reclustering on specific leiden clusters

This example demonstrates who to perform a reclustering on a selected subset of leiden clusters. You will want to do this for example during the process of celltype annotation, when the clusters do not have a sufficient resolution to seperate all clusters and mixed cell populations still exist.

import besca as bc
import scanpy as sc
import pytest

# pytest.skip('Test is only for here as example and should not be executed')
# load and preprocess data (here we will start from a preprocessed dataset)
adata = bc.datasets.pbmc3k_processed()

# extract subset using the recluster function whcih is part of the reclustering (rc) toolkit
adata_subset = bc.tl.rc.recluster(
    adata,
    celltype=("2", "3", "4", "5", "6", "8", "9", "10", "11", "12"),
    celltype_label="leiden",
    resolution=1.2,
)


# visualize the new clusters
sc.pl.umap(
    adata_subset, color=["leiden", "CD3G", "CD8A", "CD4", "IL7R", "NKG7", "GNLY"]
)

# append new celltype labels to the subclusters.
# This is an approximative hand annotation that should be dealt into more widths.
labels = [
    "NK cell",  # 0
    "CD4 T-cell",  # 1
    "CD8 T-cell",  # 2
    "CD4 T-cell",  # 3
    "CD8 T-cell",  # 4
    "CD8 T-cell",  # 5
    "CD4 T-cell",  # 6
    "CD4 T-cell",  # 7
    "CD4 T-cell",  # 8
    "CD4 T-cell",  # 9
    "CD4 T-cell",  # 10
    "CD4 T-cell",  # 11
    "CD4 T-cell",  # 12
    "CD4 T-cell",  # 13
    "CD4 T-cell",  # 14
    "CD4 T-cell",  # 15
    "CD4 T-cell",  # 16
    "CD4 T-cell",  # 17
    "CD4 T-cell",  # 18
    "CD4 T-cell",  # 19
]  # 10

new_labels = [
    labels[i]
    for i in range(len(adata_subset.obs.get("leiden").value_counts().index.tolist()))
]


# merge the labels back into the original adata object
# note this will overwrite what ever was saved in adata.obs.celltype;
# Here is was not assigned yet.
bc.tl.rc.annotate_new_cellnames(
    adata, adata_subset, names=new_labels, new_label="celltype"
)