Reading and subetting organoid atlas dataset - HNOCA extended¶

Reference paper

1. Environment Set Up¶

1.1 Library upload¶

In [2]:
import numpy as np
import pandas as pd
import scanpy as sc
import seaborn as sns
import igraph as ig
import matplotlib.pyplot as plt 
from scipy.sparse import csr_matrix, isspmatrix
from datetime import datetime
import sys
sys.path.append('../')
import functions as fn

print(np.__version__)
print(pd.__version__)
print(sc.__version__)

1.26.4
2.2.2
1.10.2
In [3]:
sc.settings.verbosity = 3
sc.settings.set_figure_params(dpi=100)

1.2 Starting computations: timestamp¶

In [4]:
print(datetime.now())

2026-03-05 14:56:32.623515

2. Read input files¶

Download details: https://datasets.cellxgene.cziscience.com/aea3d8eb-82a7-4479-b902-3cca4c9300ee.h5ad

In [5]:
adata_complete = sc.read('../../../../DataDir/scRNASeq/HNOCA/cellxgene/data.h5ad')
In [6]:
adata_complete
Out[6]:
AnnData object with n_obs × n_vars = 1920454 × 35725
    obs: 'assay_differentiation', 'assay_type_differentiation', 'bio_sample', 'cell_line', 'cell_type_original', 'gm', 'id', 'individual', 'state_exact', 'suspension_type', 'tech_sample', 'treatment', 'organoid_age_days', 'publication', 'doi', 'batch', 'annot_level_1', 'annot_level_2', 'annot_level_3_rev2', 'annot_level_4_rev2', 'annot_region_rev2', 'annot_ntt_rev2', 'Hallmark_Glycolysis', 'hnoca_core', 'annot_level_2_extended', 'tissue_type', 'sex_ontology_term_id', 'donor_id', 'assay_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'tissue_ontology_term_id', 'disease_ontology_term_id', 'development_stage_ontology_term_id', 'cell_type_ontology_term_id', 'is_primary_data', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'
    var: 'gene_length', 'highly_variable', 'highly_variable_rank', 'highly_variable_nbatches', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type'
    uns: 'batch_condition', 'citation', 'default_embedding', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'title'
    obsm: 'X_scpoli', 'X_umap_scpoli'
    obsp: 'knn_scpoli_connectivities', 'knn_scpoli_distances'
In [7]:
adata_complete.var
Out[7]:
gene_length highly_variable highly_variable_rank highly_variable_nbatches feature_is_filtered feature_name feature_reference feature_biotype feature_length feature_type
ENSG00000000003 3796 False 2243.0 118 False TSPAN6 NCBITaxon:9606 gene 2396 protein_coding
ENSG00000000005 1205 True 738.5 114 False TNMD NCBITaxon:9606 gene 873 protein_coding
ENSG00000000419 3004 False 2032.0 9 False DPM1 NCBITaxon:9606 gene 1262 protein_coding
ENSG00000000457 6308 False 2532.0 15 False SCYL3 NCBITaxon:9606 gene 2916 protein_coding
ENSG00000000460 4355 False 2444.0 37 False FIRRM NCBITaxon:9606 gene 2661 protein_coding
... ... ... ... ... ... ... ... ... ... ...
ENSG00000288721 6172 False NaN 0 False ENSG00000288721 NCBITaxon:9606 gene 3684 protein_coding
ENSG00000288722 1707 False 2566.5 6 False F8A1 NCBITaxon:9606 gene 1707 protein_coding
ENSG00000288723 1015 False NaN 0 False ENSG00000288723 NCBITaxon:9606 gene 542 lncRNA
ENSG00000288724 625 False NaN 0 False ENSG00000288724 NCBITaxon:9606 gene 494 lncRNA
ENSG00000288725 3810 False NaN 0 False ENSG00000288725 NCBITaxon:9606 gene 3810 protein_coding

