Landscape-Clustergram Visium-HD¶

https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-lung-cancer-if

In [1]:

%load_ext autoreload
%autoreload 2
%env ANYWIDGET_HMR=1

%load_ext autoreload %autoreload 2 %env ANYWIDGET_HMR=1

env: ANYWIDGET_HMR=1

In [2]:

import numpy as np
import pandas as pd
import celldega as dega
from ipywidgets import Widget

import numpy as np import pandas as pd import celldega as dega from ipywidgets import Widget

/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}, "

Viz¶

In [3]:

base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Visium-HD_Mouse_Lung_Fresh_Frozen/main/LandscapeFiles'

base_url = 'https://raw.githubusercontent.com/broadinstitute/celldega_Visium-HD_Mouse_Lung_Fresh_Frozen/main/LandscapeFiles'

In [4]:

Widget.close_all()
landscape_sst = dega.viz.Landscape(
    technology='Visium-HD',
    base_url=base_url,
    square_tile_size=3,
    height=600
)

Widget.close_all() landscape_sst = dega.viz.Landscape( technology='Visium-HD', base_url=base_url, square_tile_size=3, height=600 )

/var/folders/8d/jxpy9rd10j7fp2rcj_s5sz3c0000gq/T/ipykernel_14589/469116797.py:2: UserWarning: Transformation matrix not found at https://raw.githubusercontent.com/broadinstitute/celldega_Visium-HD_Mouse_Lung_Fresh_Frozen/main/LandscapeFiles/micron_to_image_transform.csv. Using identity.
  landscape_sst = dega.viz.Landscape(

In [5]:

df_sig = pd.read_parquet(f'{base_url}/df_sig.parquet')
meta_gene = pd.read_parquet(f'{base_url}/meta_gene.parquet')
meta_gene['mean_log1p'] = np.log1p(meta_gene['mean'])

df_sig = pd.read_parquet(f'{base_url}/df_sig.parquet') meta_gene = pd.read_parquet(f'{base_url}/meta_gene.parquet') meta_gene['mean_log1p'] = np.log1p(meta_gene['mean'])

In [6]:

mat = dega.clust.Matrix(data=df_sig, meta_row=meta_gene, row_attr=['mean_log1p'])
mat.filter(by='var', num=5000, axis='row')
mat.filter(by='mean', num=1000, axis='row')
mat.norm(by='total', axis='col')
mat.norm(by='zscore', axis='row')
mat.cluster()
cgm = dega.viz.Clustergram(matrix=mat, width=500, height=500)

mat = dega.clust.Matrix(data=df_sig, meta_row=meta_gene, row_attr=['mean_log1p']) mat.filter(by='var', num=5000, axis='row') mat.filter(by='mean', num=1000, axis='row') mat.norm(by='total', axis='col') mat.norm(by='zscore', axis='row') mat.cluster() cgm = dega.viz.Clustergram(matrix=mat, width=500, height=500)

In [7]:

dega.viz.landscape_clustergram(landscape=landscape_sst, mat=cgm)

dega.viz.landscape_clustergram(landscape=landscape_sst, mat=cgm)

Out[7]:

Enrichment Analysis: Enrichr¶

In [8]:

dega.viz.clustergram_enrich(cgm)

dega.viz.clustergram_enrich(cgm)

Out[8]:

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