DeepCNA

DeepCNA is a computational method for detecting single-cell copy number alterations from scDNA-seq data.

Requirements

• Python 3.8+.

Installation

Clone repository

First, download DeepCNA from github and change to the directory:

git clone https://github.com/zhyu-lab/deepcna
cd deepcna

Create conda environment (optional)

Create a new environment named "deepcna":

conda create --name deepcna python=3.8.15

Then activate it:

conda activate deepcna

Install requirements

To successfully compile the source code, please make sure g++-5 is installed on your machine. you can install it using following commands:

sudo apt install gcc-5 g++-5
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 20  
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 20

If your current compiler is not g++-5, you need to change the current compiler to gcc-5 and g++-5:

sudo update-alternatives --config gcc
sudo update-alternatives --config g++

Then install related packages and compile the code:

python -m pip install -r ./detectbp/requirements.txt
pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html
cd prep
cmake .
make
cd ..
chmod +x run_deepcna.sh ./callcna/run_callcna.sh ./callcna/callcna

If above version of PyTorch does not match the CUDA version, you can find version dependence between PyTorch and CUDA at https://pytorch.org/get-started/previous-versions/, and select appropriate PyTorch version to install on your machine.

After the installation completes, you can reset the compiler using same commands:

sudo update-alternatives --config gcc
sudo update-alternatives --config g++

Usage

Step 1: prepare input data

We use same pipeline as used in rcCAE to prepare the input data, please refer to rcCAE for detailed instructions.

Step 2: train the autoencoder model

The “./detectbp/train.py” Python script is used to learn latent representations of bins and infer breakpoints.

The arguments to run “./detectbp/train.py” are as follows:

Parameter	Description	Possible values
--input	input file generated by “./prep/bin/prepInput” command	Ex: /path/to/example.txt
--output	a directory to save results	Ex: /path/to/results
--epochs	number of epoches to train the AE	Ex: epochs=200 default:500
--batch_size	batch size	Ex: batch_size=64 default:256
--lr	learning rate	Ex: lr=0.0005 default:0.0001
--latent_dim	latent dimensionality	Ex: latent_dim=1 default:2
--min_size	minimum size of segments	Ex: min_size=5 default:3
--seed	random seed	Ex: seed=1 default:0

Example:

python ./detectbp/train.py --input ./data/example.txt --epochs 500 --batch_size 256 --lr 0.0001 --latent_dim 1 --seed 0 --output data

Step 3: detect single-cell CNAs

The “./callcna/callcna.m” MATLAB script is used to call single-cell copy numbers.

The arguments run “./callcna/callcna.m” are as follows:

Parameter	Description	Possible values
lrcFile	“lrc.txt” file generated by “./detectbp/train.py” script	Ex: /path/to/lrc.txt
segFile	seg.txt” file generated by “./detectbp/train.py” script	Ex: /path/to/seg.txt
outputDir	a directory to save results	Ex: /path/to/results
maxCN	maximum copy number to consider	Ex: 6 default:10

Example:

callcna('../data/lrc_example.txt','../data/seg_example.txt','../data',10)

We also provide a script “run_deepcna.sh” to integrate all three steps to run DeepCNA. This script requires that MATLAB Compiler Runtime (MCR) v91 (R2016b) is installed in user's machine. The MCR can be downloaded from MathWorks Web site.

Example:

./run_deepcna.sh /path/to/bam /path/to/ref.fa /path/to/ref.bw /path/to/barcodes.txt /path/to/results

Type ./run_deepcna.sh to learn details about how to use this script.

Contact

If you have any questions, please contact lfr_nxu@163.com.