35725 rows × 10 columns

In [8]:
adata_complete.obs.columns
Out[8]:
Index(['assay_differentiation', 'assay_type_differentiation', 'bio_sample',
       'cell_line', 'cell_type_original', 'gm', 'id', 'individual',
       'state_exact', 'suspension_type', 'tech_sample', 'treatment',
       'organoid_age_days', 'publication', 'doi', 'batch', 'annot_level_1',
       'annot_level_2', 'annot_level_3_rev2', 'annot_level_4_rev2',
       'annot_region_rev2', 'annot_ntt_rev2', 'Hallmark_Glycolysis',
       'hnoca_core', 'annot_level_2_extended', 'tissue_type',
       'sex_ontology_term_id', 'donor_id', 'assay_ontology_term_id',
       'self_reported_ethnicity_ontology_term_id', 'tissue_ontology_term_id',
       'disease_ontology_term_id', 'development_stage_ontology_term_id',
       'cell_type_ontology_term_id', 'is_primary_data', 'cell_type', 'assay',
       'disease', 'sex', 'tissue', 'self_reported_ethnicity',
       'development_stage', 'observation_joinid'],
      dtype='object')
In [9]:
adata_complete.obs[['organoid_age_days', 'assay_differentiation', 'assay_type_differentiation', 'tissue']]
Out[9]:
organoid_age_days assay_differentiation assay_type_differentiation tissue
new_index
homosapiens_hindbrain_2020_bdrhapsodywholetranscriptomeanalysis_andersenjimena_001_d10_1016_j_cell_2020_11_017_53_14-44-80 45 Andersen, 2020 (doi: 10.1016/j.cell.2020.11.017) guided hindbrain
homosapiens_hindbrain_2020_bdrhapsodywholetranscriptomeanalysis_andersenjimena_001_d10_1016_j_cell_2020_11_017_69_48-73-96 45 Andersen, 2020 (doi: 10.1016/j.cell.2020.11.017) guided hindbrain
homosapiens_hindbrain_2020_bdrhapsodywholetranscriptomeanalysis_andersenjimena_001_d10_1016_j_cell_2020_11_017_72_19-32-61 45 Andersen, 2020 (doi: 10.1016/j.cell.2020.11.017) guided hindbrain
homosapiens_hindbrain_2020_bdrhapsodywholetranscriptomeanalysis_andersenjimena_001_d10_1016_j_cell_2020_11_017_76_10-13-76 45 Andersen, 2020 (doi: 10.1016/j.cell.2020.11.017) guided hindbrain
homosapiens_hindbrain_2020_bdrhapsodywholetranscriptomeanalysis_andersenjimena_001_d10_1016_j_cell_2020_11_017_78_37-19-8 45 Andersen, 2020 (doi: 10.1016/j.cell.2020.11.017) guided hindbrain
... ... ... ... ...
TTTGTCAGTCCAAGTT-3_ce_giandomenico2019 75 NaN NaN brain
TTTGTCAGTCTCCACT-3_ce_giandomenico2019 75 NaN NaN brain
TTTGTCAGTTTAGCTG-3_ce_giandomenico2019 75 NaN NaN brain
TTTGTCATCCTCAATT-3_ce_giandomenico2019 75 NaN NaN brain
TTTGTCATCCTGCTTG-3_ce_giandomenico2019 75 NaN NaN brain

