Tutorial

Zebrafish

import package

from STMiner.SPFinder import SPFinder

Load data

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

Find SVG

sp.get_genes_csr_array(min_cells=50)
sp.spatial_high_variable_genes()

Fit GMM

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

n_comp： Number of components for each GMM model
gene_list: Gene list to fit GMM model

Build distance array

sp.build_distance_array()

build distance matrix & clustering

sp.cluster(n_clusters=6)

n_clusters: Number of cluster

Result & Visualization

The result are stored in genes_labels:

spf.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 by heatmap:

sp.get_pattern_array()
sp.plot.plot_pattern(heatmap=False,
                     s=5,
                     rotate=True,
                     reverse_y=True,
                     reverse_x=True,
                     vmax=95,
                     cmap='Spectral_r',
                     output_path='./')

s: Spot size output_path: If set, save the figure to path

To visualize the genes by labels:

sp.plot.plot_genes(label=0, n_gene=8, s=5, reverse_y=True, reverse_x=True)

n_gene: Number of genes to visualize

Additional function - Marked region (Disable in current version)

# Open the GUI of STMiner
sp.app.run()

Load the image in UI:

Load the image

Cut the image:

Cut the image

Mark the image: