Alternative ways of masking - SOM#
In addition to convolutional techniques, GRIDGEN supports masks created using KDTree-based neighbour counting and Self-Organizing Maps (SOM).
We will test derive cancer stroma with both SOM and KDTree and find identify γδ T cell regions using KDtree approaches in Xenium CRC data.
%load_ext autoreload
import os
import sys
import time
import logging
import re
from tqdm import tqdm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import json
import anndata as ad
import scanpy as sc
import squidpy as sq
from natsort import natsorted
import matplotlib.colors as mcolors
np.random.seed(42)
import cv2
import copy
import seaborn as sns
sys.path.append(os.path.dirname(os.getcwd()))
from gridgene import get_arrays as ga
from gridgene import contours, get_masks
from gridgene.mask_properties import MaskAnalysisPipeline, MaskDefinition
from gridgene.binsom import GetBins, GetContour
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
define the logger : can be None, and is set to INFO
# define the logger : can be None, and is set to INFO
# Custom logger setup
logger = logging.getLogger('contour_logger')
handler = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.INFO)
SOM-based Cancer Stroma Identification#
SOM-based mask generation applies the SOM algorithm to bins of image data, classifying each bin in an unsupervised manner. The bin size can be adjusted to control the granularity of the masks, while the number of SOM units determines the number of resulting groups.
In this study, we used SOM with two units, leading to an unsupervised separation of stromal and cancer compartments, distinction supported by differential gene expression observed between the two classes. This offers a possibility of deriving masks in an unsupervised fashion.
Get the files
xenium_path = '../../xenium_data/HLA/GD_TMA1_S3/fov_filtered'
to_exclude = [
'TMA1_Selection14_filtered.csv' , # little tumour
'TMA1_Selection15_filtered.csv', # tonsil
'TMA1_Selection18_filtered.csv' , # normal
'TMA1_Selection24_filtered.csv', # tonsl
'TMA1_Selection27_filtered.csv', # low quality
'TMA1_Selection32_filtered.csv', # low quality
'TMA1_Selection33_filtered.csv', # low quality
]
files_tma1 = os.listdir(xenium_path)
files = [os.path.join(xenium_path, file) for file in files_tma1 if file not in to_exclude]
print(len(files))
20
Use an example file
# we will do for a single file
file_csv = files[7]
filename = os.path.basename(file_csv).split('.')[0] # Extract the filename without extension
name_file = '_'.join(filename.split('_')[:2])# Extract the desired part
df_total = pd.read_csv(file_csv)
df_total = df_total[['x_location', 'y_location', 'feature_name']]
df_total = df_total.rename(columns={'feature_name': 'target'})
df_total = df_total[~df_total['target'].str.contains('System|egative')]
df_total['X'] = df_total['x_location'] - min(df_total['x_location'])
df_total['Y'] = df_total['y_location'] - min(df_total['y_location'])
n_genes = len(df_total['target'].unique())
height = int(max(df_total['X'])) + 1
width = int(max(df_total['Y'])) + 1
print(f'n genes: {n_genes}')
print(f'shape: {height}, {width}')
print(f'n hits {len(df_total)}')
n genes: 480
shape: 1809, 1769
n hits 4433228
SOM pipeline
First we will divide the image in bins. In this case, we will use non overlapping bins.
The counts of eachbin with all the space information are saved in an adata object.
Preprocess bin applies single cell normalization pipeline to the bin information (normalize total and log1) and filters out empty bins <10 counts.
bin_size = 5 # 10
min_counts = 10 # 10
unique_targets = df_total['target'].unique()
GB = GetBins(bin_size, unique_targets, logger)
GB.get_bin_cohort(df_list= [df_total], df_name_list = [name_file], cohort_name = 'HLA')
GB.preprocess_bin(min_counts = min_counts)
adata = GB.adata
2025-06-17 22:48:02,696 - contour_logger - INFO - Time to get bins for 1 dataframes: 2.22 seconds
2025-06-17 22:48:02,697 - contour_logger - INFO - Number of bins: 99374
2025-06-17 22:48:02,697 - contour_logger - INFO - Number of genes: 480
With the bins of the images, we can apply the SOM clustering.
We will apply a SOM map with 2 cells, and see if it accurately divides cancer and stroma compartments.
