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-# scDEC
-
-[![DOI](https://zenodo.org/badge/286327774.svg)](https://zenodo.org/badge/latestdoi/286327774)
-
-![model](https://github.com/kimmo1019/scDEC/blob/master/model.png)
-
-scDEC is a computational tool for single cell ATAC-seq data analysis with deep generative neural networks. scDEC enables simultaneously learning the deep embedding and clustering of the cells in an unsupervised manner. scDEC is also applicable to multi-modal single cell data. We tested it on the PBMC paired data (scRNA-seq and scATAC-seq) from 10x Genomics (see Tutorials).
-
-## Recent News
-
-An modified version of scDEC won the first place in [NeurIPS 2021 Multimodal Single-Cell Data Integration competition](https://openproblems.bio/neurips_2021/) two Joint Embedding tasks.
-
-## Requirements
-- TensorFlow==1.13.1
-- Scikit-learn==0.19.0
-- Python==2.7
-
-## Installation
-Download scDEC by
-```shell
-git clone https://github.com/kimmo1019/scDEC
-```
-Installation has been tested in a Linux platform with Python2.7. GPU is recommended for accelerating the training process.
-
-## Instructions
-
-This section provides instructions on how to run scDEC with scATAC-seq datasets. One can also refer to [Codeocean platform](https://codeocean.com/capsule/0746056) and click `Reproducible Run` on the right. The embedding and clustering results of several datasets will be shown on the right panel.
-
-### Data preparation
-
-Several scATAC-seq datasets have been prepared as the input of scDEC model. These datasets can be downloaded from the [zenode repository](https://zenodo.org/record/3984189#.XzDpJRNKhTY). Uncompress the `datasets.tar.gz` in `datasets` folder then each dataset will have its own subfolder. Each dataset will contain two major files, which denote raw read count matrix (`sc_mat.txt`) and cell label (`label.txt`), respectively. The first column of `sc_mat.txt` represents the peaks information.
-
-### Model training
-
-scDEC is an unsupervised learning model for analyzing scATAC-seq data. One can run 
-
-```python
-python main_clustering.py --data [dataset] --K [nb_of_clusters] --dx [x_dim] --dy [y_dim] --train [is_train]
-[dataset]  -  the name of the dataset (e.g.,Splenocyte)
-[nb_of_clusters]  -  the number of clusters (e.g., 6)
-[x_dim]  -  the dimension of Gaussian distribution
-[y_dim]  -  the dimension of PCA (defalt: 20)
-[is_train] - indicate training from scratch or using pretrained model
-
-```
-For an example, one can run `CUDA_VISIBLE_DEVICES=0 python main_clustering.py  --data Splenocyte --K 12 --dx 8 --dy 20` to cluster the scATAC-seq data with pretrained model. Note that the dimension of the embedding should be `K+x_dim`
-
-Or one can run `CUDA_VISIBLE_DEVICES=0 python main_clustering.py  --data Splenocyte --K 12 --dx 8 --dy 20 --train True` to train the model from scratch.
-
-### Model evaluation
-
-If the pretrained model was used, the clustering results in the last step will be directly saved in `results/[dataset]/data_pre.npz` where `dataset` is the name of the scATAC-seq dataset. Note that `data_pre.npz` or `data_at_xxx.npz` contains the predictions from the H network. The first part denotes the embeddings and the second part denotes the inferred one-hot label where one can use `np.argmax` function to get the cluster label.
-
-Then one can run `python eval.py --data [dataset]` to analyze the clustering results. 
-For an example, one can run `python eval.py --data Splenocyte`
-
-The t-SNE visualization plot of latent features (`scDEC_embedding.png`), latent feature matrix (`scDEC_embedding.csv`), inferred cluster label (`scDEC_cluster.txt`) will be saved in the `results/[dataset]` folder.
