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Plot features in adata.obsm

This example shows how to use squidpy.pl.extract() to plot features in anndata.AnnData.obsm.

See also

See Extract summary features for computing an example of such features.

import squidpy as sq

adata = sq.datasets.slideseqv2()
adata

Out:

AnnData object with n_obs × n_vars = 41786 × 4000
    obs: 'barcode', 'x', 'y', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_MT', 'log1p_total_counts_MT', 'pct_counts_MT', 'n_counts', 'leiden', 'cluster'
    var: 'MT', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
    uns: 'cluster_colors', 'hvg', 'leiden', 'leiden_colors', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
    obsm: 'X_pca', 'X_umap', 'deconvolution_results', 'spatial'
    varm: 'PCs'
    obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'

In this dataset, we have saved deconvolution results in anndata.AnnData.obsm and we would like to plot them with squidpy.pl.spatial_scatter().

adata.obsm["deconvolution_results"].head(10)
Interneurons Subiculum_Entorhinal_cl2 Subiculum_Entorhinal_cl3 DentatePyramids CA1_CA2_CA3_Subiculum Mural Astrocytes Oligodendrocytes Polydendrocytes Microglia Ependymal Endothelial_Tip Neurogenesis Endothelial_Stalk barcode max_cell_type maxval thresh_ct
AACGTCATAATCGT 0.113249 0.203010 0.086060 0.247319 0.153769 0.002455 0.072134 0.022327 0.008712 0.065562 0.003853 0.000000 0.000000 0.021550 AACGTCATAATCGT 4 0.247319 0.000000
TACTTTAGCGCAGT 0.055718 0.077973 0.048517 0.187755 0.195889 0.040198 0.126317 0.071605 0.052353 0.013668 0.016075 0.044060 0.012159 0.057714 TACTTTAGCGCAGT 5 0.195889 0.000000
CATGCCTGGGTTCG 0.108751 0.228845 0.109581 0.246070 0.115723 0.006306 0.071186 0.015768 0.000000 0.059735 0.000000 0.013702 0.000000 0.024332 CATGCCTGGGTTCG 4 0.246070 0.000000
TCGATATGGCACAA 0.108163 0.029694 0.112905 0.172960 0.122573 0.014295 0.065901 0.031834 0.096153 0.034155 0.094124 0.065322 0.011114 0.040808 TCGATATGGCACAA 4 0.172960 0.000000
TTATCTGACGAAGC 0.065790 0.236463 0.012458 0.233441 0.145091 0.014810 0.097935 0.052590 0.023642 0.008578 0.014275 0.058715 0.000000 0.036213 TTATCTGACGAAGC 2 0.236463 0.000000
GATGCGACTCCTCG 0.000000 0.000000 0.000000 0.222606 0.705572 0.042319 0.009459 0.000000 0.002753 0.009457 0.000000 0.000000 0.004932 0.002902 GATGCGACTCCTCG 5 0.705572 0.705572
ACGGATGTTCCGAT 0.000000 0.000000 0.000000 0.037305 0.078873 0.014507 0.032262 0.500225 0.099139 0.147571 0.013209 0.023574 0.010208 0.043127 ACGGATGTTCCGAT 8 0.500225 0.500225
TCTCATGGGTGGGA 0.011898 0.000000 0.000000 0.114613 0.089905 0.010118 0.411598 0.188769 0.034367 0.059139 0.000000 0.029453 0.017615 0.032526 TCTCATGGGTGGGA 7 0.411598 0.411598
ACCGGAACTTCTTC 0.016862 0.000000 0.000000 0.017110 0.070573 0.017013 0.006445 0.091637 0.034633 0.022682 0.659986 0.029675 0.009862 0.023523 ACCGGAACTTCTTC 11 0.659986 0.659986
ACAGGGTTTATCGA 0.012235 0.000000 0.010678 0.138919 0.723275 0.026109 0.029469 0.003275 0.003829 0.011391 0.011063 0.000000 0.014785 0.014970 ACAGGGTTTATCGA 5 0.723275 0.723275


Squidpy provides an easy wrapper that creates a temporary copy of the feature matrix and pass it to anndata.AnnData.obs.

sq.pl.spatial_scatter(
    sq.pl.extract(adata, "deconvolution_results"),
    shape=None,
    color=["Astrocytes", "Mural", "CA1_CA2_CA3_Subiculum"],
    size=4,
)
Astrocytes, Mural, CA1_CA2_CA3_Subiculum

Total running time of the script: ( 0 minutes 18.491 seconds)

Estimated memory usage: 496 MB