In [1]:

import warnings
warnings.filterwarnings('ignore')

import os
import numpy as np
import pandas as pd
import scanpy as sc
import squidpy as sq
import anndata as ad
import scipy as sp

In [2]:

adata = sc.read_h5ad('../../data/from_spatial_omics/DBiT-seq/DBiT-seq_liu2020high_E10_whole_gene_best_data.h5ad')

In [3]:

adata

Out[3]:

AnnData object with n_obs × n_vars = 936 × 22802
    obs: 'leiden'
    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm'
    uns: 'hvg', 'leiden', 'leiden_colors', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
    obsm: 'X_pca', 'X_umap', 'spatial'
    varm: 'PCs'
    obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'

In [4]:

sc.pp.calculate_qc_metrics(adata, inplace=True)

In [5]:

adata.layers['counts'] = adata.X.copy()

In [6]:

sc.pp.filter_cells(adata, min_genes=10)
sc.pp.filter_genes(adata, min_cells=50)

In [7]:

adata

Out[7]:

AnnData object with n_obs × n_vars = 936 × 12060
    obs: 'leiden', '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', 'n_genes'
    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'
    uns: 'hvg', 'leiden', 'leiden_colors', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
    obsm: 'X_pca', 'X_umap', 'spatial'
    varm: 'PCs'
    layers: 'counts'
    obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'

In [8]:

sq.pl.spatial_scatter(adata, color=['leiden', 'n_genes', 'total_counts'], size=25, shape=None)

WARNING: Please specify a valid `library_id` or set it permanently in `adata.uns['spatial']`

In [9]:

# remove MT genes
non_mito_genes_list = [name for name in adata.var_names if not name.startswith('mt-')]
adata = adata[:, non_mito_genes_list]

In [10]:

sc.pp.normalize_total(adata)
sc.pp.log1p(adata)

In [11]:

sq.gr.spatial_neighbors(adata, coord_type="generic", delaunay=True)
sq.gr.spatial_autocorr(adata, mode="moran", 
                       n_perms=100, n_jobs=10, 
                       genes=adata.var_names)

100%|██████████| 100/100 [00:10<00:00,  9.69/s]

In [12]:

n_svgs = 200
sel_genes = (
    adata.uns["moranI"]["I"].sort_values(ascending=False).head(n_svgs).index.tolist()
)

In [13]:

sq.pl.spatial_scatter(
    adata, color=sel_genes[:10], figsize=(5, 5), size=25, 
    cmap="Reds", shape=None, use_raw=False
)

WARNING: Please specify a valid `library_id` or set it permanently in `adata.uns['spatial']`

In [14]:

# select top 50 variable genes as reference
adata = adata[:, sel_genes]

In [15]:

adata

Out[15]:

View of AnnData object with n_obs × n_vars = 936 × 200
    obs: 'leiden', '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', 'n_genes'
    var: 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'
    uns: 'hvg', 'leiden', 'leiden_colors', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap', 'log1p'
    obsm: 'X_pca', 'X_umap', 'spatial'
    varm: 'PCs'
    layers: 'counts'
    obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'

In [16]:

adata.write_h5ad('../../results/00_prepare_reference_data/28_DBiT_seq_E10_whole.h5ad')

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