1920454 rows × 4 columns

In [10]:
adata_complete.obs['assay_differentiation'].value_counts()
Out[10]:
assay_differentiation
Velasco, 2019 (doi: 10.1038/s41586-019-1289-x)                     790295
Yoon, 2019 (doi: 10.1038/s41592-018-0255-0)                        213860
Lancaster, 2014 (doi: 10.1038/nprot.2014.158)                      192890
Fiorenzano, 2021 (doi: 10.1038/s41467-021-27464-5); standard        78612
Quadrato, 2017 (doi: 10.1038/protex.2017.049)                       65256
Jo et al., 2016 (doi: 10.1016/j.stem.2016.07.005)                   59980
Qian et al., 2016 (doi: 10.1016/j.cell.2016.04.032)                 49275
Bhaduri, 2020 (doi: 10.1038/s41586-020-1962-0); most directed       45755
Bhaduri, 2020 (doi: 10.1038/s41586-020-1962-0); directed            43308
Watanabe, 2017 (doi: 10.1016/j.celrep.2017.09.047)                  27874
Huang, 2021 (doi: 10.1016/j.stem.2021.04.006)                       26693
Pasca, 2015 (doi: 10.1038/nmeth.3415)                               20899
Miura, 2020 (doi: 10.1038/s41587-020-00763-w)                       19011
Pellegrini, 2020 (doi: 10.1126/science.aaz5626); hChPO              15577
Trujillo, 2019 (doi: 10.1016/j.stem.2019.08.002)                    14903
Quadrato, 2023 (doi: no_doi)                                        14285
Fiorenzano, 2021 (doi:10.1038/s41467-021-27464-5); silk+laminin     14130
Fiorenzano, 2021 (doi: 10.1038/s41467-021-27464-5); silk            14052
Andersen, 2020 (doi: 10.1016/j.cell.2020.11.017)                    13217
Pellegrini, 2020 (doi: 10.1126/science.aaz5626); hCO                12214
Xiang, 2019 (doi: 10.1016/j.stem.2018.12.015)                       10568
Esk, 2020 (doi: 10.1126/science.abb5390)                             8599
Qian, 2020 (doi: 10.1016/j.stem.2020.02.002)                         6381
Birey, 2017 (doi: 10.1038/nature22330)                               4506
Andersen, 2020 (doi: 10.1016/j.cell.2020.11.017); without DAPT       3486
Sawada, 2020 (doi: 10.1038/s41380-020-0844-z)                        1441
Marton, 2019 (doi: 10.1038/s41593-018-0316-9)                         279
Name: count, dtype: int64
In [14]:
# ---- Barplot 1: Number of cells per organoid age ----
age_counts = (
    adata_complete.obs['organoid_age_days']
    .value_counts()
    .sort_index()
)

plt.figure(figsize=(16, 6))  # wider
plt.bar(age_counts.index.astype(str), age_counts.values)
plt.xlabel("Organoid Age (Days)", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Organoid Age", fontsize=16)
plt.xticks(rotation=45, fontsize=12)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()


# ---- Barplot 2: Number of cells per publication protocol ----
protocol_counts = (
    adata_complete.obs['assay_differentiation']
    .value_counts()
)

plt.figure(figsize=(24, 7))  # much wider
plt.bar(protocol_counts.index.astype(str), protocol_counts.values)
plt.xlabel("Publication Protocol", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Publication Protocol", fontsize=16)
plt.xticks(rotation=90, fontsize=11)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()

Filtering by both protocol and age of organoids

In [41]:
authors_keep = [
    "Esk, 2020",
    "Pellegrini, 2020",
    "Velasco, 2019",
    "Pasca, 2015",
    "Trujillo, 2019",
    "Yoon, 2019",
    "Qian, 2020",
    "Bhaduri, 2020",
    "Birey, 2017"
]

mask_author = adata_complete.obs["assay_differentiation"].str.contains("|".join(authors_keep), na=False)
mask_age = adata_complete.obs["organoid_age_days"] <= 50
In [53]:
# ---- Barplot 1: Number of cells per organoid age ----
age_counts = (
    adata_complete[mask_author & mask_age].obs['organoid_age_days']
    .value_counts()
    .sort_index()
)

plt.figure(figsize=(16, 6))  # wider
plt.bar(age_counts.index.astype(str), age_counts.values)
plt.xlabel("Organoid Age (Days)", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Organoid Age", fontsize=16)
plt.xticks(rotation=45, fontsize=12)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()


# ---- Barplot 2: Number of cells per publication protocol ----
protocol_counts = (
    adata_complete[mask_author & mask_age].obs['assay_differentiation']
    .value_counts()
)

plt.figure(figsize=(24, 7))  # much wider
plt.bar(protocol_counts.index.astype(str), protocol_counts.values)
plt.xlabel("Publication Protocol", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Publication Protocol", fontsize=16)
plt.xticks(rotation=90, fontsize=11)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()

