Developing Mouse - Visual Cortex 10X scRNASeq analysis: clustering and annotations

The Developing Mouse - Visual Cortex taxonomy is derived from a single cell transcriptomic dataset containing 568,654 cells from donors ranging in age from embryonic to adult. The cell-type assignment is a four level taxonomy defining 15 classes, 40 subclasses, 148 clusters and 714 subclusters. This cell-type assignment is broadly consistent with those from the previous studies from the Allen Institute for Brain Science, Whole Mouse Brain (WMB) and the Broad Institute whole mouse brain taxonomy at the subclass level while providing finer cell-type and temporal resolutions with additional subcluster annotations.

To generate this developing taxonomy, we applied the Quality Control (QC) and post-integration QC pipelines. Integration and label transfer of scRNA-seq between ages was performed using Seurat/scVI. A detailed cell type annotation table accompanies the taxonomy, including hierarchical membership and anatomical localization.

You need to be connected to the internet to run this notebook or connected to a cache that has the Developing Mouse data downloaded already.

The notebook presented here shows quick visualizations from precomputed metadata in the atlas, exploring both cell, donor, and library metadata as well as the taxonomy. We also plot visualizations for the various data and metadata in a Uniform Manifold Approximation and Projection (UMAP). For examples on accessing the expression matrices, specifically selecting genes from expression matrices, see the general_accessing_10x_snRNASeq_tutorial.ipynb tutorial/example. In a related tutorial, we also show how to access and use Developing Mouse - Visual Cortex gene expression data.

import pandas as pd
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
from typing import Tuple, Optional

from abc_atlas_access.abc_atlas_cache.abc_project_cache import AbcProjectCache

We will interact with the data using the AbcProjectCache. This cache object downloads data requested by the user, tracks which files have already been downloaded to your local system, and serves the path to the requested data on disk. For metadata, the cache can also directly serve up a Pandas DataFrame. See the getting_started notebook for more details on using the cache including installing it if it has not already been.

Change the download_base variable to where you would like to download the data in your system or a location where a cache is already available.

download_base = Path('../../data/abc_atlas')
abc_cache = AbcProjectCache.from_cache_dir(
    download_base,
)

abc_cache.current_manifest

'releases/20260131/manifest.json'

Data overview

We’ll quickly walk through the data we will be using in this notebook. The Developing Mouse 10X dataset is located across two directories listed in the ABCProjectCache. These data are the metadata and data for the cells and the metadata defining the taxonomy. We will be using data and metadata from the following directories:

• Developing-Mouse-Vis-Cortex-10X

• Developing-Mouse-Vis-Cortex-taxonomy

Below we list the data and metadata in the Developing Mouse dataset.

print("Developing-Mouse-Vis-Cortex-10X: gene expression data (h5ad)\n\t", abc_cache.list_expression_matrix_files(directory='Developing-Mouse-Vis-Cortex-10X'))
print("Developing-Mouse-Vis-Cortex-10X: metadata (csv)\n\t", abc_cache.list_metadata_files(directory='Developing-Mouse-Vis-Cortex-10X'))

Developing-Mouse-Vis-Cortex-10X: gene expression data (h5ad)
	 ['Developing-Mouse-Vis-Cortex-10X/log2', 'Developing-Mouse-Vis-Cortex-10X/raw']
Developing-Mouse-Vis-Cortex-10X: metadata (csv)
	 ['cell_metadata', 'donor', 'example_gene_expression', 'gene', 'library', 'value_sets']

We will also use metadata from the Developing-Mouse-Vis-Cortex-taxonomy directory. Below is the list of available files:

print("Developing-Mouse-Vis-Cortex-taxonomy: metadata (csv)\n\t", abc_cache.list_metadata_files(directory='Developing-Mouse-Vis-Cortex-taxonomy'))

Developing-Mouse-Vis-Cortex-taxonomy: metadata (csv)
	 ['cell_2d_embedding_coordinates', 'cell_to_cluster_membership', 'cluster', 'cluster_annotation_term', 'cluster_annotation_term_set', 'cluster_to_cluster_annotation_membership', 'de_genes']

Cell metadata

Essential cell metadata is stored as a CSV file that we load as a Pandas DataFrame. Each row represents one cell indexed by a cell label. The cell label is the concatenation of barcode and name of the sample. In this context, the sample is the barcoded cell sample that represents a single load into one port of the 10x Chromium. Note that cell barcodes are only unique within a single barcoded cell sample and that the same barcode can be reused.

Each cell is associated with a library label, donor label, alignment_job_id, feature_matrix_label and dataset_label identifying which data package this cell is part of. This metadata file will be combined with other metadata files that ship with this package to add information associated with the donor, UMAP coordinates, taxonomy assignments, and more.

Below, we load the first of the metadata used in this tutorial. This represents the cell metadata for the aligned dataset.

