diff --git a/README.md b/README.md
index e97a693..201d8f7 100644
--- a/README.md
+++ b/README.md
@@ -58,16 +58,16 @@ sc.tl.leiden(adata, resolution=0.5)
 ```
 ### Multi-sample support
 
-If you have multiple samples in `adata.obs['sample']`, you can provide the key `sample` to `npc.wf.nichepca` this uses harmony by default:
+If you have multiple samples in `adata.obs["sample"]`, you can provide the key `sample` to `npc.wf.nichepca` this uses harmony by default:
 
 ```python
 npc.wf.nichepca(adata, knn=25, sample_key="sample")
 ```
 
-If you have cell type labels in `adata.obs['cell_type']`, you can directly provide them to `nichepca` as follows (we found this sometimes works better for multi-sample domain identification). However, in this case we need to run `npc.cl.leiden_unique` to handle potential duplicate embeddings:
+If you have cell type labels in `adata.obs["cell_type"]`, you can directly provide them to `nichepca` as follows (we found this sometimes works better for multi-sample domain identification). However, in this case we need to run `npc.cl.leiden_unique` to handle potential duplicate embeddings:
 
 ```python
-npc.wf.nichepca(adata, knn=25, obs_key='cell_type', sample_key="sample")
+npc.wf.nichepca(adata, knn=25, obs_key="cell_type", sample_key="sample")
 npc.cl.leiden_unique(adata, use_rep="X_npca", resolution=0.5, n_neighbors=15)
 ```
 
diff --git a/docs/api.md b/docs/api.md
index 4b34928..f26e30c 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -1,14 +1,65 @@
 # API
 
+## Workflows
+
 ```{eval-rst}
 .. autosummary::
     :toctree: generated
-    :recursive:
-    :nosignatures:
 
     nichepca.workflows
+    nichepca.workflows.nichepca
+```
+
+## Clustering
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+
     nichepca.clustering
+    nichepca.clustering.leiden_unique
+    nichepca.clustering.leiden_multires
+    nichepca.clustering.leiden_with_nclusters
+```
+
+## Graph Construction
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+
     nichepca.graph_construction
+    nichepca.graph_construction.knn_graph
+    nichepca.graph_construction.delaunay_graph
+    nichepca.graph_construction.distance_graph
+    nichepca.graph_construction.construct_multi_sample_graph
+    nichepca.graph_construction.from_squidpy
+    nichepca.graph_construction.to_squidpy
+    nichepca.graph_construction.remove_long_links
+    nichepca.graph_construction.calc_graph_stats
+    nichepca.graph_construction.print_graph_stats
+    nichepca.graph_construction.resolve_graph_constructor
+```
+
+## Neighborhood Embedding
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+
     nichepca.nhood_embedding
+    nichepca.nhood_embedding.aggregate
+```
+
+## Utilities
+
+```{eval-rst}
+.. autosummary::
+    :toctree: generated
+
     nichepca.utils
+    nichepca.utils.check_for_raw_counts
+    nichepca.utils.normalize_per_sample
+    nichepca.utils.to_numpy
+    nichepca.utils.to_torch
 ```