# ---- Barplot 3: Number of cells per tissue ----
tissue_counts = (
    adata_complete[mask_author & mask_age].obs['tissue']
    .value_counts()
)

plt.figure(figsize=(24, 7))  # much wider
plt.bar(tissue_counts.index.astype(str), tissue_counts.values)
plt.xlabel("Tissue", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Tissue", fontsize=16)
plt.xticks(rotation=90, fontsize=11)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()

Some of the protocols were lost with the filtering by organoids age, e.g. Pasca like is it possible to see:

In [54]:
mask_Pasca = adata_complete.obs["assay_differentiation"].str.contains("Pasca", na=False)

age_counts = (
    adata_complete[mask_Pasca].obs['organoid_age_days']
    .value_counts()
    .sort_index()
)

plt.figure(figsize=(16, 6))  # wider
plt.bar(age_counts.index.astype(str), age_counts.values)
plt.xlabel("Organoid Age (Days)", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Organoid Age - Pasca protocol", fontsize=16)
plt.xticks(rotation=45, fontsize=12)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()

Filtering by both protocol and age of organoids + tissue = "cerabral cortex"

In [57]:
mask_tissue = adata_complete.obs["tissue"].str.contains("cerebral cortex")
adata_complete.obs[mask_author & mask_age & mask_tissue]
Out[57]:
assay_differentiation assay_type_differentiation bio_sample cell_line cell_type_original gm id individual state_exact suspension_type ... cell_type_ontology_term_id is_primary_data cell_type assay disease sex tissue self_reported_ethnicity development_stage observation_joinid
new_index
homosapiens_None_2020_10x3v2_bhaduriaparna_001_d10_1038_s41586_020_1962_0_2_H28126XWeek5_CGAGAAGCACGAAGCA Bhaduri, 2020 (doi: 10.1038/s41586-020-1962-0)... guided homosapiens_None_2020_10x3v2_bhaduriaparna_001... custom_H28126 Excitatory Neuron_Pan-neuronal unknown homosapiens_None_2020_10x3v2_bhaduriaparna_001... unknown unknown cell ... CL:0000679 True glutamatergic neuron 10x 3' v2 normal male cerebral cortex unknown unknown P<-*kQg=GU
homosapiens_None_2020_10x3v2_bhaduriaparna_001_d10_1038_s41586_020_1962_0_11_H28126XWeek5_GCGCGATGTTTGCATG Bhaduri, 2020 (doi: 10.1038/s41586-020-1962-0)... guided homosapiens_None_2020_10x3v2_bhaduriaparna_001... custom_H28126 Radial Glia_Glycolytic RG unknown homosapiens_None_2020_10x3v2_bhaduriaparna_001... unknown unknown cell ... CL:0000681 True radial glial cell 10x 3' v2 normal male cerebral cortex unknown unknown |1}k6aZWxK
homosapiens_None_2020_10x3v2_bhaduriaparna_001_d10_1038_s41586_020_1962_0_62_H28126XWeek5_TGGGCGTCAGCGTCCA Bhaduri, 2020 (doi: 10.1038/s41586-020-1962-0)... guided homosapiens_None_2020_10x3v2_bhaduriaparna_001... custom_H28126 Radial Glia_Pan-radial glia unknown homosapiens_None_2020_10x3v2_bhaduriaparna_001... unknown unknown cell ... CL:0000681 True radial glial cell 10x 3' v2 normal male cerebral cortex unknown unknown cS409x?OB7
homosapiens_None_2020_10x3v2_bhaduriaparna_001_d10_1038_s41586_020_1962_0_64_H28126XWeek5_ACAGCCGAGTTAACGA Bhaduri, 2020 (doi: 10.1038/s41586-020-1962-0)... guided homosapiens_None_2020_10x3v2_bhaduriaparna_001... custom_H28126 Radial Glia_Hindbrain RG unknown homosapiens_None_2020_10x3v2_bhaduriaparna_001... unknown unknown cell ... CL:0000681 True radial glial cell 10x 3' v2 normal male cerebral cortex unknown unknown vMA|@gFJwJ
