STMiner 0.0.0

Python package for spatial transcriptomics data analysis

Introduction

Spatial transcriptomics revolutionizes transcriptomics by incorporating positional information. However, an emergency problem is to find out the gene expression pattern which can reveal the special region in tissue and find out the genes only expression in those regions.

Here we propose “STMiner” based on the Gaussian mixture model to solve this problem. STMiner is a bottom-up methodology algorithm. It is initiated by fitting a parametric model of gene spatial distributions and constructing a distance array between them utilizing the Hellinger distance. Genes are clustered, thereby recognizing spatial co-expression patterns across distinct gene classes.

Please visit STMiner Documents for details.

Quick start by example

import package

from STMiner import SPFinder

Load data

You can download test data here.

sp = SPFinder()
file_path = 'D://10X_Visium_hunter2021spatially_sample_C_data.h5ad'
sp.read_h5ad(file=file_path)

Find spatial high variable genes

sp.get_genes_csr_array(min_cells=500, log1p=False)
sp.spatial_high_variable_genes()

You can check the distance of each genes by

sp.global_distance

Gene	Distance
geneA	9998
geneB	9994
...	...
geneC	8724

Preprocess and Fit GMM

sp.fit_pattern(n_comp=20, gene_list=list(sp.global_distance[:1000]['Gene']))

Each GMM model has 20 components.

Build distance matrix & clustering

sp.build_distance_array()
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

To visualize the patterns:

sp.get_pattern_array(vote_rate=0.3)
sp.plot.plot_pattern(vmax=99,
                     heatmap=False,
                     s=5,
                     reverse_y=True,
                     reverse_x=True,
                     image_path='E://cut_img.png',
                     rotate_img=True,
                     k=4,
                     aspect=0.55)

Visualize the intersections between patterns 3 & 1:

sp.plot.plot_intersection(pattern_list=[0, 1],
                          image_path='E://OneDrive - stu.xjtu.edu.cn/paper/cut_img.png',
                          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)

Attribute of STMiner.SPFinder Object

Attribute	Type	Description
adata	Anndata	Anndata for loaded spatial data
global_distance	pd.DataFrame	OT distance between gene and background
genes_labels	pd.DataFrame	Gene name and their pattern labels
genes_patterns	dict	GMM model for each gene
genes_distance_array	pd.DataFrame	Distance between each GMM
kmeans_fit_result	obj	Result of k-means
mds_features	pd.DataFrame	embedding features after MDS