Merge pull request #44 from quadbio/docs/readme

Prettify the README a bit.

Author: Marius1311

README.md

@@ -18,38 +18,38 @@
 
 k-NN-based mapping of cells across representations to transfer labels, embeddings and expression values. Works for millions of cells, on CPU and GPU, across molecular modalities, between spatial and non-spatial data, for arbitrary query and reference datasets. Using [faiss][] to compute k-NN graphs, CellMapper takes about 30 seconds to transfer cell type labels from 1.5M cells to 1.5M cells on a single RTX 4090 with 60 GB CPU memory.
 
-Inspired by previous tools, including scanpy's [ingest][] and the [HNOCA-tools][] packages. Check out the [docs][] to learn more, in particular our [tutorials][].
+Inspired by previous tools, including scanpy's [ingest][] and the [HNOCA-tools][] packages. Check out the 📚 [docs][] to learn more, in particular our [tutorials][].
 
-## Key use cases
+## ✨ Key use cases
 
-- Transfer cell type labels and expression values from dissociated to spatial datasets.
-- Transfer embeddings between arbitrary query and reference datasets.
-- Compute presence scores for query datasets in large reference atlasses.
-- Identify niches in spatial datasets by contextualizing latent spaces in spatial coordinates.
-- Evaluate the results of transferring labels, embeddings and feature spaces using a variety of metrics.
+- 🧬 Transfer cell type labels and expression values from dissociated to spatial datasets.
+- ↔️ Transfer embeddings between arbitrary query and reference datasets.
+- 📊 Compute presence scores for query datasets in large reference atlasses.
+- 🗺️ Identify niches in spatial datasets by contextualizing latent spaces in spatial coordinates.
+- 📈 Evaluate the results of transferring labels, embeddings and feature spaces using a variety of metrics.
 
 The core idea of `CellMapper` is to separate the method (k-NN graph with some kernel applied to get a mapping matrix) from the application (mapping across arbitrary representations), to be flexible and fast. The tool currently supports [pynndescent][], [sklearn][], [faiss][] and [rapids][] for neighborhood search, implements a variety of graph kernels, and is closely integrated with `AnnData` objects.
 
-## Installation
+## 📦 Installation
 
-You need to have Python 3.10 or newer installed on your system.
+You need to have 🐍 Python 3.10 or newer installed on your system.
 If you don't have Python installed, we recommend installing [uv][].
 
 There are two alternative options to install ``cellmapper``:
 
-- Install the latest release from [PyPI][]:
+- 🚀 Install the latest release from [PyPI][]:
 
   ```bash
   pip install cellmapper
   ```
 
-- Install the latest development version:
+- 🛠️ Install the latest development version:
 
   ```bash
   pip install git+https://github.com/quadbio/cellmapper.git@main
   ```
 
-## Getting started
+## 🏁 Getting started
 
 This package assumes that you have ``query`` and ``reference`` AnnData objects, with a joint embedding computed and stored in ``.obsm``. While we implement some baseline approaches to compute joint embeddings (PCA and a fast reimplementation of CCA), we typically expect you to provide a pre-computed joint embedding from some task-specific representation learning tools, e.g. [GimVI][] or [ENVI][] for spatial mapping, [GLUE][], [MIDAS][] and [MOFA+][] for modality translation, and [scVI][], [scANVI][] and [scArches][] for query-to-reference mapping - this is just a small selection!
 
@@ -64,17 +64,17 @@ cmap = CellMapper(query, reference).map(
 
 This will transfer data from the reference to the query dataset, including celltype labels stored in ``reference.obs``, a UMAP embedding stored in ``reference.obsm``, and expression values stored in ``reference.X``.
 
-There are many ways to customize this, e.g. use different ways to compute k-NN graphs and to turn them into mapping matrices, and we implement a few methods to evaluate whether your k-NN transfer was sucessful. The tool also implements a `self-mapping` mode (only a query object, no reference), which is useful for spatial contextualization and data denoising. Check out the [docs][] to learn more.
+There are many ways to customize this, e.g. use different ways to compute k-NN graphs and to turn them into mapping matrices, and we implement a few methods to evaluate whether your k-NN transfer was sucessful. The tool also implements a `self-mapping` mode (only a query object, no reference), which is useful for spatial contextualization and data denoising. Check out the 📚 [docs][] to learn more.
 
-## Release notes
+## 📝 Release notes
 
 See the [changelog][].
 
-## Contact
+## 📬 Contact
 
 If you found a bug, please use the [issue tracker][].
 
-## Citation
+## 📖 Citation
 Please use our [zenodo][] entry to cite this software.
 
 [uv]: https://github.com/astral-sh/uv