GC = GetContour(adata, logger)
GC.run_som(som_shape = (2,1), n_iter = 5000, sigma=.5, learning_rate=.5, random_state = 42)
2025-06-17 22:48:04,084 - contour_logger - INFO - Time to run som on 78461 bins: 0.92
2025-06-17 22:48:04,085 - contour_logger - INFO - Number of clusters: 2
2025-06-17 22:48:04,086 - contour_logger - INFO - number of bins in each cluster: cluster_som
0 42722
1 35739
Name: count, dtype: int64
As you can see, the fitting process is nearly instantaneous.
We can now evaluate the differentially expressed genes using the GC.adata object and perform the preferred analyses. Additionally, you can apply GC.eval_som_statistical for further evaluation.
GC.eval_som_statistical(top_n=10)
2025-06-17 22:48:05,116 - contour_logger - INFO - n top genes for group 0
2025-06-17 22:48:05,118 - contour_logger - INFO -
names scores logfoldchanges pvals pvals_adj group
0 EPCAM 496.127167 4.710810 0.0 0.0 0
1 KRT18 401.328308 4.123829 0.0 0.0 0
2 LCN2 350.054199 4.303699 0.0 0.0 0
3 KRT8 204.175812 3.766609 0.0 0.0 0
4 CCL20 203.358932 3.733404 0.0 0.0 0
5 CA9 167.450836 4.739976 0.0 0.0 0
6 BIRC3 165.406113 3.378658 0.0 0.0 0
7 HLA-G 163.212875 1.587320 0.0 0.0 0
8 IFITM1 163.142197 2.525657 0.0 0.0 0
9 CEACAM1 161.894699 4.590243 0.0 0.0 0
2025-06-17 22:48:05,123 - contour_logger - INFO - n top genes for group 1
2025-06-17 22:48:05,125 - contour_logger - INFO -
names scores logfoldchanges pvals pvals_adj group
0 SPARC 367.663055 4.587291 0.0 0.0 1
1 COL1A1 347.223572 4.167460 0.0 0.0 1
2 IGFBP7 191.690048 4.526878 0.0 0.0 1
3 LUM 190.487732 4.909051 0.0 0.0 1
4 TIMP2 187.690933 4.334179 0.0 0.0 1
5 COL3A1 186.767471 5.427709 0.0 0.0 1
6 VIM 157.729630 2.898454 0.0 0.0 1
7 COL11A1 147.477905 5.134010 0.0 0.0 1
8 COL6A2 132.532669 4.741213 0.0 0.0 1
9 TAGLN 131.269485 4.528151 0.0 0.0 1
From the gene patterns, it appears that the SOM is effectively separating the bins based on stroma vs. cancer regions.
Let’s visualize this in two different ways:
* Reconstructing the bins into an image — we can use GC.get_som_2d_image(bin_size=10) to transform the bins back into an image and plot it with GC.plot_som.
* Plotting the centroids with Squidpy — we can leverage the centroids stored in the adata object to plot only the centroid points (note that all bins have the same size in this case).
som_images = GC.get_som_2d_image(bin_size = 5)
# Create a custom colormap and legends
cmap = mcolors.ListedColormap(['black', 'orange', 'blue',])
legend_labels = {0: 'Empty', 1: 'Cluster 0', 2: 'Cluster 1'}
for name, som_image in som_images.items():
print(name)
GC.plot_som(som_image, cmap = cmap, path=None, show=True, figsize=(5, 5), ax=None, legend_labels=legend_labels)
TMA1_Selection22
# adata.obsm["spatial"] = adata.obs[["x_centroid", "y_centroid"]].copy().to_numpy()
unique_cases =adata.obs['name'].unique()
for case in unique_cases:
# Filter the AnnData object for the current case
adata_case = adata[adata.obs['name'] == case, :]
print(case)
plt.rcParams["figure.figsize"] = (5, 4)
sq.pl.spatial_scatter(
adata_case,
library_id='name',
shape=None,
color=["cluster_som"],
size=1,
)
plt.show()
TMA1_Selection22
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
We can use this information to build masks, as we saw earlier, based on the contours defined by gene expression patterns.