homosapiens_None_2020_10x3v2_bhaduriaparna_001_d10_1038_s41586_020_1962_0_120_H28126XWeek5_ACCAGTAGTGTCAATC Bhaduri, 2020 (doi: 10.1038/s41586-020-1962-0)... guided homosapiens_None_2020_10x3v2_bhaduriaparna_001... custom_H28126 Radial Glia_Pan-radial glia unknown homosapiens_None_2020_10x3v2_bhaduriaparna_001... unknown unknown cell ... CL:0000681 True radial glial cell 10x 3' v2 normal male cerebral cortex unknown unknown >adXpfOd|c
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
homosapiens_cerebralcortex_2019_10x3v2_trujillocleber_001_d10_1016_j_stem_2019_08_002_4826_TTTGTCACAATGTAAG-1 Trujillo, 2019 (doi: 10.1016/j.stem.2019.08.002) guided unknown unknown unknown unknown homosapiens_cerebralcortex_2019_10x3v2_trujill... unknown unknown cell ... unknown True unknown 10x 3' v2 normal male cerebral cortex unknown unknown aCWAuk2C05
homosapiens_cerebralcortex_2019_10x3v2_trujillocleber_001_d10_1016_j_stem_2019_08_002_4827_TTTGTCACAGCGATCC-1 Trujillo, 2019 (doi: 10.1016/j.stem.2019.08.002) guided unknown unknown unknown unknown homosapiens_cerebralcortex_2019_10x3v2_trujill... unknown unknown cell ... unknown True unknown 10x 3' v2 normal male cerebral cortex unknown unknown 2~!ndBKC|;
homosapiens_cerebralcortex_2019_10x3v2_trujillocleber_001_d10_1016_j_stem_2019_08_002_4830_TTTGTCAGTATATGGA-1 Trujillo, 2019 (doi: 10.1016/j.stem.2019.08.002) guided unknown unknown unknown unknown homosapiens_cerebralcortex_2019_10x3v2_trujill... unknown unknown cell ... unknown True unknown 10x 3' v2 normal male cerebral cortex unknown unknown 4+l4OnHG2I
homosapiens_cerebralcortex_2019_10x3v2_trujillocleber_001_d10_1016_j_stem_2019_08_002_4831_TTTGTCAGTGGCGAAT-1 Trujillo, 2019 (doi: 10.1016/j.stem.2019.08.002) guided unknown unknown unknown unknown homosapiens_cerebralcortex_2019_10x3v2_trujill... unknown unknown cell ... unknown True unknown 10x 3' v2 normal male cerebral cortex unknown unknown ke;ZPSti6X
homosapiens_cerebralcortex_2019_10x3v2_trujillocleber_001_d10_1016_j_stem_2019_08_002_4832_TTTGTCAGTTAAAGTG-1 Trujillo, 2019 (doi: 10.1016/j.stem.2019.08.002) guided unknown unknown unknown unknown homosapiens_cerebralcortex_2019_10x3v2_trujill... unknown unknown cell ... unknown True unknown 10x 3' v2 normal male cerebral cortex unknown unknown EB)yLlt&m$

46221 rows × 43 columns

In [44]:
# ---- Barplot 1: Number of cells per organoid age ----
age_counts = (
    adata_complete[mask_author & mask_age & mask_tissue].obs['organoid_age_days']
    .value_counts()
    .sort_index()
)

plt.figure(figsize=(16, 6))  # wider
plt.bar(age_counts.index.astype(str), age_counts.values)
plt.xlabel("Organoid Age (Days)", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Organoid Age", fontsize=16)
plt.xticks(rotation=45, fontsize=12)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()


# ---- Barplot 2: Number of cells per publication protocol ----
protocol_counts = (
    adata_complete[mask_author & mask_age & mask_tissue].obs['assay_differentiation']
    .value_counts()
)

plt.figure(figsize=(24, 7))  # much wider
plt.bar(protocol_counts.index.astype(str), protocol_counts.values)
plt.xlabel("Publication Protocol", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Publication Protocol", fontsize=16)
plt.xticks(rotation=90, fontsize=11)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()

