Updated installation instructions

README.md

@@ -14,7 +14,13 @@ We welcome any contribution to this project! For more information, please check
 
 ## Contributors
 
-Martin Garrido-Rodriguez, Olga Ivanova, Victor Paton, Denes Turei
+- Victor Paton
+- Denes Turei
+- Olga Ivanova
+- Pablo Rodriguez-Mier
+- Sophia Muller-Dot
+- Martin Garrido-Rodriguez
+- Julio Saez-Rodriguez
 
 [badge-cov]: https://codecov.io/github/saezlab/networkcommons/graph/badge.svg?token=RH438ALJC2
 [link-cov]: https://codecov.io/github/saezlab/networkcommons

docs/src/conf.py

@@ -22,6 +22,7 @@
     'sphinx.ext.napoleon',
     'sphinx_autodoc_typehints',
     'sphinx.ext.autosectionlabel',
+    'sphinx_copybutton',
     'numpydoc',
     'nbsphinx',
     'IPython.sphinxext.ipython_console_highlighting'

docs/src/index.rst

@@ -41,7 +41,7 @@ collaborative advancements in network biology.
 
 Mission statement
 =================
-Thirty years ago, microarrays revolutionized the study of biological processes, making the computational analysis of vast molecular data essential for understanding phenotypes systematically. This shift gave rise to **network biology**, a field that has developed numerous approaches based on diverse networks, algorithmic assumptions, and omics data types.
+Thirty years ago, microarrays revolutionized the study of biological processes, making the computational analysis of vast molecular data essential for understanding phenotypes systematically. This shift increased usage of **network biology**, a field that has developed numerous approaches based on diverse networks, algorithmic assumptions, and omics data types.
 At SaezLab, our focus is on large-scale studies of signaling processes, typically represented as directed, and sometimes signed, networks. These networks encode the transmission of molecular activation states among proteins, enabling us to map cellular functions based on data and prior knowledge.
 
 In a  `recent review <https://www.embopress.org/doi/full/10.15252/msb.202211036>`_, we identified several challenges in large-scale modeling of signaling networks, including the **lack of benchmarks** and the need for a **unified technical implementation** comprising data, methods, and evaluation strategies.

docs/src/installation.rst

@@ -2,9 +2,54 @@
 Installation
 ############
 
-``NetworkCommons`` requires ``Python`` version >= 3.9 to run.
+``NetworkCommons`` requires ``Python`` version >= 3.10 to run.
 
+-------------------
+Requirements
+-------------------
+
+
+NetworkCommons requires `graphviz <https://graphviz.gitlab.io/download/>`_ to visualize networks.
+
+Conda
+-------------------
+
+If you are using ``conda``, it can be installed via:
+
+.. code-block:: console
+
+   conda install graphviz
+
+``graphviz`` is also available via the APT package manager in Ubuntu:
+
+Ubuntu
+-------------------
+
+.. code-block:: console
+
+   sudo apt-get install -y graphviz graphviz-dev
+
+MacOS
+-------------------
+
+In MacOS, it can be installed using `Homebrew <https://brew.sh/>`_. 
+
+.. code-block:: console
 
+   brew install graphviz
+
+In some of our local MacOS tests, we also needed to set the following environment variables:
+
+.. code-block:: console
+
+   export PATH="$(brew --prefix graphviz)/bin:$PATH"'
+   export CFLAGS="-I$(brew --prefix graphviz)/include"'
+   export LDFLAGS="-L$(brew --prefix graphviz)/lib"'
+
+Please note, if you are using a different architecture or operating system within a subsystem, you may need to make sure that the installed binaries of graphviz were compiled for the correct architecture and that they are visible to the Python interpreter.
+
+-------------------
+PIP
 -------------------
 
 Currently, NetworkCommons can be installed via pip.

docs/src/requirements.txt

@@ -3,4 +3,5 @@ nbsphinx
 ipython
 skranger
 sphinx_rtd_theme
-sphinx_autodoc_typehints
+sphinx_autodoc_typehints
+sphinx-copybutton