add readme.md

README.md

+# 下载数据
+
+```
+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
+
+
+```
+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/
+
+
+
+

demo.py

+
+from higashi.Higashi_wrapper import *
+config = "/work/magroup/ruochiz/Higashi/config_dir/config_ramani.JSON"
+higashi_model = Higashi(config)
+higashi_model.process_data()
+higashi_model.prep_model()
+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()
\ No newline at end of file

test.py

+from higashi.Higashi_wrapper import *
+config = "/work/magroup/ruochiz/Higashi/config_dir/config_ramani.JSON"
+higashi_model = Higashi(config)
+higashi_model.process_data()