scanpy 空转数据结构seurattoscanpy spatial

Analysis and visualization of spatial transcriptomics data — Scanpy documentation (scanpy-tutorials.readthedocs.io)

两种都可以读取

ns7=sc.read_visium(path="./ns7/",count_file='./2.3.h5_files/filtered_feature_bc_matrix.h5',library_id="NS_7",load_images=True,s
    ...: ource_image_path="./ns7/spatial/")
 
 
adata=sc.read_visium(path="./ns56/",count_file='filtered_feature_bc_matrix.h5',library_id="NS_56",load_images=True,source_image_
   ...: path="./ns56/spatial/")

scanpy.read_visium
scanpy.read_visium(path, genome=None, *, count_file='filtered_feature_bc_matrix.h5', library_id=None, load_images=True, source_image_path=None)[source]
Read 10x-Genomics-formatted visum dataset.
 
In addition to reading regular 10x output, this looks for the spatial folder and loads images, coordinates and scale factors. Based on the Space Ranger output docs.
 
See spatial() for a compatible plotting function.
Parameters
path
str | Path
Path to directory for visium datafiles.
genome
str | None (default: None)
Filter expression to genes within this genome.
count_file
str (default: 'filtered_feature_bc_matrix.h5')
Which file in the passed directory to use as the count file. Typically would be one of: ‘filtered_feature_bc_matrix.h5’ or ‘raw_feature_bc_matrix.h5’.
library_id
str | None (default: None)
Identifier for the visium library. Can be modified when concatenating multiple adata objects.
source_image_path
str | Path | None (default: None)
Path to the high-resolution tissue image. Path will be included in .uns["spatial"][library_id]["metadata"]["source_image_path"].

sc.pl.spatial(adata, img_key = "hires",color=['total_counts', 'n_genes_by_counts'])

HDF5 Feature-Barcode Matrix Format -Software -Spatial Gene Expression -Official 10x Genomics Support HDF5 Feature-Barcode Matrix Format -Software -Spatial Gene Expression -Official 10x Genomics Supporthttps://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/advanced/h5_matrices

Spatial Outputs -Software -Spatial Gene Expression -Official 10x Genomics Supporthttps://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/output/spatial

 
#https://scanpy-tutorials.readthedocs.io/en/multiomics/analysis-visualization-spatial.html
#
#conda activate squidpy
import scanpy as sc
import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib import rcParams
import seaborn as sb
 
import SpatialDE
 
plt.rcParams['figure.figsize']=(8,8)
 
%load_ext autoreload
%autoreload 2
 
#sc.read_visium() 
#
adata = sc.datasets.visium_sge('V1_Human_Lymph_Node')
adata.var_names_make_unique()