# ---- Barplot 3: Number of cells per tissue ----
tissue_counts = (
    adata_complete[mask_author & mask_age & mask_tissue].obs['tissue']
    .value_counts()
)

plt.figure(figsize=(24, 7))  # much wider
plt.bar(tissue_counts.index.astype(str), tissue_counts.values)
plt.xlabel("Tissue", fontsize=14)
plt.ylabel("Number of Cells", fontsize=14)
plt.title("Number of Cells per Tissue", fontsize=16)
plt.xticks(rotation=90, fontsize=11)
plt.yticks(fontsize=12)
plt.tight_layout()
plt.show()
In [55]:
adata_protocol_age = adata_complete[mask_author & mask_age].copy()
In [56]:
adata_protocol_age
Out[56]:
AnnData object with n_obs × n_vars = 336027 × 35725
    obs: 'assay_differentiation', 'assay_type_differentiation', 'bio_sample', 'cell_line', 'cell_type_original', 'gm', 'id', 'individual', 'state_exact', 'suspension_type', 'tech_sample', 'treatment', 'organoid_age_days', 'publication', 'doi', 'batch', 'annot_level_1', 'annot_level_2', 'annot_level_3_rev2', 'annot_level_4_rev2', 'annot_region_rev2', 'annot_ntt_rev2', 'Hallmark_Glycolysis', 'hnoca_core', 'annot_level_2_extended', 'tissue_type', 'sex_ontology_term_id', 'donor_id', 'assay_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'tissue_ontology_term_id', 'disease_ontology_term_id', 'development_stage_ontology_term_id', 'cell_type_ontology_term_id', 'is_primary_data', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'
    var: 'gene_length', 'highly_variable', 'highly_variable_rank', 'highly_variable_nbatches', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type'
    uns: 'batch_condition', 'citation', 'default_embedding', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'title'
    obsm: 'X_scpoli', 'X_umap_scpoli'
    obsp: 'knn_scpoli_connectivities', 'knn_scpoli_distances'
In [58]:
adata_protocol_age_tissue = adata_complete[mask_author & mask_age & mask_tissue].copy()
adata_protocol_age_tissue
Out[58]:
AnnData object with n_obs × n_vars = 46221 × 35725
    obs: 'assay_differentiation', 'assay_type_differentiation', 'bio_sample', 'cell_line', 'cell_type_original', 'gm', 'id', 'individual', 'state_exact', 'suspension_type', 'tech_sample', 'treatment', 'organoid_age_days', 'publication', 'doi', 'batch', 'annot_level_1', 'annot_level_2', 'annot_level_3_rev2', 'annot_level_4_rev2', 'annot_region_rev2', 'annot_ntt_rev2', 'Hallmark_Glycolysis', 'hnoca_core', 'annot_level_2_extended', 'tissue_type', 'sex_ontology_term_id', 'donor_id', 'assay_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'tissue_ontology_term_id', 'disease_ontology_term_id', 'development_stage_ontology_term_id', 'cell_type_ontology_term_id', 'is_primary_data', 'cell_type', 'assay', 'disease', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage', 'observation_joinid'
    var: 'gene_length', 'highly_variable', 'highly_variable_rank', 'highly_variable_nbatches', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype', 'feature_length', 'feature_type'
    uns: 'batch_condition', 'citation', 'default_embedding', 'organism', 'organism_ontology_term_id', 'schema_reference', 'schema_version', 'title'
    obsm: 'X_scpoli', 'X_umap_scpoli'
    obsp: 'knn_scpoli_connectivities', 'knn_scpoli_distances'

3. Savings¶

In [59]:
print(datetime.now())

2026-03-05 16:04:03.561061
In [60]:
adata_protocol_age.write("../../../../DataDir/ExternalData/SingleCellData/HNOCA_protocol_age.h5ad")
In [61]:
adata_protocol_age_tissue.write("../../../../DataDir/ExternalData/SingleCellData/HNOCA_protocol_age_tissue.h5ad")
In [ ]: