tsne_nearest_neighbor_method

example.py

import scanpy as sc
import numpy as np
from matplotlib import pyplot as plt
from openTSNE import TSNE

from itertools import product

pbmc = sc.datasets.pbmc3k_processed()
RANDOM_STATE = 1291321
keys = []

for metric, knn_method in product(["euclidean", "cosine"], ["annoy", "pynndescent"]):
    key = f"tsne_{metric}_{knn_method}"
    keys.append(key)
    pbmc.obsm[key] = np.asarray(
        TSNE(
            n_jobs=-1,
            metric=metric,
            negative_gradient_method="bh",
            neighbors=knn_method,
            random_state=RANDOM_STATE,
        ).fit(pbmc.X)
    )

with plt.rc_context({"figure.dpi": 150, "figure.figsize": (5, 5)}):
    fig, axs = plt.subplots(ncols=2, nrows=2)
    for key, ax in zip(keys, axs.flat):
        sc.pl.embedding(
            pbmc,
            basis=key,
            size=20,
            color="louvain",
            ax=ax,
            show=False,
            legend_loc=None,
            title=key,
        )
    plt.tight_layout()