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CHANGELOG.md

@@ -12,4 +12,4 @@ and this project adheres to [Semantic Versioning][].
 
 ### Added
 
--   Basic tool, preprocessing and plotting functions
+- Basic tool, preprocessing and plotting functions

README.md

@@ -39,24 +39,24 @@ CellCharter is able to automatically identify spatial domains and offers a suite
 
 ## Features
 
--   **Identify niches for multiple samples**: By combining the power of scVI and scArches, CellCharter can identify domains for multiple samples simultaneously, even with in presence of batch effects.
--   **Scalability**: CellCharter can handle large datasets with millions of cells and thousands of features. The possibility to run it on GPUs makes it even faster
--   **Flexibility**: CellCharter can be used with different types of spatial omics data, such as spatial transcriptomics, proteomics, epigenomics and multiomics data. The only difference is the method used for dimensionality reduction and batch effect removal.
-    -   Spatial transcriptomics: CellCharter has been tested on [scVI](https://docs.scvi-tools.org/en/stable/api/reference/scvi.model.SCVI.html#scvi.model.SCVI) with Zero-inflated negative binomial distribution.
-    -   Spatial proteomics: CellCharter has been tested on a version of [scArches](https://docs.scarches.org/en/latest/api/models.html#scarches.models.TRVAE), modified to use Mean Squared Error loss instead of the default Negative Binomial loss.
-    -   Spatial epigenomics: CellCharter has been tested on [scVI](https://docs.scvi-tools.org/en/stable/api/reference/scvi.model.SCVI.html#scvi.model.SCVI) with Poisson distribution.
-    -   Spatial multiomics: it's possible to use multi-omics models such as [MultiVI](https://docs.scvi-tools.org/en/stable/api/reference/scvi.model.MULTIVI.html#scvi.model.MULTIVI), or use the concatenation of the results from the different models.
--   **Best candidates for the number of domains**: CellCharter offers a [method to find multiple best candidates](https://cellcharter.readthedocs.io/en/latest/generated/cellcharter.tl.ClusterAutoK.html) for the number of domains, based on the stability of a certain number of domains across multiple runs.
--   **Domain characterization**: CellCharter provides a set of tools to characterize and compare the spatial domains, such as domain proportion, cell type enrichment, (differential) neighborhood enrichment, and domain shape characterization.
+- **Identify niches for multiple samples**: By combining the power of scVI and scArches, CellCharter can identify domains for multiple samples simultaneously, even with in presence of batch effects.
+- **Scalability**: CellCharter can handle large datasets with millions of cells and thousands of features. The possibility to run it on GPUs makes it even faster
+- **Flexibility**: CellCharter can be used with different types of spatial omics data, such as spatial transcriptomics, proteomics, epigenomics and multiomics data. The only difference is the method used for dimensionality reduction and batch effect removal.
+    - Spatial transcriptomics: CellCharter has been tested on [scVI](https://docs.scvi-tools.org/en/stable/api/reference/scvi.model.SCVI.html#scvi.model.SCVI) with Zero-inflated negative binomial distribution.
+    - Spatial proteomics: CellCharter has been tested on a version of [scArches](https://docs.scarches.org/en/latest/api/models.html#scarches.models.TRVAE), modified to use Mean Squared Error loss instead of the default Negative Binomial loss.
+    - Spatial epigenomics: CellCharter has been tested on [scVI](https://docs.scvi-tools.org/en/stable/api/reference/scvi.model.SCVI.html#scvi.model.SCVI) with Poisson distribution.
+    - Spatial multiomics: it's possible to use multi-omics models such as [MultiVI](https://docs.scvi-tools.org/en/stable/api/reference/scvi.model.MULTIVI.html#scvi.model.MULTIVI), or use the concatenation of the results from the different models.
+- **Best candidates for the number of domains**: CellCharter offers a [method to find multiple best candidates](https://cellcharter.readthedocs.io/en/latest/generated/cellcharter.tl.ClusterAutoK.html) for the number of domains, based on the stability of a certain number of domains across multiple runs.
+- **Domain characterization**: CellCharter provides a set of tools to characterize and compare the spatial domains, such as domain proportion, cell type enrichment, (differential) neighborhood enrichment, and domain shape characterization.
 
 Since CellCharter 0.3.0, we moved the implementation of the Gaussian Mixture Model (GMM) from [PyCave](https://github.com/borchero/pycave), not maintained anymore, to [TorchGMM](https://github.com/CSOgroup/torchgmm), a fork of PyCave maintained by the CSOgroup. This change allows us to have a more stable and maintained implementation of GMM that is compatible with the most recent versions of PyTorch.
 
 ## Getting started
 
 Please refer to the [documentation][link-docs]. In particular, the
 
--   [API documentation][link-api].
--   [Tutorials][link-tutorial]
+- [API documentation][link-api].
+- [Tutorials][link-tutorial]
 
 ## Installation
 

docs/contributing.md

@@ -99,11 +99,11 @@ Specify `vX.X.X` as a tag name and create a release. For more information, see [
 
 Please write documentation for new or changed features and use-cases. This project uses [sphinx][] with the following features:
 
--   the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
--   [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension).
--   Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
--   [Sphinx autodoc typehints][], to automatically reference annotated input and output types
--   Citations (like {cite:p}`Virshup_2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
+- the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
+- [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension).
+- Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
+- [Sphinx autodoc typehints][], to automatically reference annotated input and output types
+- Citations (like {cite:p}`Virshup_2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
 
 See the [scanpy developer docs](https://scanpy.readthedocs.io/en/latest/dev/documentation.html) for more information
 on how to write documentation.
@@ -120,10 +120,10 @@ repository.
 
 #### Hints
 
--   If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
-    if you do so can sphinx automatically create a link to the external documentation.
--   If building the documentation fails because of a missing link that is outside your control, you can add an entry to
-    the `nitpick_ignore` list in `docs/conf.py`
+- If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
+  if you do so can sphinx automatically create a link to the external documentation.
+- If building the documentation fails because of a missing link that is outside your control, you can add an entry to
+  the `nitpick_ignore` list in `docs/conf.py`
 
 #### Building the docs locally