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,
)

Estimated memory usage: 496 MB