-
-
-If scDEC model was trained from scratch, the results will be marked by a unique timestamp YYYYMMDD_HHMMSS. This timestamp records the exact time when you run the script. The outputs from the training includes:
-
- 1) `log` files and predicted assignmemnts `data_at_xxx.npz` (xxx denotes different epoch) can be found at folder `results/[dataset]/YYYYMMDD_HHMMSS_x_dim=8_y_dim=20_alpha=10.0_beta=10.0_ratio=0.2`.
- 
- 2) Model weights will be saved at folder `checkpoint/YYYYMMDD_HHMMSS_x_dim=8_y_dim=20_alpha=10.0_beta=10.0_ratio=0.2`. 
- 
- 3) The training loss curves were recorded at folder `graph/YYYYMMDD_HHMMSS_x_dim=8_y_dim=20_alpha=10.0_beta=10.0_ratio=0.2`, which can be visualized using TensorBoard.
-
- Next, one can run 
- 
-```python
-python eval.py --data [dataset] --timestamp [timestamp] --epoch [epoch] --train [is_train]
-[dataset]  -  the name of the dataset (e.g.,Splenocyte)
-[timestamp]  -  the timestamp of the experiment you ran
-[epoch]  -  specify to use the results of which epoch (it can be ignored)
-[is_train] - indicate training from scratch 
-```
-
-E.g., `python eval.py --data All_blood --timestamp 20200910_143208 --train True`
-
-The t-SNE visualization plot of latent features (`scDEC_embedding.png`), latent feature matrix (`scDEC_embedding.csv`), inferred cluster label (`scDEC_cluster.txt`) will be saved in the same `results` folder as 1).
-
-
-### Analyzing scATAC dataset without label
-
-One can also use scDEC to analyze custome scATAC-seq dataset, especially the label is unknown. First, the users should prepare raw read count matrix (`sc_mat.txt`) under the folder `datasets/[NAME]`. `[NAME]` denotes the dataset name. 
-
-Second, one can run the following command:
-
-```python
-python main_clustering.py --data [dataset] --K [nb_of_clusters] --dx [x_dim] --dy [y_dim] --train [is_train] --no_label
-[dataset]  -  the name of the dataset (e.g.,Mydataset)
-[nb_of_clusters]  -  the number of clusters (e.g., 6)
-[x_dim]  -  the dimension of latent space (continous part)
-[y_dim]  -  the dimension of PCA (defalt: 20)
-[is_train] - indicate training from scratch 
-```
-
-For an example, one can run `CUDA_VISIBLE_DEVICES=0 python main_clustering.py  --data Mydataset --K 10 --dx 5 --dy 20 --train True --no_label` to clustering custom dataset.
-
-Then one can run `python eval.py --data Mydataset --timestamp YYYYMMDD_HHMMSS --epoch epoch --no_label`. Nota time the timestamp `YYYYMMDD_HHMMSS` (for training) and epoch/batch index `epoch` (the last training epoch/batch index is recommended) should be provided. The clustering results (cluster assignments) will be saved in the `results/Mydataset/YYYYMMDD_HHMMSS_xxx` folder.
-
-
-
-## Tutorial
-
-[Tutorial Splenocyte](https://github.com/kimmo1019/scDEC/wiki/Splenocyte) Run scDEC on Splenocyte dataset (3166 cells)
-
-[Tutorial Full mouse atlas](https://github.com/kimmo1019/scDEC/wiki/Full-Mouse-atlas) Run scDEC on full Mouse atlas dataset (81173 cells)
-
-[Tutorial PBMC10k paired data ](https://github.com/kimmo1019/scDEC/wiki/PBMC10k) Run scDEC on PBMC data, which contains around 10k cells with both scRNA-seq and scATAC-seq (labels were manually annotated from 10x Genomic R&D group)
- 
-## Contact
-
-Also Feel free to open an issue in Github or contact `liuqiao@stanford.edu` if you have any problem in running scDEC.