The command we use below both downloads the data if it is not already available in the local cache and loads the data as a Pandas DataFrame. This pattern of loading metadata is repeated throughout the tutorials.

cell = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-10X',
    file_name='cell_metadata'
).set_index('cell_label')
print("Number of cells = ", len(cell))
cell.head()

cell_metadata.csv: 100%|██████████| 105M/105M [00:03<00:00, 30.0MMB/s]   

Number of cells =  568654

	cell_barcode	barcoded_cell_sample_label	library_label	alignment_id	log.qc.score.cr6	synchronized_age	donor_label	dataset_label	feature_matrix_label
cell_label
AAACCCAAGGATTTAG-898_A02	AAACCCAAGGATTTAG	898_A02	L8TX_211028_01_G04	1157582505	338.986629	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X
AAACCCACACATTCTT-898_A02	AAACCCACACATTCTT	898_A02	L8TX_211028_01_G04	1157582505	425.642457	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X
AAACCCACACTTACAG-898_A02	AAACCCACACTTACAG	898_A02	L8TX_211028_01_G04	1157582505	342.366675	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X
AAACGAATCACTCTTA-898_A02	AAACGAATCACTCTTA	898_A02	L8TX_211028_01_G04	1157582505	353.204604	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X
AAACGAATCGACCAAT-898_A02	AAACGAATCGACCAAT	898_A02	L8TX_211028_01_G04	1157582505	355.052147	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X

We can use pandas groupby function to see how many unique items are associated for each field and list them out if the number of unique items is small.

def print_column_info(df):
    
    for c in df.columns:
        grouped = df[[c]].groupby(c).count()
        members = ''
        if len(grouped) < 30:
            members = str(list(grouped.index))
        print("Number of unique %s = %d %s" % (c, len(grouped), members))

print_column_info(cell)

Number of unique cell_barcode = 527176 
Number of unique barcoded_cell_sample_label = 91 
Number of unique library_label = 91 
Number of unique alignment_id = 91 
Number of unique log.qc.score.cr6 = 496566 
Number of unique synchronized_age = 35 
Number of unique donor_label = 53 
Number of unique dataset_label = 1 ['Developing-Mouse-Vis-Cortex-10X']
Number of unique feature_matrix_label = 1 ['Developing-Mouse-Vis-Cortex-10X']

Donor and Library metadata

The first two associated metadata we load are the donor and library tables. The donor table contains species, sex, and age information. The library table contains information on 10X methods and brain region of interest the tissue was extracted from.

Below we load the donor metadata. Note that we flatten the values of P54 and above to P56 or “Adult” to match the published analysis. The original age values are available in the donor table.

donor = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-10X',
    file_name='donor'
).set_index('donor_label')

for idx, row in donor.iterrows():
    if row['donor_age'] in ['P54', 'P59', 'P60', 'P61', 'P68']:
        donor.loc[idx, 'donor_age'] = 'P56'

donor.head()

donor.csv:   0%|          | 0.00/5.08k [00:00<?, ?MB/s]

donor.csv: 100%|██████████| 5.08k/5.08k [00:00<00:00, 96.1kMB/s]

	donor_species	species_scientific_name	species_genus	donor_sex	donor_age_value	donor_age_unit	donor_age_reference_point	donor_age	age_bin
donor_label
C57BL6J-603767	NCBITaxon:10090	Mus musculus	Mouse	Male	18.5	days	conception	E18.5	E17_E18.5
C57BL6J-620146	NCBITaxon:10090	Mus musculus	Mouse	Male	9.0	days	birth	P9	9
C57BL6J-628080	NCBITaxon:10090	Mus musculus	Mouse	Male	13.5	days	conception	E13.5	E13.5_E16.5
C57BL6J-628083	NCBITaxon:10090	Mus musculus	Mouse	Male	13.5	days	conception	E13.5	E13.5_E16.5
C57BL6J-628825	NCBITaxon:10090	Mus musculus	Mouse	Female	18.0	days	conception	E18	E17_E18.5

Next we load the library metadata. The information we will primarily use from this table are the region of interest that each library is associated with

library = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-10X',
    file_name='library'
).set_index('library_label')
library.head()

library.csv: 100%|██████████| 11.2k/11.2k [00:00<00:00, 138kMB/s]

	library_technique	barcoded_cell_sample_label	enrichment_population	cell_specimen_type	parcellation_term_identifier	region_of_interest_name	region_of_interest_label	donor_label
library_label
L8TX_200618_01_D07	10xV3.1;GEXOnly	265_B02	No FACS	Cells	MBA:385	Primary visual area	VISp	Snap25-IRES2-Cre;Ai14-536764
L8TX_200618_01_F06	10xV3.1;GEXOnly	264_A01	RFP-positive, Hoechst-positive	Cells	MBA:385	Primary visual area	VISp	Snap25-IRES2-Cre;Ai14-536763
L8TX_200618_01_G05	10xV3.1;GEXOnly	264_B01	RFP-positive, Hoechst-positive	Cells	MBA:385	Primary visual area	VISp	Snap25-IRES2-Cre;Ai14-536763
L8TX_200618_01_H05	10xV3.1;GEXOnly	265_A02	No FACS	Cells	MBA:385	Primary visual area	VISp	Snap25-IRES2-Cre;Ai14-536764
L8TX_200709_01_B03	10xV3.1;GEXOnly	282_B02	No FACS	Cells	MBA:385	Primary visual area	VISp	Snap25-IRES2-Cre;Ai14-538743

