readme.md

README.md

@@ -8,49 +8,47 @@ https://doi.org/10.1038/s41587-021-01034-y
 
 Higashi使用超图神经网络来揭示这个构造的超图中的高阶交互模式。Higashi可以为scHi-C制作嵌入物，用于下游分析。Higashi可以输入单细胞Hi-C接触图谱，从而能够以单细胞分辨率详细表征3D基因组特征，如TAD样结构域边界和A/B区分数。
 
-
 ![Alt text](./image/image.png)
 
 
-
-
 # 算法原理
 
 Higashi的关键算法设计是将scHi-C数据转换为超图。这种转化保留了scHi-C接触图谱的单细胞分辨率和3D基因组特征。具体来说，嵌入scHi-C数据的过程现在相当于学习超图的节点嵌入，输入scHi-C接触图就变成了预测超图中缺失的超边。在Higashi，我们使用我们最近开发的Hyper-SAGNN架构22，这是一个通用的超图表示学习框架，专门针对scHi-C分析进行了大量的新开发
 
-
-
 ![Alt text](./image/image-1.png)
 
 
-
 # 环境配置
 Docker(方式一)
 推荐使用docker方式运行，提供拉取的docker镜像：
+
 ```
 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=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 image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10 /bin/bash
-docker exec -it geneformer /bin/bash
+docker run -dit --shm-size 80g --network=host --name=higashi --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 higashi /bin/bash
 ```
 
 安装docker中没有的依赖:
 
 ```
 pip install -r requirements.txt  -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
+python setup.py install
 ```
 
-
-
 Dockerfile(方式二)
 
-
 ```
-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 geneformer /bin/bash
+docker build -t higashi:latest .
+docker run -dit --shm-size 80g --network=host --name=higashi --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 higashi  /bin/bash
 
 ```
+安装docker中没有的依赖:
 
+```
+pip install -r requirements.txt  -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
+python setup.py install
+```
 
 
 Conda(方式三)
@@ -58,8 +56,8 @@ Conda(方式三)
 1.创建conda虚拟环境：
 
 ```
-conda create -n geneformer python=3.10
-conda activate geneformer 
+conda create -n higashi python=3.10
+conda activate higashi 
 ```
 
 2.关于本项目DCU显卡所需的工具包、深度学习库等均可从光合开发者社区下载安装。
@@ -72,152 +70,63 @@ Tips：以上dtk驱动、torch等工具版本需要严格一一对应。
 
 3. 其它依赖库参照requirements.txt安装：
 ```
+python setup.py install
 pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
 ```
 
+# 数据集
+
+```
+mkdir -p /work/magroup/ruochiz/Data/scHiC_collection/ramani
+mkdir -p /work/magroup/ruochiz/Higashi/Temp/ramani
+
+wget -P  /work/magroup/ruochiz/Higashi/ https://mirror.ghproxy.com/https://raw.githubusercontent.com/hanfang/Topsorter/refs/heads/master/data/hg19.chrom.sizes.txt
+wget -P  /work/magroup/ruochiz/Higashi/   https://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/cytoBand.txt.gz
+wget https://drive.google.com/drive/folders/1S0KOMAj60MxQP6mgPV1OKjn_J-lVpzKM?usp=sharing
+
+```
+
+# 测试
+
+##  结合测试数据和Higashi模型生成具备超图分析与接触图嵌入能力的demo
+
+
+```
+python train.py 
+
+```
+
+# 精度
 
+bce:  0.5046, mse:  0.7233,  acc: 86.692 %, pearson: 0.590, spearman: 0.514, elapse: 27.894 s 
 
 
 
+# 应用场景
 
+生物
 
 
 
-# 下载数据
+# 热点应用行业
+
+
+科研  单细胞预测    基因预测
+
+# 源码仓库及问题反馈
 
 ```
-wget https://github.com/hanfang/Topsorter/blob/master/data/hg19.chrom.sizes.txt
-wget https://drive.google.com/drive/folders/1S0KOMAj60MxQP6mgPV1OKjn_J-lVpzKM?usp=sharing
-wget https://hgdownload.cse.ucsc.edu/goldenpath/hg19/database/cytoBand.txt.gz
+
 ```
 