The SOM clusters are numbered, but we need to assign biological identities to these clusters. By running GC.eval_som_statistical(top_n=10), we observe that:
Cluster 1 corresponds to Stroma, characterized by marker genes such as SPARC, COL1A1, VIM, etc.
Cluster 0 corresponds to Tumor, marked by genes like EPCAM, KRT18, KRT8, and others.
name, som_image = list(som_images.items())[0]
# pass the contour area threshold
GM = get_masks.GetMasks(image_shape = (som_image.shape[0], som_image.shape[1]))
mask_S = (som_image==2).astype(np.uint8) * 255
GM.mask_S = (GM.filter_binary_mask_by_area(mask_S, min_area=700))
mask_T = (som_image==1).astype(np.uint8) * 255
GM.mask_T = (GM.filter_binary_mask_by_area(mask_T, min_area=700))
GM.plot_masks(masks=[GM.mask_S, GM.mask_T], mask_names = ['Stroma', 'Tumour'],
background_color=(1, 1, 1),
mask_colors={'Stroma': (65, 105, 225), 'Tumour': (255, 165, 0)}, # {'Stroma': (0, 0, 255), 'Tumour': (255, 0, 0)
figsize= (6,6),
path=None, show=True, ax=None)
2025-06-17 22:48:14,931 - gridgen.get_masks.GetMasks - INFO - Initialized GetMasks
With the masks, we can proceed and do the remaining pipeline with expansions, resuming mask information and hierarchy analysis as described in other notebooks. Important parameters include the bin size and the number of units in SOM (see appropriate notebook).
Full pipeline
In this case, instead of performing the binning per image we will apply to a full cohort.
obtain list of dfs
df_list = []
df_name_list = []
for file_csv in tqdm(files, desc="Processing Folders", unit="folder"):
start_time = time.time()
df_total = pd.read_csv(file_csv)
df_total = df_total[['x_location', 'y_location', 'feature_name']]
df_total = df_total.rename(columns={'feature_name': 'target'})
df_total = df_total[~df_total['target'].str.contains('System|egative')]
df_total['X'] = df_total['x_location'] - min(df_total['x_location'])
df_total['Y'] = df_total['y_location'] - min(df_total['y_location'])
# Extract the file name with extension
file_name_with_ext = os.path.basename(file_csv)
file_name = os.path.splitext(file_name_with_ext)[0] # Remove the .csv extension
df_list.append(df_total)
df_name_list.append(file_name)
Processing Folders: 90%|███████████████████████████████████████████████████████████████████████████████████████████████████████████ | 18/20 [01:00<00:06, 3.38s/folder]/tmp/ipykernel_3631713/2623634986.py:6: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.
df_total = pd.read_csv(file_csv)
Processing Folders: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [01:05<00:00, 3.29s/folder]
Get the bins of all the images in the cohort
bin_size = 10
min_counts = 20
unique_targets = df_total['target'].unique()
GB = GetBins(bin_size, unique_targets, logger)
GB.get_bin_cohort(df_list, df_name_list, cohort_name = 'HLA')
GB.preprocess_bin(min_counts = min_counts)
adata = GB.adata
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
utils.warn_names_duplicates("obs")
2025-06-17 22:49:45,883 - contour_logger - INFO - Time to get bins for 20 dataframes: 21.68 seconds
2025-06-17 22:49:45,883 - contour_logger - INFO - Number of bins: 551546
2025-06-17 22:49:45,883 - contour_logger - INFO - Number of genes: 480
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
utils.warn_names_duplicates("obs")
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
