add versions

Author: delipouya

README.md

@@ -15,12 +15,12 @@ Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within
 
 ## Installation
 Please make sure to install the following packages **before installing sciRED**:
-numpy, pandas, scanpy, statsmodels, seaborn, umap-learn, matplotlib, scikit-learn, scipy, xgboost, scikit-image, [diptest==0.2.0](https://pypi.org/project/diptest/0.2.0/)
+numpy, pandas, scanpy, statsmodels, seaborn, [umap-learn (>=0.5.6)](https://pypi.org/project/umap-learn/), matplotlib, scikit-learn, scipy, xgboost, [scikit-image] (https://pypi.org/project/scikit-image/), [diptest==0.2.0](https://pypi.org/project/diptest/0.2.0/)
 
 
 ## Tutorial
 
-Follow [tutorial-1](https://github.com/delipouya/sciRED/blob/main/tutorial1_scMixology.ipynb) and [tutorial-2](https://github.com/delipouya/sciRED/blob/main/tutorial2_stimulatedPBMC.ipynb) to learn how to use sciRED. These tutorials introduce the standard processing pipeline and indicate application of sciRED on the scMixlogy and stimulated PBMC datasets.
+Follow [tutorial-1](https://github.com/delipouya/sciRED/blob/main/tutorial1_scMixology.ipynb) and [tutorial-2](https://github.com/delipouya/sciRED/blob/main/tutorial2_stimulatedPBMC.ipynb) to learn how to use sciRED. These tutorials introduce the standard processing pipeline and demonstrate the application of sciRED on the scMixology and stimulated PBMC datasets. Further details about the input datasets are available in the manuscript. The data processing scripts are available in the _data_prep_ folder. 
 
 ## Citation