-# 创建环境
 
+# 参考资料
 
 ```
-conda create -n higashi python=3.10
-source activate higashi 
-pip install https://cancon.hpccube.com:65024/directlink/4/pytorch/DAS1.1.1/torch-2.1.0+gitf643949.abi1.dtk2404-cp310-cp310-manylinux_2_31_x86_64.whl
-git clone https://github.com/ma-compbio/Higashi/
-cd Higashi
-python setup.py install
- pip install matplotlib==3.7.3  -i https://pypi.tuna.tsinghua.edu.cn/simple
-```
-安装后环境如下：
-
-```
-asciitree          0.3.3
-bokeh              3.5.1
-click              8.1.7
-contourpy          1.2.1
-cooler             0.9.0
-cycler             0.12.1
-Cython             0.29.24
-cytoolz            0.12.3
-dill               0.3.8
-fbpca              1.0
-filelock           3.15.4
-fonttools          4.53.1
-fsspec             2024.6.1
-h5py               3.11.0
-higashi            0.1.0a0
-importlib_metadata 8.2.0
-Jinja2             3.1.4
-joblib             1.4.2
-kiwisolver         1.4.5
-llvmlite           0.43.0
-MarkupSafe         2.1.5
-matplotlib         3.7.3
-mpmath             1.3.0
-multiprocess       0.70.16
-networkx           3.3
-numba              0.60.0
-numpy              1.23.0
-packaging          24.1
-pandas             1.3.4
-pillow             10.4.0
-pip                24.2
-pyfaidx            0.8.1.2
-pynndescent        0.5.13
-pyparsing          3.1.2
-python-dateutil    2.9.0.post0
-pytz               2024.1
-PyYAML             6.0.2
-scikit-learn       1.5.1
-scipy              1.7.3
-seaborn            0.11.2
-setuptools         72.1.0
-simplejson         3.19.3
-six                1.16.0
-sympy              1.13.2
-threadpoolctl      3.5.0
-toolz              0.12.1
-torch              2.1.0+das1.1.git3ac1bdd.abi1.dtk2404
-tornado            6.4.1
-tqdm               4.66.5
-typing_extensions  4.12.2
-tzdata             2024.1
-umap-learn         0.5.6
-wheel              0.43.0
-xyzservices        2024.6.0
-zipp               3.20.0
-
-```
-
-# 结合测试数据和Higashi模型生成具备超图分析与接触图嵌入能力的demo
-
-```
-from higashi.Higashi_wrapper import *
-config = "/work/magroup/ruochiz/Higashi/config_dir/config_ramani.JSON"     # 修改下载文件的路径，如客户对数据集有指定，则根据客户数据集进行修改
-higashi_model = Higashi(config)
-higashi_model.process_data()
-higashi_model.prep_model()
-higashi_model.train_for_embeddings()
-
-```
-
-
-# 验证单细胞Hi-C数据的超图分析与接触图嵌入能力
-
-```
-higashi_model.train_for_embeddings()
-higashi_model.train_for_imputation_nbr_0()
-higashi_model.impute_no_nbr()
-higashi_model.train_for_imputation_with_nbr()
-higashi_model.impute_with_nbr()
-# Visualize embedding results
-cell_embeddings = higashi_model.fetch_cell_embeddings()
-print (cell_embeddings.shape)
-
-from umap import UMAP
-from sklearn.decomposition import PCA
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-cell_type = higashi_model.label_info['cell type']
-fig = plt.figure(figsize=(14, 5))
-ax = plt.subplot(1, 2, 1)
-vec = PCA(n_components=2).fit_transform(cell_embeddings)
-sns.scatterplot(x=vec[:, 0], y=vec[:, 1], hue=cell_type, ax=ax, s=6, linewidth=0)
-handles, labels = ax.get_legend_handles_labels()
-labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
-ax.legend(handles=handles, labels=labels, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncol=1)
-ax = plt.subplot(1, 2, 2)
-vec = UMAP(n_components=2).fit_transform(cell_embeddings)
-sns.scatterplot(x=vec[:, 0], y=vec[:, 1], hue=cell_type, ax=ax, s=6, linewidth=0)
-handles, labels = ax.get_legend_handles_labels()
-labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
-ax.legend(handles=handles, labels=labels, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncol=1)
-plt.tight_layout()
-plt.show()
-
-```
-
-
-# 参考文档
-
 https://github.com/ma-compbio/Higashi/
 
+```
+
 
 
 

dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-ubuntu20.04-dtk24.04.1-py3.10
+RUN source /opt/dtk-24.04.1/env.sh
+RUN cp /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && echo 'Asia/Shanghai' >/etc/timezone 
+

requirements.txt

 matplotlib==3.7.3
+cytoolz
+asciitree
+multiprocess
+joblib
+threadpoolctl
+pandas==1.3.4
 
 

test.py → train.py

@@ -2,3 +2,6 @@ from higashi.Higashi_wrapper import *
 config = "/work/magroup/ruochiz/Higashi/config_dir/config_ramani.JSON"
 higashi_model = Higashi(config)
 higashi_model.process_data()
+
+
+