utils.warn_names_duplicates("obs")
Get contours with a SOM with (2,1) shape
GC = GetContour(adata, logger)
GC.run_som(som_shape = (2,1), n_iter = 5000, sigma=.5, learning_rate=.5, random_state = 42)
som_images = GC.get_som_2d_image(bin_size = bin_size)
GC.eval_som_statistical(top_n=10)
2025-06-17 22:50:25,088 - contour_logger - INFO - Time to run som on 375638 bins: 3.90
2025-06-17 22:50:25,091 - contour_logger - INFO - Number of clusters: 2
2025-06-17 22:50:25,093 - contour_logger - INFO - number of bins in each cluster: cluster_som
0 249093
1 126545
Name: count, dtype: int64
2025-06-17 22:50:42,743 - contour_logger - INFO - n top genes for group 0
2025-06-17 22:50:42,745 - contour_logger - INFO -
names scores logfoldchanges pvals pvals_adj group
0 SPARC 628.395996 4.158346 0.0 0.0 0
1 VIM 558.854919 3.332307 0.0 0.0 0
2 COL1A1 558.634033 3.965682 0.0 0.0 0
3 IGFBP7 523.938293 4.027341 0.0 0.0 0
4 COL3A1 497.098663 4.770787 0.0 0.0 0
5 LUM 451.008423 4.369345 0.0 0.0 0
6 TAGLN 445.750000 4.511294 0.0 0.0 0
7 TIMP2 385.620026 2.833496 0.0 0.0 0
8 COL6A2 375.963989 4.444396 0.0 0.0 0
9 COL4A1 360.146545 3.352051 0.0 0.0 0
2025-06-17 22:50:42,750 - contour_logger - INFO - n top genes for group 1
2025-06-17 22:50:42,752 - contour_logger - INFO -
names scores logfoldchanges pvals pvals_adj group
0 EPCAM 1081.801758 4.638615 0.0 0.0 1
1 KRT18 911.584412 3.848826 0.0 0.0 1
2 KRT8 686.701599 3.492445 0.0 0.0 1
3 LGALS3 468.466888 3.026920 0.0 0.0 1
4 CEACAM1 447.078094 4.065439 0.0 0.0 1
5 LAD1 400.892151 3.543878 0.0 0.0 1
6 MYC 383.509033 3.395639 0.0 0.0 1
7 LCN2 352.629822 3.871783 0.0 0.0 1
8 STMN1 349.352020 2.470007 0.0 0.0 1
9 CD47 349.283112 2.390444 0.0 0.0 1
GC.eval_som_statistical_df
| names | scores | logfoldchanges | pvals | pvals_adj | group | |
|---|---|---|---|---|---|---|
| 0 | SPARC | 628.395996 | 4.158346 | 0.0 | 0.0 | 0 |
| 1 | VIM | 558.854919 | 3.332307 | 0.0 | 0.0 | 0 |
| 2 | COL1A1 | 558.634033 | 3.965682 | 0.0 | 0.0 | 0 |
| 3 | IGFBP7 | 523.938293 | 4.027341 | 0.0 | 0.0 | 0 |
| 4 | COL3A1 | 497.098663 | 4.770787 | 0.0 | 0.0 | 0 |
| 5 | LUM | 451.008423 | 4.369345 | 0.0 | 0.0 | 0 |
| 6 | TAGLN | 445.750000 | 4.511294 | 0.0 | 0.0 | 0 |
| 7 | TIMP2 | 385.620026 | 2.833496 | 0.0 | 0.0 | 0 |
| 8 | COL6A2 | 375.963989 | 4.444396 | 0.0 | 0.0 | 0 |
| 9 | COL4A1 | 360.146545 | 3.352051 | 0.0 | 0.0 | 0 |
| 10 | EPCAM | 1081.801758 | 4.638615 | 0.0 | 0.0 | 1 |
| 11 | KRT18 | 911.584412 | 3.848826 | 0.0 | 0.0 | 1 |
| 12 | KRT8 | 686.701599 | 3.492445 | 0.0 | 0.0 | 1 |
| 13 | LGALS3 | 468.466888 | 3.026920 | 0.0 | 0.0 | 1 |
| 14 | CEACAM1 | 447.078094 | 4.065439 | 0.0 | 0.0 | 1 |
| 15 | LAD1 | 400.892151 | 3.543878 | 0.0 | 0.0 | 1 |
| 16 | MYC | 383.509033 | 3.395639 | 0.0 | 0.0 | 1 |
| 17 | LCN2 | 352.629822 | 3.871783 | 0.0 | 0.0 | 1 |
| 18 | STMN1 | 349.352020 | 2.470007 | 0.0 | 0.0 | 1 |
| 19 | CD47 | 349.283112 | 2.390444 | 0.0 | 0.0 | 1 |
adata.obs['cluster_som'].value_counts()
cluster_som
0 249093
1 126545
Name: count, dtype: int64
column_name = 'cluster_som'
groupcl = copy.deepcopy(adata)
groupcl.obs[column_name] = pd.Categorical(
groupcl.obs[column_name],
categories=[0, 1], # ← integer categories
# categories=[ '0','1'],
ordered=True
)
# groupcl.uns['log1p']['base'] = None
sc.tl.rank_genes_groups(groupcl,column_name, method='wilcoxon', pts=True)
