Add Dockerfile, add github action, simplify dependencies,

.github/workflows/build_and_publish.yaml

@@ -0,0 +1,49 @@
+# from https://docs.github.com/en/actions/publishing-packages/publishing-docker-images
+name: Create and publish a Docker image
+
+on:
+  push:
+    branches: ['main']
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=tag
+            type=raw,value={{sha}}
+            type=raw,value=latest,enable=${{ github.ref == format('refs/heads/{0}', 'main') }}
+          flavor: |
+            latest=auto
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@ad44023a93711e3deb337508980b4b5e9bcdc5dc
+        with:
+          context: .
+          file: Dockerfile-base
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

Dockerfile

@@ -0,0 +1 @@
+FROM ghcr.io/nickp60/shunpyker:latest

Dockerfile-base

@@ -0,0 +1,16 @@
+FROM jupyter/base-notebook
+add envs/shunPykeR.yml ./shunPykeR.yml
+add notebooks/shunPykeR_guide.ipynb shunPykeR_guide.ipynb
+# dependency annoy needs to be built, and base-notebook does not have GCC
+USER root
+RUN apt-get update --yes && apt-get install g++ --yes --no-install-recommends &&  rm -rf /var/lib/apt/lists/*
+USER ${NB_UID}
+RUN mamba env create -f shunPykeR.yml
+# inspired by https://medium.com/@nrk25693/how-to-add-your-conda-environment-to-your-jupyter-notebook-in-just-4-steps-abeab8b8d084
+# This adds this env as a dropdown option in jupyter
+RUN mamba install -n shunPykeR -c anaconda ipykernel && mamba install -n base nb_conda_kernels && mamba clean -afy
+# do we need dpeerlab's fork? If not, use pip install magic-impute
+# RUN wget https://github.com/dpeerlab/magic/archive/refs/tags/v0.1.1.tar.gz && \
+#     tar xzf v0.1.1.tar.gz && \
+#     ls && cd magic-0.1.1/ && \
+#     /opt/conda/envs/shunPykeR/bin/pip install .

README.md

@@ -1,4 +1,4 @@
-[![DOI](https://zenodo.org/badge/402799957.svg)](https://zenodo.org/badge/latestdoi/402799957)
+[![DOI](https://zenodo.org/badge/402799957.svg)](https://zenodo.org/badge/latestdoi/402799957)[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/nickp60/shunPykeR/HEAD?labpath=notebooks/shunPykeR_guide.ipynb/)
 # The shunPykeR's guide to single cell analysis
 *Anastasia Kousa and Andri Lemarquis*
 
@@ -12,13 +12,13 @@
 <br/>
 
 <p float="left">
-  <img width="550" src="infographics/shunPykeR_infographic.png"> 
-    
+  <img width="550" src="infographics/shunPykeR_infographic.png">
+
 **GRAPHICAL OVERVIEW**
 
 **Step 1** guides you through the quality control and necessary clean-up tread that scRNA-seq data require. **Steps 2** and **3** are optional and can be skipped if initial inspection of the samples does not reveal any obvious technical artifacts that can bias results interpretation. **Step 4** introduces a plethora of common visualization options. **Step 5** provides again an optional practise to impute gene expression. This can be beneficial when interrogating in a correlative way the expression of lowly expressed genes, such as transcription factors, due to the high dropout rate that single cell data suffer from. **Step 6** walks you through differential expression analysis for your comparisons of interest, advices on pathway and network enrichment analysis options and provides complementary visualization for the specific output files. **Step 7** introduces ways of integrating your scRNA-seq data with (a) loom files to perform RNA velocity analysis, (b) visium files to probe the localization of the expressional data and (c) human/mouse orthologue to associate and inform the analysis from cross-species comparisons.
 
-<sup> * *Magenta denotes optional analysis modules*</sup> 
+<sup> * *Magenta denotes optional analysis modules*</sup>
 
 <br/>
 
@@ -27,7 +27,7 @@
 ##  This guide in a nutshell
 
 
-This is a complete guide for the analysis of scRNA-seq data interlaced with sections of common and complementary practises. This guide introduces all code in one notebook in a coherent order and it has been "seeded" -where necessary- to make it fully reproducible. These two main qualities make it simple and intuitive enough allowing non computational scientists to perform a robust and thorough analysis of their scRNA-seq datasets with (relative) peace of mind. Nonetheless, it can be used by bioinformaticians that may want to adjust and reuse it in their own way. 
+This is a complete guide for the analysis of scRNA-seq data interlaced with sections of common and complementary practises. This guide introduces all code in one notebook in a coherent order and it has been "seeded" -where necessary- to make it fully reproducible. These two main qualities make it simple and intuitive enough allowing non computational scientists to perform a robust and thorough analysis of their scRNA-seq datasets with (relative) peace of mind. Nonetheless, it can be used by bioinformaticians that may want to adjust and reuse it in their own way.
 
