@@ -20,7 +20,7 @@ Conda is used to build the self-contained Python environnment that hosts the fol
-**umap-learn** version **0.5.3**: used for building the umap embedding (instead of the default R package uwot),
-**leidenalg** version **0.9.0 (or later)**: used for applying Leiden clustering algorithm when calling the `Seurat::FindClusters` function.
Note that the conda environnment is only required for running one of the preprocessing scripts: `9396.R`, `WP.R` or `5hAPF.R`. Both the `test.R` and `analysis.R` scripts can be run without installing the conda environnment.
Note that the conda environnment is only required for running one of the preprocessing scripts: `9396.R`, `WP.R` or `5hAPF.R`.
### Steps
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@@ -28,7 +28,6 @@ Note that the conda environnment is only required for running one of the preproc
2. Build the R environnment:
* launch the R project `ClusteringAnalysis.Rproj`,
* download the dependencies with the R command : `renv::restore()`
3. (Optional) Create the conda environnment from the environnment file `seurat.yml`. See [this post](https://igfl.gitbiopages.ens-lyon.fr/websites/OldShack/python/r/2022/09/28/project-local-env.html#environment-file) for further instructions.
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@@ -40,7 +39,7 @@ To perform the clustering analysis on the 9396, White Pupae or 5hAPF datasets :
1. Create a folder named "data" at the project root and a subfolder of "results" where the figures and files will be stored,
2. Copy the dataset in the "data" folder,
3. Two options:
* either run the corresponding preprocessing script to build the ".h5Seurat" object (to do so, you need to have install the remote conda environnment),
* either run the corresponding preprocessing script to build the ".h5Seurat" object,
* or, directly copy the corresponding ".h5Seurat" object in the "results" subfolder.
4. Run the "analysis.R" with the following specifications:
- the variable `savedir` should correctly point to the subfolder created at step 1,
