diff --git a/README.md b/README.md
index 5c923eb..de81e5b 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 [badge-tests]: https://github.com/quadbio/cellmapper/actions/workflows/test.yaml/badge.svg
 [badge-docs]: https://img.shields.io/readthedocs/cellmapper
 
-k-NN-based mapping of cells across representations to tranfer 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.
+k-NN-based mapping of cells across representations to tranfer 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.
 
 ## Getting started
 
@@ -40,7 +40,6 @@ See the [changelog][].
 
 ## Contact
 
-For questions and help requests, you can reach out in the [scverse discourse][].
 If you found a bug, please use the [issue tracker][].
 
 ## Citation
@@ -48,7 +47,6 @@ If you found a bug, please use the [issue tracker][].
 Please cite this GitHub repo if you find CellMapper useful for your research.
 
 [uv]: https://github.com/astral-sh/uv
-[scverse discourse]: https://discourse.scverse.org/
 [issue tracker]: https://github.com/quadbio/cellmapper/issues
 [tests]: https://github.com/quadbio/cellmapper/actions/workflows/test.yaml
 [documentation]: https://cellmapper.readthedocs.io
diff --git a/src/cellmapper/cellmapper.py b/src/cellmapper/cellmapper.py
index 75a747b..702dbe7 100644
--- a/src/cellmapper/cellmapper.py
+++ b/src/cellmapper/cellmapper.py
@@ -235,14 +235,14 @@ def compute_mappping_matrix(
 
         Parameters
         ----------
-        n_neighbors
-            Number of nearest neighbors.
-        use_rep
-            Data representation based on which to find nearest neighbors.
-        use_rapids
-            Whether to use cuML and cuPy for GPU-accelerated nearest neighbor search.
-        batch_size
-            Batch size for processing rows during Jaccard computation.
+        method
+            Method to use for computing the mapping matrix. Options include:
+            - "jaccard": Jaccard similarity. Inspired by GLUE: Cao et al., Nature Biotechnology, 2022: https://www.nature.com/articles/s41587-022-01284-4
+            - "gaussian": Gaussian kernel with adaptive bandwith. Loosely inspired by MAGIC: Van Dijk et al., Cell, 2018: https://www.sciencedirect.com/science/article/pii/S0092867418307244?via%3Dihub
+            - "scarches": scArches kernel. Inspired by scArches: Lotfollahi et al., Nature Biotechnology, 2021: https://www.nature.com/articles/s41587-021-01001-7
+            - "inverse_distance": Inverse distance kernel.
+            - "random": Random kernel, useful for testing.
+            - "hnoca": HNOCA kernel. Inspired by HNOCA-tools: He et al., Nature 2024: https://www.nature.com/articles/s41586-024-08172-8
 
         Returns
         -------