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(feat): add n_components parameter to tSNE #2803

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Merged
merged 7 commits into from
May 15, 2025
Merged

(feat): add n_components parameter to tSNE #2803

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f3f9694
Update _tsne.py
ch1ru Jan 11, 2024
959a72f
Update _tsne.py
ch1ru Jan 11, 2024
573a78f
Update _tsne.py
ch1ru Jan 11, 2024
ad504ee
Update _tsne.py
ch1ru Jan 11, 2024
88d721c
Update _tsne.py
ch1ru Jan 11, 2024
9b3a94a
Merge branch 'main'
flying-sheep May 8, 2025
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relnote
flying-sheep May 8, 2025
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1 change: 1 addition & 0 deletions docs/release-notes/2803.feature.md
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Original file line number Diff line number Diff line change
@@ -0,0 +1 @@
Added `n_components` parameter to {func}`~scanpy.tl.tsne` {smaller}`Kitsune`
5 changes: 5 additions & 0 deletions src/scanpy/tools/_tsne.py
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Expand Up @@ -33,6 +33,7 @@ def tsne( # noqa: PLR0913
adata: AnnData,
n_pcs: int | None = None,
*,
n_components: int = 2,
use_rep: str | None = None,
perplexity: float = 30,
metric: str = "euclidean",
Expand Down Expand Up @@ -60,6 +61,8 @@ def tsne( # noqa: PLR0913
Annotated data matrix.
{doc_n_pcs}
{use_rep}
n_components
The number of dimensions of the embedding.
perplexity
The perplexity is related to the number of nearest neighbors that
is used in other manifold learning algorithms. Larger datasets
Expand Down Expand Up @@ -124,6 +127,7 @@ def tsne( # noqa: PLR0913
learning_rate=learning_rate,
n_jobs=n_jobs,
metric=metric,
n_components=n_components,
)
if metric != "euclidean" and (Version(sklearn.__version__) < Version("1.3.0rc1")):
params_sklearn["square_distances"] = True
Expand Down Expand Up @@ -179,6 +183,7 @@ def tsne( # noqa: PLR0913
n_jobs=n_jobs,
metric=metric,
use_rep=use_rep,
n_components=n_components,
)
key_uns, key_obsm = ("tsne", "X_tsne") if key_added is None else [key_added] * 2
adata.obsm[key_obsm] = X_tsne # annotate samples with tSNE coordinates
Expand Down
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