SOView is a visualization tool for spatial omics data¶

Introduction¶

SOView is designed for tissue visualization for general spatial omics data, the data input is in Anndata format.

Installation¶

Create a conda environment and activate it.

conda create --name SOView python=3.9 -y
conda activate SOView

Clone the source code.

git clone https://github.com/yuanzhiyuan/SOView

Install SOView as a dependency or third-party package with pip:

cd SOView
pip install .

Also install pysodb to load the data: https://pysodb.readthedocs.io/en/latest/

Examples¶

[1]:

import warnings
warnings.filterwarnings("ignore")

load Visium data using pysodb¶

[2]:

import pysodb
sodb = pysodb.SODB()

[3]:

dataset_name = '10x'
experiment_name = 'V1_Mouse_Brain_Sagittal_Posterior_filtered_feature_bc_matrix'
adata = sodb.load_experiment(dataset_name,experiment_name)

load experiment: V1_Mouse_Brain_Sagittal_Posterior_filtered_feature_bc_matrix in dataset: 10x

plot SOView for Visium data¶

[4]:

import SOView

scanpy==1.9.1 anndata==0.8.0 umap==0.5.2 numpy==1.22.4 scipy==1.7.3 pandas==1.5.2 scikit-learn==1.0.2 statsmodels==0.13.5 python-igraph==0.10.2 pynndescent==0.5.8
squidpy==1.1.2

[5]:

SOView.SOViewer_plot(
    adata = adata, # the data to plot
    save = None, # save the result to specified path or don't save (None)
    embedding_use='X_umap', # which embedding to be used for plot
    dot_size=10, # the marker size of the plot
    marker = 'o' # marker style


)

# SOView function gets results of both CIELAB and RGB color coding

generating color coding...
1.0 0.0

1.0 0.0

[9]:

# change the marker style to hexagon to fit for 10X Visium
SOView.SOViewer_plot(
    adata = adata, # the data to plot
    save = None, # save the result to specified path or don't save (None)
    embedding_use='X_umap', # which embedding to be used for plot
    dot_size=13, # the marker size of the plot
    marker = 'h' # marker style


)

generating color coding...
1.0 0.0

1.0 0.0

[ ]:

[1]:

import warnings
warnings.filterwarnings("ignore")

load stereo-seq data using pysodb¶

[2]:

import pysodb
sodb = pysodb.SODB()

[7]:

dataset_name = 'chen2022spatiotemporal'
experiment_name = 'E14.5_E1S1.MOSTA'
adata = sodb.load_experiment(dataset_name,experiment_name)

load experiment: E14.5_E1S1.MOSTA in dataset: chen2022spatiotemporal

plot SOView for stereo-seq data¶

[5]:

import SOView

scanpy==1.9.1 anndata==0.8.0 umap==0.5.2 numpy==1.22.4 scipy==1.7.3 pandas==1.5.2 scikit-learn==1.0.2 statsmodels==0.13.5 python-igraph==0.10.2 pynndescent==0.5.8
squidpy==1.1.2

[8]:

SOView.SOViewer_plot(
    adata = adata, # the data to plot
    save = None, # save the result to specified path or don't save (None)
    embedding_use='X_umap', # which embedding to be used for plot
    dot_size=10, # the marker size of the plot
    marker = 'o' # marker style


)

generating color coding...
1.0 0.0

1.0 0.0

[ ]:

Other¶

Users can try other data in SODB for other test.

If you find SOView useful in your research, please consider citing:¶

TBD