Exploring and mapping the HNOCA

The Human Neural Organoid Cell Atlas (HNOCA) is a continuously expanding collection of single-cell RNA-seq data from human neural organoids. Exploring the dataset will give you a sense of the diversity of cell types present in human brain organoids and mapping to a primary reference uncovers what fraction of human neural diversity can be captured in vitro.

In this notebook, we will explore the HNOCA dataset, compute glycolysis scores and show you how to perform query-to-reference to an atlas of the developing human brain (Braun at al.). First, let's load some libraries and the data.

import os
import scarches
import scanpy as sc
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import hnoca.map as hmap
import hnoca.stats as stats

os.chdir("/home/fleckj/scratch/hnoca/")

hnoca_adata = sc.read("HNOCA_hv2k.h5ad")
print(hnoca_adata)

AnnData object with n_obs × n_vars = 1770578 × 2000
    obs: 'assay_sc', 'assay_differentiation', 'assay_type_differentiation', 'bio_sample', 'cell_line', 'cell_type', 'development_stage', 'disease', 'ethnicity', 'gm', 'id', 'individual', 'organ', 'organism', 'sex', 'state_exact', 'sample_source', 'source_doi', 'suspension_type_original', 'tech_sample', 'treatment', 'assay_sc_original', 'cell_line_original', 'cell_type_original', 'development_stage_original', 'disease_original', 'ethnicity_original', 'organ_original', 'organism_original', 'sex_original', 'suspension_type', 'obs_names_original', 'organoid_age_days', 'publication', 'doi', 'batch', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'leiden_pca_unintegrated_1', 'leiden_pca_unintegrated_80', 'leiden_pca_rss_1', 'leiden_pca_rss_80', 'snapseed_pca_unintegrated_level_1', 'snapseed_pca_unintegrated_level_2', 'snapseed_pca_unintegrated_level_3', 'snapseed_pca_unintegrated_level_4', 'snapseed_pca_unintegrated_level_5', 'snapseed_pca_unintegrated_level_12', 'snapseed_pca_unintegrated_level_123', 'snapseed_pca_unintegrated_level_1234', 'snapseed_pca_unintegrated_level_12345', 'snapseed_pca_rss_level_1', 'snapseed_pca_rss_level_2', 'snapseed_pca_rss_level_3', 'snapseed_pca_rss_level_4', 'snapseed_pca_rss_level_5', 'snapseed_pca_rss_level_12', 'snapseed_pca_rss_level_123', 'snapseed_pca_rss_level_1234', 'snapseed_pca_rss_level_12345', 'leiden_scpoli_1', 'leiden_scpoli_80', 'snapseed_scpoli_level_1', 'snapseed_scpoli_level_2', 'snapseed_scpoli_level_3', 'snapseed_scpoli_level_4', 'snapseed_scpoli_level_5', 'snapseed_scpoli_level_12', 'snapseed_scpoli_level_123', 'snapseed_scpoli_level_1234', 'snapseed_scpoli_level_12345', 'annot_region_rev2', 'annot_level_3_rev2', 'annot_level_4_rev2', 'annot_ntt_rev2'
    var: 'ensembl', 'gene_symbol', 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'gene_length', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm', 'highly_variable_nbatches'
    uns: 'annot_region_rev2_colors', 'hvg', 'knn_pca_rss', 'knn_pca_unintegrated', 'knn_scpoli', 'log1p'
    obsm: 'X_pca_rss', 'X_pca_unintegrated', 'X_rss', 'X_scpoli', 'X_umap_pca_rss', 'X_umap_pca_unintegrated', 'X_umap_scpoli'
    layers: 'counts', 'counts_lengthnorm', 'lognorm'
    obsp: 'knn_pca_rss_connectivities', 'knn_pca_rss_distances', 'knn_pca_unintegrated_connectivities', 'knn_pca_unintegrated_distances', 'knn_scpoli_connectivities', 'knn_scpoli_distances'

The most common and straight forward thing to check on a single-cell dataset is to explore gene expression patterns. For instance, we can plot the expression of some marker genes on a UMAP embedding.

hnoca_adata.obsm["X_umap"] = hnoca_adata.obsm["X_umap_scpoli"].copy()
sc.pl.umap(
    hnoca_adata, 
    color=["FOXG1", "EMX1", "BCL11B", "SATB2", "CA8", "SLC17A6"],
    color_map="inferno",
    ncols=3,
)