APIs

Import and Initialization

[1]:

import pysodb

[2]:

# to access what datasets are privided by SODB,
# one can either use the command-line version of sodb like following,
# or directly visiting the GUI website: https://gene.ai.tencent.com/SpatialOmics/

[3]:

# Initialization
sodb = pysodb.SODB()

List datasets

[4]:

# show the all datasets
sodb.list_dataset()

[4]:

['rodriques2019slide',
 'Dhainaut2022Spatial',
 'eng2019transcriptome',
 'gut2018multiplexed',
 'Gouin2021An',
 'maynard2021trans',
 'Beechem2022High',
 'stahl2016visualization',
 'carlberg2019exploring',
 'lohoff2021integration',
 'Shah2016InSitu',
 'xia2022the',
 'Garcia2021Mapping',
 'mantri2021spatiotemporal',
 'ortiz2020molecular',
 'hartmann2020single_cell',
 'liu2020high',
 'berglund2018spatial',
 'Goltsev2028deep',
 'schurch2020coordinated',
 'asp2017spatial',
 'zhang2021spatially',
 'ji2020multimodal',
 'Buzzi2022Spatial',
 'Fang2022Conservation',
 'Biermann2022Dissecting',
 'wang2022high',
 'codeluppi2018spatial',
 'MALDI_lung',
 'mcCaffrey2022the',
 '10x',
 'bergenstrahle2021super',
 'hildebrandt2021spatial',
 'chen2020spatial',
 'maniatis2019spatiotemporal',
 'hunter2021spatially',
 'Pascual2021Dietary',
 'he2020integrating',
 'Wang2018three',
 'keren2018a',
 'fawkner2021spatiotemporal',
 'MALDI_seed',
 'asp2019a',
 'Wang2018Three_1k',
 'chen2022spatiotemporal',
 'Tower2021Spatial',
 'moffitt2018molecular',
 'guilliams2022spatial',
 'kuett2021three',
 'yuan2021seam',
 'chen2022spatiotemporal_compre_20',
 'liu2022reproducible',
 'Sun2022Excitatory',
 'Barkley2022Cancer',
 'lin2018highly',
 'Sanchez2021A',
 'kvastad2021the',
 'xia2019spatial',
 'scispace',
 'Joglekar2021A',
 'chen2021dissecting',
 'zhao2022spatial',
 'Kleshchevnikov2022Cell2location',
 'Vickovic2019high',
 'niehaus2019transmission',
 'Marshall2022High_mouse',
 'Juntaro2022MEK',
 'Ratz2022Clonal',
 'parigi2022the',
 'moncada2020integrating',
 'Kadur2022Human',
 'MALDI_kidney',
 'stickels2020highly',
 'Dixon2022Spatially',
 'thrane2018spatially',
 'Konieczny2022Interleukin',
 'desi_metaspace',
 'chen2021decoding',
 'wang2021easi',
 'risom2022transition',
 'liu2022spatiotemporal',
 'MALDI_brain',
 'Lebrigand2022The',
 'gracia2021genome',
 'Misra2021Characterizing',
 'Melo2021Integrating',
 'Navarro2020Spatial',
 'Marshall2022High_human',
 'backdahl2021spatial']

List datasets by category

[5]:

# show the spatial proteomics datasets
sodb.list_dataset_by_category('Spatial Proteomics')

[5]:

['Goltsev2028deep',
 'kuett2021three',
 'schurch2020coordinated',
 'risom2022transition',
 'keren2018a',
 'mcCaffrey2022the',
 'liu2022reproducible',
 'hartmann2020single_cell',
 'lin2018highly',
 'gut2018multiplexed']

[6]:

# show the spatial transcriptomics datasets
sodb.list_dataset_by_category('Spatial Transcriptomics')

[6]:

['he2020integrating',
 'Kleshchevnikov2022Cell2location',
 'Vickovic2019high',
 'Marshall2022High_mouse',
 'Juntaro2022MEK',
 'Wang2018three',
 'asp2017spatial',
 'Ratz2022Clonal',
 'rodriques2019slide',
 'ji2020multimodal',
 'zhang2021spatially',
 'parigi2022the',
 'fawkner2021spatiotemporal',
 'Dhainaut2022Spatial',
 'eng2019transcriptome',
 'moncada2020integrating',
 'Gouin2021An',
 'Buzzi2022Spatial',
 'Fang2022Conservation',
 'maynard2021trans',
 'asp2019a',
 'Kadur2022Human',
 'Biermann2022Dissecting',
 'stickels2020highly',
 'Wang2018Three_1k',
 'chen2022spatiotemporal',
 'Dixon2022Spatially',
 'thrane2018spatially',
 'Konieczny2022Interleukin',
 'wang2022high',
 'Tower2021Spatial',
 'stahl2016visualization',
 'moffitt2018molecular',
 'chen2021decoding',
 'guilliams2022spatial',
 'codeluppi2018spatial',
 'wang2021easi',
 '10x',
 'carlberg2019exploring',
 'bergenstrahle2021super',
 'chen2022spatiotemporal_compre_20',
 'Sun2022Excitatory',
 'liu2022spatiotemporal',
 'Barkley2022Cancer',
 'Lebrigand2022The',
 'gracia2021genome',
 'hildebrandt2021spatial',
 'Sanchez2021A',
 'chen2020spatial',
 'kvastad2021the',
 'lohoff2021integration',
 'xia2019spatial',
 'maniatis2019spatiotemporal',
 'hunter2021spatially',
 'Shah2016InSitu',
 'Pascual2021Dietary',
 'scispace',
 'xia2022the',
 'Garcia2021Mapping',
 'Melo2021Integrating',
 'Misra2021Characterizing',
 'Joglekar2021A',
 'Navarro2020Spatial',
 'mantri2021spatiotemporal',
 'ortiz2020molecular',
 'chen2021dissecting',
 'Marshall2022High_human',
 'berglund2018spatial',
 'backdahl2021spatial']

[7]:

# show the spatial metabolomics datasets
sodb.list_dataset_by_category('Spatial Metabolomics')

[7]:

['desi_metaspace',
 'MALDI_seed',
 'yuan2021seam',
 'MALDI_lung',
 'niehaus2019transmission',
 'MALDI_kidney',
 'MALDI_brain']

[8]:

# show the spatial MultiOmics datasets
sodb.list_dataset_by_category('Spatial MultiOmics')

[8]:

['Beechem2022High', 'liu2020high']

[9]:

# show the spatial genomics datasets
sodb.list_dataset_by_category('Spatial Genomics')

[9]:

['zhao2022spatial']

List experiments by dataset

[10]:

# show the spatial genomics datasets
sodb.list_experiment_by_dataset('gut2018multiplexed')

[10]:

['cell_129',
 'cell_143',
 'cell_140',
 'cell_127',
 'cell_122',
 'cell_120',
 'cell_139',
 'cell_146',
 'cell_all',
 'cell_136',
 'cell_141',
 'cell_118',
 'cell_119',
 'cell_142']

load dataset

[11]:

# Load a specific dataset
sodb.load_dataset("yuan2021seam") # Load a specific dataset

download experiment[R2_pixel] in dataset[yuan2021seam]

100%|██████████| 114M/114M [00:08<00:00, 13.3MB/s]

load experiment[R2_pixel] in dataset[yuan2021seam] from /home/yzy/anaconda3/envs/SODB/lib/python3.9/site-packages/pysodb-1.0.0-py3.9.egg/pysodb/cache/yuan2021seam/R2_pixel.h5ad
download experiment[NCTC1469withIdU_Hepa1-6_pixel] in dataset[yuan2021seam]

100%|██████████| 99.1M/99.1M [00:08<00:00, 12.6MB/s]

load experiment[NCTC1469withIdU_Hepa1-6_pixel] in dataset[yuan2021seam] from /home/yzy/anaconda3/envs/SODB/lib/python3.9/site-packages/pysodb-1.0.0-py3.9.egg/pysodb/cache/yuan2021seam/NCTC1469withIdU_Hepa1-6_pixel.h5ad
download experiment[R1_pixel] in dataset[yuan2021seam]

