--- a/README.md
+++ b/README.md
@@ -1,53 +1,53 @@
-# DL-mo
-## A benchmark study of deep learning based multi-omics data fusion methods for cancer
-***
-![Multi-Omics](./img/3.png "Multi-Omics")  
-We here compare the performances of 10 deep learning methods in three contexts: 
-1. Simulated datasets
-2. Cancer datasets
-3. Single-cell datasets       
-
-We use `python` and `R` to code the programs.  The python scripts are in `./python-scripts/` folder .The R scripts are in `./R-scripts/` folder .
-***
-## 16 deep learning methods
-* [lfAE](./python-scripts/runCancerAE2.py)
-* [efAE](./python-scripts/runCancerAE.py) 
-* [lfDAE](./python-scripts/runCancerDAE2.py) 
-* [efDAE](./python-scripts/runCancerDAE.py) 
-* [lfVAE](./python-scripts/runCancerVAE2.py)
-* [efVAE](./python-scripts/runCancerVAE.py)
-* [lfSVAE](./python-scripts/runCancerSVAE2.py)
-* [efSVAE](./python-scripts/runCancerSVAE.py) 
-* [lfmmdVAE](./python-scripts/runCancerMMDVAE2.py) 
-* [efmmdVAE](./python-scripts/runCancerMMDVAE.py) 
-* [lfNN](./python-scripts/runCancerDNN.py) 
-* [efNN](./python-scripts/runCancerDNN.py)
-* [lfCNN](./python-scripts/runCancerCNN.py) 
-* [efCNN](./python-scripts/runCancerCNN.py)
-* [moGCN](./python-scripts/MOGONET/main_mogonet_zly.py)
-* [moGAT](./python-scripts/MOGONET/main_mogonet_zyh.py)
-***
-## Input data
-The data for python scripts is in `./python-scripts/data/` folder .The data for R scripts is in `./R-scripts/data/` folder .    
-For python-scripts,Simulated datasets are in `./python-scripts/data/simulations`,Cancer datasets are in `./python-scripts/data/cancer` ,Single-cell datasets are in `./python-scripts/data/single-cell`.
-***
-## python scripts
-Each of the three datasets above corresponds to a differnet python scripts in this repositiory:
-1. `runSimulations*.py`
-2. `runCancer*.py`
-3. `runSingle*.py`
-***
-## R scripts
-Each of the three datasets above corresponds to a differnet Jupyter notebook in this repositiory:
-1. `simulated*.ipynb`
-2. `cancer*.ipynb`
-3. `single-cell*.ipynb`
-
-***
-## Install the R software environment
-Use conda to create a new environment: `conda create -n momix -c conda-forge -c bioconda -c lcantini momix r-irkernel`
-***
-## Install the python software environment
-You need to build a virtual environment for python.    
-You need to install the following main libraries: `Python==3.7.0,Tensorflow==1.15.0, scikit-learn==0.20.0, Jupyter==1.0.0`.
-
+# DL-mo
+## A benchmark study of deep learning based multi-omics data fusion methods for cancer
+***
+![Multi-Omics](https://easymed.ai/models/AlyssaS/DL-mo/git/ci/main/tree/img/3.png "Multi-Omics")  
+We here compare the performances of 10 deep learning methods in three contexts: 
+1. Simulated datasets
+2. Cancer datasets
+3. Single-cell datasets       
+
+We use `python` and `R` to code the programs.  The python scripts are in `./python-scripts/` folder .The R scripts are in `./R-scripts/` folder .
+***
+## 16 deep learning methods
+* [lfAE](./python-scripts/runCancerAE2.py)
+* [efAE](./python-scripts/runCancerAE.py) 
+* [lfDAE](./python-scripts/runCancerDAE2.py) 
+* [efDAE](./python-scripts/runCancerDAE.py) 
+* [lfVAE](./python-scripts/runCancerVAE2.py)
+* [efVAE](./python-scripts/runCancerVAE.py)
+* [lfSVAE](./python-scripts/runCancerSVAE2.py)
+* [efSVAE](./python-scripts/runCancerSVAE.py) 
+* [lfmmdVAE](./python-scripts/runCancerMMDVAE2.py) 
+* [efmmdVAE](./python-scripts/runCancerMMDVAE.py) 
+* [lfNN](./python-scripts/runCancerDNN.py) 
+* [efNN](./python-scripts/runCancerDNN.py)
+* [lfCNN](./python-scripts/runCancerCNN.py) 
+* [efCNN](./python-scripts/runCancerCNN.py)
+* [moGCN](./python-scripts/MOGONET/main_mogonet_zly.py)
+* [moGAT](./python-scripts/MOGONET/main_mogonet_zyh.py)
+***
+## Input data
+The data for python scripts is in `./python-scripts/data/` folder .The data for R scripts is in `./R-scripts/data/` folder .    
+For python-scripts,Simulated datasets are in `./python-scripts/data/simulations`,Cancer datasets are in `./python-scripts/data/cancer` ,Single-cell datasets are in `./python-scripts/data/single-cell`.
+***
+## python scripts
+Each of the three datasets above corresponds to a differnet python scripts in this repositiory:
+1. `runSimulations*.py`
+2. `runCancer*.py`
+3. `runSingle*.py`
+***
+## R scripts
+Each of the three datasets above corresponds to a differnet Jupyter notebook in this repositiory:
+1. `simulated*.ipynb`
+2. `cancer*.ipynb`
+3. `single-cell*.ipynb`
+
+***
+## Install the R software environment
+Use conda to create a new environment: `conda create -n momix -c conda-forge -c bioconda -c lcantini momix r-irkernel`
+***
+## Install the python software environment
+You need to build a virtual environment for python.    
+You need to install the following main libraries: `Python==3.7.0,Tensorflow==1.15.0, scikit-learn==0.20.0, Jupyter==1.0.0`.
+