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE.md file for details
+# scDEC
+
+[![DOI](https://zenodo.org/badge/286327774.svg)](https://zenodo.org/badge/latestdoi/286327774)
+
+![model](https://github.com/kimmo1019/scDEC/blob/master/model.png?raw=true)
+
+scDEC is a computational tool for single cell ATAC-seq data analysis with deep generative neural networks. scDEC enables simultaneously learning the deep embedding and clustering of the cells in an unsupervised manner. scDEC is also applicable to multi-modal single cell data. We tested it on the PBMC paired data (scRNA-seq and scATAC-seq) from 10x Genomics (see Tutorials).
+
+## Recent News
+
+An modified version of scDEC won the first place in [NeurIPS 2021 Multimodal Single-Cell Data Integration competition](https://openproblems.bio/neurips_2021/) two Joint Embedding tasks.
+
+## Requirements
+- TensorFlow==1.13.1
+- Scikit-learn==0.19.0
+- Python==2.7
+
+## Installation
+Download scDEC by
+```shell
+git clone https://github.com/kimmo1019/scDEC
+```
+Installation has been tested in a Linux platform with Python2.7. GPU is recommended for accelerating the training process.
+
+## Instructions
+
+This section provides instructions on how to run scDEC with scATAC-seq datasets. One can also refer to [Codeocean platform](https://codeocean.com/capsule/0746056) and click `Reproducible Run` on the right. The embedding and clustering results of several datasets will be shown on the right panel.
+
+### Data preparation
+
+Several scATAC-seq datasets have been prepared as the input of scDEC model. These datasets can be downloaded from the [zenode repository](https://zenodo.org/record/3984189#.XzDpJRNKhTY). Uncompress the `datasets.tar.gz` in `datasets` folder then each dataset will have its own subfolder. Each dataset will contain two major files, which denote raw read count matrix (`sc_mat.txt`) and cell label (`label.txt`), respectively. The first column of `sc_mat.txt` represents the peaks information.
+
+### Model training
+
+scDEC is an unsupervised learning model for analyzing scATAC-seq data. One can run 
+
+```python
+python main_clustering.py --data [dataset] --K [nb_of_clusters] --dx [x_dim] --dy [y_dim] --train [is_train]
+[dataset]  -  the name of the dataset (e.g.,Splenocyte)
+[nb_of_clusters]  -  the number of clusters (e.g., 6)
+[x_dim]  -  the dimension of Gaussian distribution
+[y_dim]  -  the dimension of PCA (defalt: 20)
+[is_train] - indicate training from scratch or using pretrained model
+
+```
+For an example, one can run `CUDA_VISIBLE_DEVICES=0 python main_clustering.py  --data Splenocyte --K 12 --dx 8 --dy 20` to cluster the scATAC-seq data with pretrained model. Note that the dimension of the embedding should be `K+x_dim`
+
+Or one can run `CUDA_VISIBLE_DEVICES=0 python main_clustering.py  --data Splenocyte --K 12 --dx 8 --dy 20 --train True` to train the model from scratch.
+
+### Model evaluation
+
+If the pretrained model was used, the clustering results in the last step will be directly saved in `results/[dataset]/data_pre.npz` where `dataset` is the name of the scATAC-seq dataset. Note that `data_pre.npz` or `data_at_xxx.npz` contains the predictions from the H network. The first part denotes the embeddings and the second part denotes the inferred one-hot label where one can use `np.argmax` function to get the cluster label.
+
+Then one can run `python eval.py --data [dataset]` to analyze the clustering results. 
+For an example, one can run `python eval.py --data Splenocyte`
+
+The t-SNE visualization plot of latent features (`scDEC_embedding.png`), latent feature matrix (`scDEC_embedding.csv`), inferred cluster label (`scDEC_cluster.txt`) will be saved in the `results/[dataset]` folder.
