Scanpy-Squidpy Xenium Pancreas¶

https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_xenium.html#analyze-xenium-data

In [1]:

import pandas as pd
import scanpy as sc
import squidpy as sq
from spatialdata_io import xenium
import celldega as dega

import pandas as pd import scanpy as sc import squidpy as sq from spatialdata_io import xenium import celldega as dega

/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.5 when it was built against 1.14.6, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "
/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/dask/dataframe/__init__.py:31: FutureWarning: The legacy Dask DataFrame implementation is deprecated and will be removed in a future version. Set the configuration option `dataframe.query-planning` to `True` or None to enable the new Dask Dataframe implementation and silence this warning.
  warnings.warn(
/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import DistributionNotFound, get_distribution
/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/anndata/utils.py:434: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.
  warnings.warn(msg, FutureWarning)
/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/anndata/utils.py:434: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.
  warnings.warn(msg, FutureWarning)

In [2]:

xenium_path = 'data/xenium_data/Xenium_V1_human_Pancreas_FFPE_outs/'
zarr_path = 'data/Xenium_V1_human_Pancreas_FFPE_outs.zarr'

xenium_path = 'data/xenium_data/Xenium_V1_human_Pancreas_FFPE_outs/' zarr_path = 'data/Xenium_V1_human_Pancreas_FFPE_outs.zarr'

Pre-Process, Cluster, and UMAP¶

In [3]:

sdata = xenium(xenium_path)

sdata = xenium(xenium_path)

INFO     reading data/xenium_data/Xenium_V1_human_Pancreas_FFPE_outs/cell_feature_matrix.h5                        

/var/folders/8d/jxpy9rd10j7fp2rcj_s5sz3c0000gq/T/ipykernel_37920/1221898808.py:1: DeprecationWarning: The default value of `cells_as_circles` will change to `False` in the next release. Please pass `True` explicitly to maintain the current behavior.
  sdata = xenium(xenium_path)

In [4]:

adata = sdata.tables["table"]
adata

adata = sdata.tables["table"] adata

Out[4]:

AnnData object with n_obs × n_vars = 140702 × 377
    obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'unassigned_codeword_counts', 'deprecated_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region', 'z_level', 'nucleus_count', 'cell_labels'
    var: 'gene_ids', 'feature_types', 'genome'
    uns: 'spatialdata_attrs'
    obsm: 'spatial'

In [5]:

del sdata

del sdata

Filter Cells¶

In [6]:

sc.pp.filter_cells(adata, min_counts=10)
sc.pp.filter_genes(adata, min_cells=5)

sc.pp.filter_cells(adata, min_counts=10) sc.pp.filter_genes(adata, min_cells=5)

/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index.
  warnings.warn("Transforming to str index.", ImplicitModificationWarning)

UMAP and Cluster¶

In [ ]:

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.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)

OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

In [15]:

sc.pl.umap(
    adata,
    color=[

        "leiden",
    ],
    wspace=0.4,
)

sc.pl.umap( adata, color=[ "leiden", ], wspace=0.4, )

In [16]:

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

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

/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
  _cax = scatter(

In [ ]:

adata.write_h5ad('data/xenium_data/Xenium_V1_human_Pancreas_FFPE_outs.h5ad')

adata.write_h5ad('data/xenium_data/Xenium_V1_human_Pancreas_FFPE_outs.h5ad')

Visualize using Celldega¶

In [4]:

base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_human_Pancreas_FFPE/main/Landscape_Xenium_V1_human_Pancreas_FFPE_outs_webp'

base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Xenium_human_Pancreas_FFPE/main/Landscape_Xenium_V1_human_Pancreas_FFPE_outs_webp'

In [5]:

landscape = dega.viz.Landscape(
    technology='Xenium', 
    base_url = base_url,
    height=600,
    adata=adata,
    landscape_state='umap'
)

landscape = dega.viz.Landscape( technology='Xenium', base_url = base_url, height=600, adata=adata, landscape_state='umap' )

In [6]:

mat = dega.clust.Matrix(adata, row_attr=['n_cells'], col_attr=['cell_area'])
mat.downsample_to(category='leiden')
mat.norm(axis='row', by='zscore')
mat.clust()
cgm = dega.viz.Clustergram(matrix=mat, width=500, height=500)

mat = dega.clust.Matrix(adata, row_attr=['n_cells'], col_attr=['cell_area']) mat.downsample_to(category='leiden') mat.norm(axis='row', by='zscore') mat.clust() cgm = dega.viz.Clustergram(matrix=mat, width=500, height=500)

/Users/feni/Documents/celldega/src/celldega/clust/matrix.py:194: UserWarning: Large matrix (122678 x 377). Consider filtering.
  self.load_adata(data, col_attr=col_attr, row_attr=row_attr)

In [7]:

dega.viz.landscape_clustergram(landscape, cgm)

dega.viz.landscape_clustergram(landscape, cgm)

Out[7]:

Squidpy¶

In [8]:

sq.gr.spatial_neighbors(adata, coord_type="generic", delaunay=True)

sq.gr.spatial_neighbors(adata, coord_type="generic", delaunay=True)

In [9]:

sq.gr.nhood_enrichment(adata, cluster_key="leiden")

sq.gr.nhood_enrichment(adata, cluster_key="leiden")

/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/dask/dataframe/__init__.py:31: FutureWarning: The legacy Dask DataFrame implementation is deprecated and will be removed in a future version. Set the configuration option `dataframe.query-planning` to `True` or None to enable the new Dask Dataframe implementation and silence this warning.
  warnings.warn(
/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/h5py/__init__.py:36: UserWarning: h5py is running against HDF5 1.14.5 when it was built against 1.14.6, this may cause problems
  _warn(("h5py is running against HDF5 {0} when it was built against {1}, "
/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import DistributionNotFound, get_distribution
/Users/feni/Documents/celldega/dega/lib/python3.12/site-packages/anndata/utils.py:434: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.
  warnings.warn(msg, FutureWarning)

In [10]:

# Extract the z-score matrix from Squidpy output
z = adata.uns["leiden_nhood_enrichment"]["zscore"]

# Convert it to a DataFrame with labeled index and columns
df = pd.DataFrame(
    z,
    index=adata.obs["leiden"].cat.categories,
    columns=adata.obs["leiden"].cat.categories,
)

# Extract the z-score matrix from Squidpy output z = adata.uns["leiden_nhood_enrichment"]["zscore"] # Convert it to a DataFrame with labeled index and columns df = pd.DataFrame( z, index=adata.obs["leiden"].cat.categories, columns=adata.obs["leiden"].cat.categories, )

In [11]:

mat_sq = dega.clust.Matrix(df)
mat_sq.clust()
cgm_sq = dega.viz.Clustergram(matrix=mat_sq, width=500, height=500)

mat_sq = dega.clust.Matrix(df) mat_sq.clust() cgm_sq = dega.viz.Clustergram(matrix=mat_sq, width=500, height=500)

In [13]:

landscape_2 = dega.viz.Landscape(
    technology='Xenium', 
    base_url = base_url,
    height=600,
    adata=adata
)

landscape_2 = dega.viz.Landscape( technology='Xenium', base_url = base_url, height=600, adata=adata )

In [14]:

dega.viz.landscape_clustergram(landscape_2, cgm_sq)

dega.viz.landscape_clustergram(landscape_2, cgm_sq)

Out[14]:

In [ ]: