Update installation manual

README.md

@@ -89,7 +89,7 @@ To set up a conda environment, the following instructions must be followed.
     ```
     install.packages(c('devtools', 'gam', 'RColorBrewer', 'BiocManager'))
     update.packages(ask=F)
-    BiocManager::install(c("scran","MAST","monocle","ComplexHeatmap","slingshot"), version = "3.8")
+    BiocManager::install(c("scran","MAST","monocle","ComplexHeatmap","slingshot", "DropletUtils"), version = "3.15")
     ```
 
 These steps should set up an environment to perform single cell analysis with the tutorial workflow on a Linux system. Please note that we have encountered issues with conda environments on Mac OS. When using Mac OS we recommend installing the packages without conda using separately installed `python` and `R` versions. Alternatively, you can try using the base conda environment and installing all packages as described in the `conda_env_instructions_for_mac.txt` file. In the base environment, R should be able to find the relevant gsl libraries, so `LDFLAGS` and `CFLAGS` should not need to be set.
@@ -148,7 +148,7 @@ General:
 - Jupyter notebook
 - IRKernel
 - rpy2
-- R >= 3.4.3
+- R >= 4.0
 - Python >= 3.5
 
 Python:
@@ -199,4 +199,4 @@ Ensure that the correct g:profiler package is used for the notebook. Notebooks u
 
 #### If not R packages can be found:
 
-Ensure that IRkernel has linked the correct version of R with your jupyter notebook. Check instructions at `https://github.com/IRkernel/IRkernel`.
+Ensure that IRkernel has linked the correct version of R with your jupyter notebook. Check instructions at `https://github.com/IRkernel/IRkernel`.

conda_env_instructions_for_mac.md

@@ -1,19 +1,37 @@
-# 1. Install miniconda or anaconda with python version 3.6 or above.
+# 1. Install miniconda or anaconda with python version 3.8 or above.
 
 # 2. Run the following commands for conda and pip
 
     conda update --all
     conda install r-essentials r-rgl
     conda install pandas numpy scipy scikit-learn jupyter gsl tzlocal simplegeneric natsort h5py tqdm patsy llvmlite numba networkx joblib numexpr pytables seaborn statsmodels pip
-    conda install -c conda-forge python-igraph louvain
+    conda install -c conda-forge python-igraph louvain leidenalg
 
     pip install --upgrade pip
-    pip install MulticoreTSNE anndata anndata2ri fa2 gprofiler-official scanpy rpy2
+    pip install MulticoreTSNE anndata anndata2ri fa2 gprofiler-official scanpy rpy2 scrublet
 
 `MulticoreTSNE` is optional and just for speed, so leave it out if you run into problems installing it.
 
+
 # 3. Start R and run the following commands:
 
     install.packages(c('devtools', 'gam', 'RColorBrewer', 'BiocManager'))
     update.packages(ask=F)
-    BiocManager::install(c("scran","MAST","monocle","ComplexHeatmap","slingshot"), version = "3.8")
+    BiocManager::install(c("scran","MAST","monocle","ComplexHeatmap","slingshot", "DropletUtils"), version = "3.15")
+
+The version of the `BiocManager` may depend on the installed R version (here `R>=4.2`).  
+
+# Troubleshooting
+
+## MulticoreTSNE
+A current workaround (as of 23/09/2022) to install `MulticoreTSNE` is to clone the `git` repository, adjust the `setup.py` and install manually as follows (solution provided by [Lisa Barros](https://github.com/lisa-sousa)):
+
+```
+git clone https://github.com/DmitryUlyanov/Multicore-TSNE.git
+```
+Then open the `setup.py` and replace `self.cmake_args or "--",` by `self.cmake_args or "",`. 
+
+Then install via
+```
+python setup.py install
+```

sc_tutorial_environment.yml

@@ -3,7 +3,7 @@ channels:
  - conda-forge
  - bioconda
 dependencies:
- - python>=3.5, <3.7
+ - python>=3.8
  - cmake>=3.9, <3.11
  - r-essentials
  - r-rgl
@@ -38,6 +38,6 @@ dependencies:
    - fa2
    - gprofiler-official
    - scanpy
-   - rpy2
+   - rpy2==3.4.2
    - git+https://github.com/flying-sheep/anndata2ri