100%|██████████| 113M/113M [00:14<00:00, 7.99MB/s]

load experiment[R1_pixel] in dataset[yuan2021seam] from /home/yzy/anaconda3/envs/SODB/lib/python3.9/site-packages/pysodb-1.0.0-py3.9.egg/pysodb/cache/yuan2021seam/R1_pixel.h5ad
download experiment[R3_pixel] in dataset[yuan2021seam]

100%|██████████| 114M/114M [00:08<00:00, 13.7MB/s]

load experiment[R3_pixel] in dataset[yuan2021seam] from /home/yzy/anaconda3/envs/SODB/lib/python3.9/site-packages/pysodb-1.0.0-py3.9.egg/pysodb/cache/yuan2021seam/R3_pixel.h5ad
download experiment[A549withBrdU_Hela_pixel] in dataset[yuan2021seam]

100%|██████████| 125M/125M [00:16<00:00, 8.11MB/s]

load experiment[A549withBrdU_Hela_pixel] in dataset[yuan2021seam] from /home/yzy/anaconda3/envs/SODB/lib/python3.9/site-packages/pysodb-1.0.0-py3.9.egg/pysodb/cache/yuan2021seam/A549withBrdU_Hela_pixel.h5ad
download experiment[R4_pixel] in dataset[yuan2021seam]

100%|██████████| 113M/113M [00:16<00:00, 7.34MB/s]

load experiment[R4_pixel] in dataset[yuan2021seam] from /home/yzy/anaconda3/envs/SODB/lib/python3.9/site-packages/pysodb-1.0.0-py3.9.egg/pysodb/cache/yuan2021seam/R4_pixel.h5ad

[11]:

{'R2_pixel': AnnData object with n_obs × n_vars = 65536 × 231
     obs: 'leiden'
     uns: 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
     obsm: 'X_pca', 'X_umap', 'spatial'
     varm: 'PCs'
     obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances',
 'NCTC1469withIdU_Hepa1-6_pixel': AnnData object with n_obs × n_vars = 65536 × 168
     obs: 'mask', 'cellidx', 'cell_label', 'leiden'
     uns: 'cell_label_colors', 'cellidx_colors', 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
     obsm: 'X_pca', 'X_umap', 'spatial'
     varm: 'PCs'
     obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances',
 'R1_pixel': AnnData object with n_obs × n_vars = 65536 × 228
     obs: 'leiden'
     uns: 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
     obsm: 'X_pca', 'X_umap', 'spatial'
     varm: 'PCs'
     obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances',
 'R3_pixel': AnnData object with n_obs × n_vars = 65536 × 234
     obs: 'leiden'
     uns: 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
     obsm: 'X_pca', 'X_umap', 'spatial'
     varm: 'PCs'
     obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances',
 'A549withBrdU_Hela_pixel': AnnData object with n_obs × n_vars = 65536 × 274
     obs: 'leiden'
     uns: 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
     obsm: 'X_pca', 'X_umap', 'spatial'
     varm: 'PCs'
     obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances',
 'R4_pixel': AnnData object with n_obs × n_vars = 65536 × 228
     obs: 'leiden'
     uns: 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
     obsm: 'X_pca', 'X_umap', 'spatial'
     varm: 'PCs'
     obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'}

Load experiment

[12]:

# Load a specific experiment
sodb.load_experiment("yuan2021seam", "NCTC1469withIdU_Hepa1-6_pixel")

load experiment[NCTC1469withIdU_Hepa1-6_pixel] in dataset[yuan2021seam] from /home/yzy/anaconda3/envs/SODB/lib/python3.9/site-packages/pysodb-1.0.0-py3.9.egg/pysodb/cache/yuan2021seam/NCTC1469withIdU_Hepa1-6_pixel.h5ad

[12]:

AnnData object with n_obs × n_vars = 65536 × 168
    obs: 'mask', 'cellidx', 'cell_label', 'leiden'
    uns: 'cell_label_colors', 'cellidx_colors', 'leiden', 'leiden_colors', 'log1p', 'moranI', 'neighbors', 'pca', 'spatial_neighbors', 'umap'
    obsm: 'X_pca', 'X_umap', 'spatial'
    varm: 'PCs'
    obsp: 'connectivities', 'distances', 'spatial_connectivities', 'spatial_distances'