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IlyaLab · Feb 10, 2024 · 97617f4 · 97617f4
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diff --git a/README.md b/README.md
@@ -4,18 +4,16 @@ Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq
 
 This package works with Scanpy AnnData objects stored as h5ad files.
 
+  *  **[Read the Docs!](https://gssnng.readthedocs.io/en/latest/)**
 
   * **Notebook using gmt files  ===>>>**  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IlyaLab/gssnng/blob/main/notebooks/gssnng_quick_start.ipynb)
 
   * **Notebook using Decoupler/Omnipath style API ===>>>** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IlyaLab/gssnng/blob/main/notebooks/Scoring_PBMC_data_with_the_GSSNNG_decoupleR_API.ipynb)
 
-  * **Notebook for smoothing counts, breaks AnnData into groups.
+  * **Notebook for smoothing counts. COMING SOON! For now, see the example script in test.
 
   * **See the paper ===>>>** [gssnng](https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad150/7321111?login=false)
 
-  * and finally, [Read the Docs!](https://gssnng.readthedocs.io/en/latest/)
-
-
 The GSSNNG method is based on using the nearest neighbor graph of cells for data smoothing. This essentially creates 
 mini-pseudobulk expression profiles for each cell, which can be scored by using single sample gene set scoring 
 methods often associated with bulk RNA-seq. 

diff --git a/docs/decoupler_api_doc.rst b/docs/decoupler_api_doc.rst
@@ -13,14 +13,7 @@ Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq
        :caption: Table of Contents
        :maxdepth: 2
 
-       Installation
-       Example script
-       Usage
-       Scoring Functions
-       Parameters
-       Groupby
-       Gene sets
-       References
+       ___Usage
 
 
 `**Notebook using gmt files**  <https://colab.research.google.com/github/IlyaLab/gssnng/blob/main/notebooks/gssnng_quick_start.ipynb>`_

diff --git a/docs/gmt_files_doc.rst b/docs/gmt_files_doc.rst
@@ -13,14 +13,9 @@ Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq
        :caption: Table of Contents
        :maxdepth: 2
 
-       Installation
-       Example script
-       Usage
-       Scoring Functions
-       Parameters
-       Groupby
-       Gene sets
-       References
+       ___Usage
+       ___Scoring Functions
+       ___Parameters
 
 
 `**Notebook using gmt files**  <https://colab.research.google.com/github/IlyaLab/gssnng/blob/main/notebooks/gssnng_quick_start.ipynb>`_

diff --git a/docs/index.rst b/docs/index.rst
@@ -14,8 +14,8 @@ Contents
 
 .. toctree::
    gssnng with gene set gmt files <gmt_files_doc>
-   gssnng with a decoupler/omnipath style <decoupler_api>
-   gssnng to smooth count matrices <smoothing_adatas>
+   gssnng with a decoupler/omnipath style <decoupler_api_doc>
+   gssnng to smooth count matrices <smoothing_adatas_doc>
 
 The problem: The sparsity of scRNA-seq data creates a poor overlap with many gene sets, which in turn makes gene set scoring difficult.
 The GSSNNG method is based on using the nearest neighbor graph of cells for data smoothing. This essentially creates mini-pseudobulk expression profiles for each cell, which can be scored by using single sample gene set scoring methods often associated with bulk RNA-seq.

diff --git a/docs/smoothing_adatas.rst → docs/smoothing_adatas_doc.rst b/docs/smoothing_adatas.rst → docs/smoothing_adatas_doc.rst
@@ -13,13 +13,8 @@ Gene Set Scoring on the Nearest Neighbor Graph (gssnng) for Single Cell RNA-seq
        :caption: Table of Contents
        :maxdepth: 2
 
-       Installation
-       Example script
-       Usage
-       Parameters
-       Groupby
-       Gene sets
-       References
+       __Usage
+
 
 
 `**Notebook using gmt files**  <https://colab.research.google.com/github/IlyaLab/gssnng/blob/main/notebooks/gssnng_quick_start.ipynb>`_