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Analyze Xenium data

Prepare the input .h5ad file

Download the Feature-cell Matrix (HDF5) and the Cell summary file (CSV) from the Xenium breast cancer tumor microenvironment Dataset.

Then get the raw .h5ad file in jupyter with the following code:

import scanpy as sc
import pandas as pd
!mkdir tutorial_data
!mkdir tutorial_data/xenium_data
!wget -P tutorial_data/xenium_data/ https://cf.10xgenomics.com/samples/xenium/preview/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_cell_feature_matrix.h5
!wget -P tutorial_data/xenium_data/ https://cf.10xgenomics.com/samples/xenium/preview/Xenium_FFPE_Human_Breast_Cancer_Rep1/Xenium_FFPE_Human_Breast_Cancer_Rep1_cells.csv.gz
!tar -xzf tutorial_data/xenium_data/Xenium_FFPE_Human_Breast_Cancer_Rep1_cells.csv.gz -C tutorial_data/xenium_data/

adata = sc.read_10x_h5(
    filename="tutorial_data/xenium_data/Xenium_FFPE_Human_Breast_Cancer_Rep1_cell_feature_matrix.h5"
)
df = pd.read_csv(
    "tutorial_data/xenium_data/Xenium_FFPE_Human_Breast_Cancer_Rep1_cells.csv"
)
df.set_index(adata.obs_names, inplace=True)
adata.obs = df.copy()
adata.obsm["spatial"] = adata.obs[["x_centroid", "y_centroid"]].copy().to_numpy()
adata.write('xenium.h5ad')

The data contained 1 slice, 167782 cells and 313 genes. The coordinates are stored in anndata.obs['spatial'].

Quality control

Features of image-based spatial transcriptome technology: (1) single-cell resolution; (2) Low depth; (3) Genes are marker genes of prior design.

Execute SpatialQC in the shell terminal:

SpatialQC --adata xenium.h5ad --platform MERFISH --slice_number 1

slice1 score: 5.0  
Number of detected doublets: 109  
Automatic threshold for n_genes is: 30  
Count of cells with mitochondrial gene percentage greater than 0.1: 0  
Using user-provided min_cells: 1

--platform MERFISH same as: --doublet True --n 0.9 --min_cells 1 --s 3 --min_genes_list 0 10 20 30 40 50 --min_genes_list2 0 10 20 30 40 50 --min_cells_list 1 --s2 0 0 1. Users can specify which parameter overrides they want to modify. Keep the default values here. The html output can be downloaded at github.

As the terminal output shows, the only slice has a score of 5; 109 double cells were detected and removed. To preserve more than 90% of the available cells, cells with less than 30 n_genes were deleted. All marker genes from the Xenium data are retained.

Clean data after filtering:

Downstream analysis with scanpy and squidpy

Visualize the Leiden clustering with cells divided into 14 clusters.

import scanpy as sc
adata = sc.read_h5ad('filtered.h5ad')
adata.layers["counts"] = adata.X.copy()
sc.pp.normalize_total(adata, inplace=True)
sc.pp.log1p(adata)
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)
sc.pl.umap(adata, color=["leiden"], wspace=0.4)


import squidpy as sq
sq.pl.spatial_scatter(adata, library_id="spatial", shape=None, color=["leiden"], wspace=0.4)

Plot the clustering on spatial coordinates.

Neighbors enrichment analysis

sq.gr.spatial_neighbors(adata, coord_type="generic", delaunay=True)
sq.gr.nhood_enrichment(adata, cluster_key="leiden")
sq.pl.nhood_enrichment(adata, cluster_key="leiden", title="Neighborhood enrichment adata")