scanpy.datasets.pbmc68k_reduced

scanpy.datasets.pbmc68k_reduced()

Subsampled and processed 68k PBMCs.

PBMC 68k dataset from 10x Genomics.

The original PBMC 68k dataset was preprocessed with steps including normalize_total()[1] and scale(). It was saved keeping only 724 cells and 221 highly variable genes.

The saved file contains the annotation of cell types (key: 'bulk_labels'), UMAP coordinates, louvain clustering and gene rankings based on the bulk_labels.

[1]

Back when the dataset was created, normalize_per_cell() was used instead.

Return type:

AnnData

Returns:

Annotated data matrix.

Examples

>>> import scanpy as sc
>>> sc.datasets.pbmc68k_reduced()
AnnData object with n_obs × n_vars = 700 × 765
    obs: 'bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score', 'G2M_score', 'phase', 'louvain'
    var: 'n_counts', 'means', 'dispersions', 'dispersions_norm', 'highly_variable'
    uns: 'bulk_labels_colors', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'