sc.tl.filter_rank_genes_groups(groupcl, groupby=column_name, min_fold_change=1)
# Extract the results
result = groupcl.uns['rank_genes_groups']
groups = result['names'].dtype.names
# Create a DataFrame to store the results
results_df = pd.DataFrame()
for group in groups:
group_df = pd.DataFrame({
'gene': result['names'][group],
'logfoldchanges': result['logfoldchanges'][group],
'pvals': result['pvals'][group],
'pvals_adj': result['pvals_adj'][group],
'scores': result['scores'][group]
})
group_df['mask'] = group
results_df = pd.concat([results_df, group_df], ignore_index=True)
sc.set_figure_params(scanpy=True, fontsize=14)
dotplot = sc.pl.rank_genes_groups_dotplot(
groupcl,
groupby=column_name,
standard_scale="var",
n_genes=10, #15
dendrogram=True,
return_fig=True,
# swap_axes=True,
)
dotplot.savefig('figures/dotplot_highres.png', dpi=500, bbox_inches='tight')
WARNING: Dendrogram not added. Dendrogram is added only when the number of categories to plot > 2
heatmap_dict = sc.pl.rank_genes_groups_heatmap(
groupcl,
groupby=column_name,
n_genes=10,
cmap='inferno',
dendrogram=False,
standard_scale='var',
swap_axes=True,
return_fig=True,
show=False
)
# Get the heatmap axis
heatmap_ax = heatmap_dict["heatmap_ax"]
# Add label to the colorbar if it exists
for im in heatmap_ax.get_images():
if hasattr(im, 'colorbar') and im.colorbar is not None:
im.colorbar.set_label("Z-score (standardized expression)", fontsize=12)
# Save the full figure (not just the axis!)
heatmap_ax.figure.tight_layout()
heatmap_ax.figure.savefig("figures/heatmap_with_label.png", dpi=300)
/tmp/ipykernel_3631713/4040328311.py:22: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
heatmap_ax.figure.tight_layout()
plot
adata = GC.adata
unique_cases =adata.obs['name'].unique()
mask_colors = {
0: (65/255, 105/255, 225/255), # RoyalBlue
1: (255/255, 165/255, 0/255), # Orange
}
categories = adata.obs['cluster_som'].cat.categories
palette_list = [mask_colors[cat] for cat in categories]
# Convert the list of colors to a ListedColormap
palette_cmap = mcolors.ListedColormap(palette_list)
for case in unique_cases:
# Filter the AnnData object for the current case
adata_case = adata[adata.obs['name'] == case, :]
plt.rcParams["figure.figsize"] = (5, 4)
sq.pl.spatial_scatter(
adata_case,
library_id='name',
shape=None,
color=["cluster_som"],
size=1,
palette=palette_cmap # pass the colormap object
)
plt.show()
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/scanpy/plotting/_utils.py:487: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[value_to_plot + "_colors"] = colors_list
/home/martinha/miniconda3/envs/GRIDGEN/lib/python3.11/site-packages/squidpy/pl/_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored
_cax = scatter(
# for saving
# for case in unique_cases:
# adata_case = adata[adata.obs['name'] == case, :]
# plt.rcParams["figure.figsize"] = (5, 4)
# fig = plt.figure()
# ax = fig.add_subplot(111)
# sq.pl.spatial_scatter(
# adata_case,
# library_id='name',
# shape=None,
# color=["cluster_som"],
# size=1,
# palette=palette_cmap,
# ax=ax,
# )
# # Remove x/y labels, ticks, title, and legend
# ax.set_xlabel('')
# ax.set_ylabel('')
# ax.set_xticks([])
# ax.set_yticks([])
# ax.set_title('')
# ax.legend().remove() if ax.get_legend() else None
# # Save figure
# fig.savefig(f"results/som/{case}.png", bbox_inches='tight', dpi=400, pad_inches = 0.05)
# plt.close(fig)