README.md

# 论文
CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning
https://onlinelibrary.wiley.com/doi/10.1002/advs.202308115



# 模型结构
CIRI-deep模型可有效用于各转录组样本间推断差异剪接环形RNA，拓展了环形RNA的研究范围，为环形RNA研究提供了新的高效分析方法。同时，CIRI-deepA模型可以提供单细胞及空间水平环形RNA的有效解析，

![img](./images/image.png)



# 算法原理
CIRI deep通过深度神经网络对circRNA的顺式特征和样本对（总RNA或富含poly（A）的RNA）的RBP表达进行训练。

![Alt text](./images/image-1.png)


# 环境配置
## Docker（方法一）

```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
docker run -dit --shm-size 80g --network=host --name=CIRI --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10    /bin/bash
docker exec -it CIRI /bin/bash
```

安装依赖

```
pip install ./whl/tensorflow-1.15.1+git06e2e8aa.dtk2404-cp37-cp37m-linux_x86_64.whl
pip install -r requirements.txt  -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```



## Dockerfile（方法二）

```
docker build -t geneformer:latest .
docker run -dit --shm-size 80g --network=host --name=geneformer --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro geneformer:latest /bin/bash
docker exec -it CIRI /bin/bash
```

## anaconda

1.创建conda虚拟环境：

```
conda create -n  CIRI  python=3.7
conda activate CIRI 
```

2.其它依赖库参照requirements.txt安装：

```
pip install ./whl/tensorflow-1.15.1+git06e2e8aa.dtk2404-cp37-cp37m-linux_x86_64.whl
pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
```


# 预测


## 用CIRI-deep进行预测
```
python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax_totalRNA.tsv -seqFeature ./demo/cisfeature.tsv -splicing_max ./demo/splicingamount_max.tsv -predict_list ./demo/predict_list.txt -model_path ./models/CIRIdeep.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_total -splicing_dir ./demo/splicingamount

```

输出文件在outputdir下

![Alt text](./images/image3.png)



## 用CIRI-deepA进行预测

```
python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax_polyA.tsv -seqFeature ./demo/cisfeature.tsv -predict_list ./demo/predict_list.txt -model_path ./models/CIRIdeepA.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_polyA --CIRIdeepA
```

输出文件在outputdir下

![Alt text](./images/image4.png)




# 应用场景


## 算法类别
ai for science

## 行业 
科研

##  医疗  
科研  高校


# 源码仓库及问题反馈

```
git@developer.sourcefind.cn:modelzoo/cirideep.git
```

# 参考资料

```
git clone https://github.com/gyjames/CIRIdeep.git
```