Merge branch 'dev' of github.com:KrishnaswamyLab/scprep into dev

README.rst

@@ -1,6 +1,5 @@
-=============
-scprep
-=============
+.. image:: logo.png
+    :alt: scprep logo
 
 .. image:: https://img.shields.io/pypi/v/scprep.svg
     :target: https://pypi.org/project/scprep/
@@ -24,8 +23,14 @@ scprep
     :target: https://github.com/KrishnaswamyLab/scprep/
     :alt: GitHub stars
 
+`scprep` provides an all-in-one framework for loading, preprocessing, and plotting matrices in Python, with a focus on single-cell genomics.
 
-Tools for loading and preprocessing biological matrices in Python.
+The philosophy of `scprep`:
+
+* Data shouldn't be hidden in a complex and bespoke class object. `scprep` works with `numpy` arrays, `pandas` data frames, and `scipy` sparse matrices, all of which are popular data formats in Python and accepted as input to most common algorithms.
+* Your analysis pipeline shouldn't have to change based on data format. Changing from a `numpy` array to a `pandas` data frame introduces endless technical differences (e.g. in indexing matrices). `scprep` provides data-agnostic methods that work the same way on all formats.
+* Simple analysis should mean simple code. `scprep` takes care of annoying edge cases and sets nice defaults so you don't have to.
+* Using a framework shouldn't be limiting. Because nothing is hidden from you, you have access to the power of `numpy`, `scipy`, `pandas` and `matplotlib` just as you would if you used them directly.
 
 Installation
 ------------
@@ -72,4 +77,4 @@ Examples
 Help
 ----
 
-If you have any questions or require assistance using scprep, please read the documentation at https://scprep.readthedocs.io/ or contact us at https://krishnaswamylab.org/get-help
+If you have any questions or require assistance using scprep, please read the documentation at https://scprep.readthedocs.io/ or contact us at https://krishnaswamylab.org/get-help

doc/source/index.rst

@@ -1,6 +1,6 @@
-===========================================================================
-scprep
-===========================================================================
+.. raw:: html
+
+    <a href="https://github.com/KrishnaswamyLab/scprep/"><img src="https://raw.githubusercontent.com/KrishnaswamyLab/scprep/dev/logo.png" alt="scprep logo"></a><br>
 
 .. raw:: html
 
@@ -26,7 +26,14 @@ scprep
 
     <a href="https://github.com/KrishnaswamyLab/scprep/"><img src="https://img.shields.io/github/stars/KrishnaswamyLab/scprep.svg?style=social&label=Stars" alt="GitHub stars"></a>
 
-Tools for building and manipulating graphs in Python.
+`scprep` provides an all-in-one framework for loading, preprocessing, and plotting matrices in Python, with a focus on single-cell genomics.
+
+The philosophy of `scprep`:
+
+* Data shouldn't be hidden in a complex and bespoke class object. `scprep` works with `numpy` arrays, `pandas` data frames, and `scipy` sparse matrices, all of which are popular data formats in Python and accepted as input to most common algorithms.
+* Your analysis pipeline shouldn't have to change based on data format. Changing from a `numpy` array to a `pandas` data frame introduces endless technical differences (e.g. in indexing matrices). `scprep` provides data-agnostic methods that work the same way on all formats.
+* Simple analysis should mean simple code. `scprep` takes care of annoying edge cases and sets nice defaults so you don't have to.
+* Using a framework shouldn't be limiting. Because nothing is hidden from you, you have access to the power of `numpy`, `scipy`, `pandas` and `matplotlib` just as you would if you used them directly.
 
 .. toctree::
     :maxdepth: 2
@@ -63,4 +70,4 @@ You can use `scprep` with your single cell data as follows::
 Help
 ====
 
-If you have any questions or require assistance using scprep, please contact us at https://krishnaswamylab.org/get-help
+If you have any questions or require assistance using scprep, please contact us at https://krishnaswamylab.org/get-help