stminer 0.0.9

Python package for spatial transcriptomics data analysis

📖 Documents | 🚀 Tutorial | 💬 Contact me

Core Concept - Discrete to Continuous

👩‍🏫 Introduction

Why STMiner?

ST data presents challenges such as uneven cell density distribution, low sampling rates, and complex spatial structures. Traditional spot-based analysis strategies struggle to effectively address these issues. STMiner explores ST data by leveraging the spatial distribution of genes, thus avoiding the biases that these conditions can introduce into the results.

Most importantly, STMiner offers seamless integration with Anndata/Scanpy and can be easily installed via PyPI.

Method detail

Here we propose “STMiner”. The three key steps of analyzing ST data in STMiner are depicted.

(Left top) STMiner first utilizes Gaussian Mixture Models (GMMs) to represent the spatial distribution of each gene and the overall spatial distribution. (Left bottom) STMiner then identifies spatially variable genes by calculating the cost that transfers the overall spatial distribution to gene spatial distribution. Genes with high costs exhibit significant spatial variation, meaning their expression patterns differ considerably across different regions of the tissue. The distance array is built between SVGs in the same way, genes with similar spatial structures have a low cost to transport to each other, and vice versa. (Right) The distance array is embedded into a low-dimensional space by Multidimensional Scaling, allowing for clustering genes with similar spatial expression patterns into distinct functional gene sets and getting their spatial structure.

🚀 Quick start by example

Please visit STMiner Documents for installation and detail usage.

💡💡💡 We also provide a step-by-step Jupyter Notebook file to reproduce the results. You can access it here.

Additionally, you can run STMiner on your own:

Import package

from STMiner import SPFinder

Load data

You can download them from STMiner-test-data. You can also download the raw dataset from GEO, STMiner can read spatial transcriptome data in various formats, such as gem, bmk, and h5ad (see STMiner Documents).
We recommend using the h5ad format, as it is currently the most widely used and supported by most algorithms and software in the spatial transcriptomics field.

sp = SPFinder()
file_path = 'Path/to/your/h5ad/file'
sp.read_h5ad(file=file_path, bin_size=1)

For high resolution ST data. You can increase the bin_size parameter when loading the data to reduce running time, for example:

sp.read_h5ad(file=file_path, bin_size=50)

This will not reduce the accuracy of STMiner, as resolution generally does not affect the spatial distribution of genes.

Find spatial high variable genes

sp.get_genes_csr_array(min_cells=100, log1p=False, , normalize=False, vmax=100)
sp.spatial_high_variable_genes()

• The parameter min_cells was used to filter genes that are too sparse to generate a reliable spatial distribution.
• The parameter log1p was used to avoid extreme values affecting the results. For most open-source h5ad files, log1p has already been executed, so the default value here is False.
• You can perform STMiner in your interested gene sets. Use parameter gene_list to input the gene list to STMiner. Then, STMiner will only calculate the given gene set of the dataset.

You can check the distance of each gene by:

sp.global_distance

Gene	Distance	z-score
myha	1.35E+08	2.771493
vmhcl	1.01E+08	2.470881
zgc:101560	9.95E+07	2.458787
pvalb1	9.82E+07	2.445257
myhz2	9.75E+07	2.437787
...	...	...
rps17	2.61E+05	-3.63207
rpl13	2.48E+05	-3.68506
rpl32	2.43E+05	-3.70327
rsl24d1	2.27E+05	-3.7757
rpl22	1.83E+05	-3.99332

The 'Gene' column is the gene name, and the 'Distance' column is the difference between the spatial distribution of the gene and the background.
A larger difference indicates a more pronounced spatial pattern of the gene.

Preprocess and Fit GMM

sp.fit_pattern(n_comp=10, gene_list=list(sp.global_distance[:2000]['Gene']))

n_comp=20 means each GMM model has 20 components.

Build distance matrix & clustering

# This step calculates the distance between genes' spatial distributions.
sp.build_distance_array()
# Dimensionality reduction and clustering.
sp.cluster_gene(n_clusters=6, mds_components=20) 

Result & Visualization

The result is stored in genes_labels:

sp.genes_labels

The output looks like the following:

	gene_id	labels
0	Cldn5	2
1	Fyco1	2
2	Pmepa1	2
3	Arhgap5	0
4	Apc	5
..	...	...
95	Cyp2a5	0
96	X5730403I07Rik	0
97	Ltbp2	2
98	Rbp4	4
99	Hist1h1e	4

Visualize the distance array:

import seaborn as sns
sns.clustermap(sp.genes_distance_array)

Finding gene sets with interested structure

Get patterns of interested gene/gene set:

interested_genes = ["mbpa", "BX957331.1", "madd"]
sp.get_pattern_of_given_genes(gene_list = interested_genes, n_comp=10)

Compare the distance between all genes and the given gene set

from STMiner.Algorithm.distance import compare_gmm_distance
df = compare_gmm_distance(sp.custom_pattern, sp.patterns)
df.to_csv('compare_distance.csv')
df

Gene	distance
mbpa	0.8914643122002152
map1ab	0.9479574709875033
snap25a	0.9801858512442632
nsfa	0.9948239449738531
stxbp1a	0.99916307128497
...	...
lrrfip1b	1.9981586323013931
si:ch211-145h19.2	1.9995115533927301
BX248122.1	1.9996375745511945
si:dkey-7i4.24	1.9997052371268462

A lower distance indicates that the spatial expression pattern of the gene is more similar to that of the gene set of interest.

To visualize the patterns:

Note: A image path for image_path is needed if you want to show background image. In this example, you can download the processed image here. Anyway, image_path is optional, not providing background images has no impact on the calculation results.

sp.get_pattern_array(vote_rate=0.2)
img_path = 'path/to/downloaded/demo_img.png'
sp.plot.plot_pattern(heatmap=False, 
                     s=10,
                     rotate=False,
                     reverse_y=True,
                     reverse_x=True,
                     vmax=95,
                     image_path="./demo_img.png",
                     cmap="Spectral_r",
                     aspect=.55)

Visualize the intersections between patterns 3 & 1:

sp.plot.plot_intersection(pattern_list=[0, 1],
                          image_path=img_path,
                          reverse_y=True,
                          reverse_x=True,
                          aspect=0.55,
                          s=20)

To visualize the gene expression by labels:

sp.plot.plot_genes(label=0, vmax=99)

Attributes of STMiner.SPFinder Object

Attributes	Type	Description
adata	Anndata	Anndata for loaded spatial data
patterns	dict	Spatial distributions pattern of genes
genes_patterns	dict	GMM model for each gene
global_distance	pd. DataFrame	Distances between genes and background
mds_features	array	embedding features of genes
genes_distance_array	pd. DataFrame	Distance between each GMM
genes_labels	pd. DataFrame	Gene name and their pattern labels
plot	Object	Call plot to visualization

📜 Release history

Version	Date	Description
0.0.8	2025/2/23	change default value of Normalize
0.0.7	2025/2/21	improved performance of get_pattern_array()

pypi: https://pypi.org/project/STMiner/#history

🔖 Referance

[1] Sun, P., Bush, S. J., Wang, S., Jia, P., Li, M., Xu, T., ... & Ye, K. (2025). STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues. Cell Genomics, 5(2).

✉️ Contact

If you encounter any issues during use, please try updating STMiner to the latest version. If the issue persists, feel free to submit your problem on the issue page or contact us through the following methods:

• Peisen Sun: 📧([e-mail]) / 𝕏(https://x.com/Sun_python)
• Kai Ye: 📧([e-mail])

Please ⭐Star STMiner on Github if you find it's useful, thank you!