engineering and Computer Science
University of Missouri
Columbia, MO 65211, USA
Email: [email protected]