 Within this guide we have brought together a combination of already established single cell analysis tools that we introduced in a logical order to simplify running your analysis. The main code is in python but it is interpolated with snippets of R code to take advantage of both worlds when using single cell developed algorithms.
 
@@ -45,10 +45,10 @@ Here is the list of tools that we implement across this guide:
 - **mousipy** integrates human with mouse datasets (https://github.com/stefanpeidli/mousipy)
 
 <br/>
-  
+
 ## Emojis to guide your way
-  
- 
+
+
 This icon (🕹️) helps you move directly to specific sections of this pipeline
 
 This icon (💡) gives you hints to assist with issues when running this pipeline
@@ -58,15 +58,15 @@ This icon (✍️) let's you know that manual input is required
 This icon (❗) asks you to pay attention
 
 This icon (⏳) let's you know that the process may take a long time to run
-  
+
 <br/>
 
 See the **complete notebook** [here](notebooks/shunPykeR_guide.ipynb).
 
 <br/>
 
 
-  
+
 ## How-to guide to open a Jupyter notebook
 
 - For **Linux**, **Mac** and **Windows** environments
@@ -75,7 +75,7 @@ See the **complete notebook** [here](notebooks/shunPykeR_guide.ipynb).
     2. Open the Anaconda-Navigator
     3. Go to `Environments` -> select an environment [default will be ` base(root)`] by clicking on the ▶️ button; select `shunPykeR` instead once you install it below
     4. Go to `Home` again -> on the Jupyter notebook box click on the `Install` button; once installed click on the `Launch` button
-    5. Finally navigate through the browser that will pop up to the directory where your jupyter notebook exists and click on the file  
+    5. Finally navigate through the browser that will pop up to the directory where your jupyter notebook exists and click on the file
 
 
 ## How-to guide to install the shunPykeR environment
@@ -85,7 +85,7 @@ See the **complete notebook** [here](notebooks/shunPykeR_guide.ipynb).
     1. Install conda for your environment (https://docs.conda.io/projects/conda/en/latest/user-guide/install/windows.html)
     2. Use the search button to find and open a `terminal` window
     3. Type the command below
-   
+
     ```
     conda env create -f shunPykeR.yml
     ```
@@ -95,19 +95,29 @@ See the **complete notebook** [here](notebooks/shunPykeR_guide.ipynb).
 
 - Alternatively, in a **Windows** environment or if you prefer a **graphical interface** to facilitate the installation (even though this can end up more tricky and slow)
 
-    1. Open Anaconda-Navigator 
+    1. Open Anaconda-Navigator
     2. Go to Environments -> click on `Import`
     3. Select the `shunPykeR.yml` file and click `Import` again in the new window
 
 <br/>
 
 ❗*Full environment installation has only been tested in a Mac environment via the terminal.*
-  
+
 
 <br/>
 
-
-
+## Using the ShunPykeR docker image
+Using Docker allows you to run ShunPykeR without having to manage any dependencies yourself other than installing Docker.
+
+1. [Install Docker](https://docs.docker.com/get-docker/)
+2. Run the following command in your terminal to download and start the Docker image:
+   ```
+    docker run -it --rm -p 8888:8888 -v "${PWD}":/home/jovyan/work  kousaa/shunpyker
+   ```
+
+3. navigate to the link shown in the console; it start with something like http://127.0.0.1:8888
+4. **change your default kernal.** Once you open the notebook, change the kernal using the dropdown in the top right from `Python [conda_env: root]` to `Python [conda_env:shunPykeR]`
+
 ## Citations
 <sup>**1.** 	F. A. Wolf, P. Angerer, F. J. Theis, SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
 **2.** 	S. L. Wolock, R. Lopez, A. M. Klein, Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Cell Syst. 8, 281-291.e9 (2019).