diff --git a/bin/st_clustering.qmd b/bin/st_clustering.qmd
index ba26fb9..ccb91ba 100644
--- a/bin/st_clustering.qmd
+++ b/bin/st_clustering.qmd
@@ -11,13 +11,12 @@ jupyter: python3
 ```{python}
 #| tags: [parameters]
 #| echo: false
-
-input_sdata = "st_sdata_filtered.zarr"  # Name of the input anndata file
+input_sdata = "st_sdata_filtered.zarr"  # Input: SpatialData file
 cluster_resolution = 1  # Resolution for Leiden clustering
 n_hvgs = 2000  # Number of HVGs to use for analyses
-artifact_dir = "artifacts"
-output_adata_processed = "st_adata_processed.h5ad"  # Name of the output anndata file
-output_sdata = "st_sdata_processed.zarr"  # Name of the input anndata file
+artifact_dir = "artifacts" # Output directory
+output_sdata = "st_sdata_processed.zarr"  # Output: SpatialData file
+output_adata_processed = "st_adata_processed.h5ad"  # Output: AnnData file
 ```
 
 The data has already been filtered in the _quality controls_ reports and is
@@ -39,9 +38,10 @@ from IPython.display import display, Markdown
 ```
 
 ```{python}
-# Make sure we can use scanpy plots with the AnnData object exported from sdata.table
-# This code is taken from the early version of https://github.com/scverse/spatialdata-io/pull/102/
-# Once the PR will be merged in spatialdata-io, we should use spatialdata_io.to_legacy_anndata(sdata).
+# Make sure we can use scanpy plots with the AnnData object exported from
+# `sdata.table`. This code is taken from the early version of https://github.com/scverse/spatialdata-io/pull/102/
+# Once that PR is merged into spatialdata-io, we should instead use
+# `spatialdata_io.to_legacy_anndata(sdata)`.
 def to_legacy_anndata(sdata: spatialdata.SpatialData) -> AnnData:
     adata = sdata.table
     for dataset_id in adata.uns["spatial"]:
diff --git a/bin/st_quality_controls.qmd b/bin/st_quality_controls.qmd
index 125beb2..ecd19a0 100644
--- a/bin/st_quality_controls.qmd
+++ b/bin/st_quality_controls.qmd
@@ -26,7 +26,7 @@ analysis tools and facilitates seamless integration into existing workflows.
 ```{python}
 #| tags: [parameters]
 #| echo: false
-input_sdata = "st_sdata_raw.zarr"  # Name of the input anndata file
+input_sdata = "st_sdata_raw.zarr"  # Input: SpatialData file
 min_counts = 500  # Min counts per spot
 min_genes = 250  # Min genes per spot
 min_spots = 1  # Min spots per gene
@@ -34,8 +34,8 @@ mito_threshold = 20  # Mitochondrial content threshold (%)
 ribo_threshold = 0  # Ribosomal content threshold (%)
 hb_threshold = 100  # content threshold (%)
 artifact_dir = "artifacts"
-output_sdata = "st_sdata_filtered.zarr"  # Name of the output zarr file
-output_adata_filtered = "st_adata_filtered.h5ad"  # Name of the output anndata file
+output_sdata = "st_sdata_filtered.zarr"  # Output: SpatialData file
+output_adata_filtered = "st_adata_filtered.h5ad"  # Output: AnnData file
 ```
 
 ```{python}
@@ -78,9 +78,7 @@ def to_legacy_anndata(sdata: spatialdata.SpatialData) -> AnnData:
 
 ```{python}
 # Read the data
-
 st_sdata = spatialdata.read_zarr(input_sdata, ["images", "table", "shapes"])
-
 st_adata = to_legacy_anndata(st_sdata)
 
 # Convert X matrix from csr to csc dense matrix for output compatibility:
diff --git a/bin/st_svg.qmd b/bin/st_svg.qmd
index 1951e4c..e9f7b00 100644
--- a/bin/st_svg.qmd
+++ b/bin/st_svg.qmd
@@ -9,12 +9,12 @@ jupyter: python3
 ```{python}
 #| tags: [parameters]
 #| echo: false
-input_sdata = "st_sdata_processed.zarr" # Input: SpatialData file
+input_sdata = "st_sdata_processed.zarr"  # Input: SpatialData file
+n_top_spatial_degs = 14  # Number of SVG to plot in report
+artifact_dir = "artifacts"  # Output directory
 output_adata_svg = "st_adata_svg.h5ad"  # Output: AnnData file
 output_sdata = "st_sdata_svg.zarr"  # Output: SpatialData file
-output_svg = "st_svg.csv" # Output: spatially variable genes
-n_top_spatial_degs = 14 # Parameter: number of SVG to plot in report
-artifact_dir = "artifacts"
+output_svg = "st_svg.csv"  # Output: spatially variable genes
 ```
 
 ```{python}