+
+
+If scDEC model was trained from scratch, the results will be marked by a unique timestamp YYYYMMDD_HHMMSS. This timestamp records the exact time when you run the script. The outputs from the training includes:
+
+ 1) `log` files and predicted assignmemnts `data_at_xxx.npz` (xxx denotes different epoch) can be found at folder `results/[dataset]/YYYYMMDD_HHMMSS_x_dim=8_y_dim=20_alpha=10.0_beta=10.0_ratio=0.2`.
+ 
+ 2) Model weights will be saved at folder `checkpoint/YYYYMMDD_HHMMSS_x_dim=8_y_dim=20_alpha=10.0_beta=10.0_ratio=0.2`. 
+ 
+ 3) The training loss curves were recorded at folder `graph/YYYYMMDD_HHMMSS_x_dim=8_y_dim=20_alpha=10.0_beta=10.0_ratio=0.2`, which can be visualized using TensorBoard.
+
+ Next, one can run 
+ 
+```python
+python eval.py --data [dataset] --timestamp [timestamp] --epoch [epoch] --train [is_train]
+[dataset]  -  the name of the dataset (e.g.,Splenocyte)
+[timestamp]  -  the timestamp of the experiment you ran
+[epoch]  -  specify to use the results of which epoch (it can be ignored)
+[is_train] - indicate training from scratch 
+```
+
+E.g., `python eval.py --data All_blood --timestamp 20200910_143208 --train True`
+
+The t-SNE visualization plot of latent features (`scDEC_embedding.png`), latent feature matrix (`scDEC_embedding.csv`), inferred cluster label (`scDEC_cluster.txt`) will be saved in the same `results` folder as 1).
+
+
+### Analyzing scATAC dataset without label
+
+One can also use scDEC to analyze custome scATAC-seq dataset, especially the label is unknown. First, the users should prepare raw read count matrix (`sc_mat.txt`) under the folder `datasets/[NAME]`. `[NAME]` denotes the dataset name. 
+
+Second, one can run the following command:
+
+```python
+python main_clustering.py --data [dataset] --K [nb_of_clusters] --dx [x_dim] --dy [y_dim] --train [is_train] --no_label
+[dataset]  -  the name of the dataset (e.g.,Mydataset)
+[nb_of_clusters]  -  the number of clusters (e.g., 6)
+[x_dim]  -  the dimension of latent space (continous part)
+[y_dim]  -  the dimension of PCA (defalt: 20)
+[is_train] - indicate training from scratch 
+```
+
+For an example, one can run `CUDA_VISIBLE_DEVICES=0 python main_clustering.py  --data Mydataset --K 10 --dx 5 --dy 20 --train True --no_label` to clustering custom dataset.
+
+Then one can run `python eval.py --data Mydataset --timestamp YYYYMMDD_HHMMSS --epoch epoch --no_label`. Nota time the timestamp `YYYYMMDD_HHMMSS` (for training) and epoch/batch index `epoch` (the last training epoch/batch index is recommended) should be provided. The clustering results (cluster assignments) will be saved in the `results/Mydataset/YYYYMMDD_HHMMSS_xxx` folder.
+
+
+
+## Tutorial
+
+[Tutorial Splenocyte](https://github.com/kimmo1019/scDEC/wiki/Splenocyte) Run scDEC on Splenocyte dataset (3166 cells)
+
+[Tutorial Full mouse atlas](https://github.com/kimmo1019/scDEC/wiki/Full-Mouse-atlas) Run scDEC on full Mouse atlas dataset (81173 cells)
+
+[Tutorial PBMC10k paired data ](https://github.com/kimmo1019/scDEC/wiki/PBMC10k) Run scDEC on PBMC data, which contains around 10k cells with both scRNA-seq and scATAC-seq (labels were manually annotated from 10x Genomic R&D group)
+ 
+## Contact
+
+Also Feel free to open an issue in Github or contact `liuqiao@stanford.edu` if you have any problem in running scDEC.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE.md file for details