We combine the donor and library tables into an extended cell metadata table.

cell_extended = cell.join(donor, on='donor_label')
cell_extended = cell_extended.join(library, on='library_label', rsuffix='_library_table')

del cell

We use the groupby function to show the number of cells in each region of interest.

cell_extended.groupby('region_of_interest_name')[['region_of_interest_label']].count()

	region_of_interest_label
region_of_interest_name
Cerebrum - Brain stem	12813
Primary visual area	128550
Visual areas	387480
Visual areas - Posterior parietal association areas	25539
brain	14272

We can use the group by functionality to group the cells by each age.

cell_extended.groupby('age_bin')[['library_label']].count().rename(columns={'library_label': 'number_of_cells'}).loc[
    ['E11.5_E12.5', 'E13.5_E16.5', 'E17_E18.5',
     '0_1', '2', '3', '4', '5_6', '7_8', '9', '10',
     '11', '12_13', '14_15', '16', '17_19', '20_28', '54_68']]

	number_of_cells
age_bin
E11.5_E12.5	17222
E13.5_E16.5	19762
E17_E18.5	22970
0_1	28315
2	23329
3	17953
4	12744
5_6	13356
7_8	31637
9	31973
10	12146
11	11885
12_13	54048
14_15	76385
16	24198
17_19	28877
20_28	68990
54_68	72864

Adding color and feature order

Each major feature in the donor and library table is associated with unique colors and an ordering with the set of values. Below we load the value_sets DataFrame which is a mapping from the various value in the donor and species tables to those colors and orderings. We incorporate these values into the cell metadata table.

value_sets = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-10X',
    file_name='value_sets'
).set_index('label')
value_sets

value_sets.csv: 100%|██████████| 4.33k/4.33k [00:00<00:00, 66.6kMB/s]

	table	field	description	order	external_identifier	parent_label	color_hex_triplet
label
Female	donor	donor_sex	Female	1	NaN	NaN	#565353
Male	donor	donor_sex	Male	2	NaN	NaN	#ADC4C3
Mouse	donor	species_genus	Mouse	1	NCBITaxon:10088	NaN	#c941a7
Mus musculus	donor	species_scientific_name	Mus musculus	1	NCBITaxon:10090	NaN	#ffa300
WholeBrain	library	region_of_interest_label	brain	1	MBA:997	NaN	#bebebe
...	...	...	...	...	...	...	...
14_15	donor	age_bin	age bin in days	14	NaN	NaN	#2C49AD
16	donor	age_bin	age bin in days	15	NaN	NaN	#0F0F99
17_19	donor	age_bin	age bin in days	16	NaN	NaN	#6B49F9
20_28	donor	age_bin	age bin in days	17	NaN	NaN	#AE43C6
54_68	donor	age_bin	age bin in days	18	NaN	NaN	#C30000

68 rows × 7 columns

We define a convenience function to add colors for the various values in the data (e.g. unique region of interest or donor sex values).

def extract_value_set(
        cell_metadata_df: pd.DataFrame,
        input_value_set: pd.DataFrame,
        input_value_set_label: str,
        dataframe_column: Optional[str] = None
    ):
    """Add color and order columns to the cell metadata dataframe based on the input
    value set.

    Columns are added as {input_value_set_label}_color and {input_value_set_label}_order.

    Parameters
    ----------
    cell_metadata_df : pd.DataFrame
        DataFrame containing cell metadata.
    input_value_set : pd.DataFrame
        DataFrame containing the value set information.
    input_value_set_label : str
        The the column name to extract color and order information for. will be added to the cell metadata.
    """
    if dataframe_column is None:
        dataframe_column = input_value_set_label
    cell_metadata_df[f'{dataframe_column}_color'] = input_value_set[
        input_value_set['field'] == input_value_set_label
    ].loc[cell_metadata_df[dataframe_column]]['color_hex_triplet'].values
    cell_metadata_df[f'{dataframe_column}_order'] = input_value_set[
        input_value_set['field'] == input_value_set_label
    ].loc[cell_metadata_df[dataframe_column]]['order'].values

Use our function to add the relevant color and order columns to our cell_metadata table.

# Add region of interest color and order
extract_value_set(cell_extended, value_sets, 'region_of_interest_label')
# Add region of interest color and order
extract_value_set(cell_extended, value_sets, 'synchronized_age', 'donor_age')
extract_value_set(cell_extended, value_sets, 'synchronized_age')
# Add region of interest color and order
extract_value_set(cell_extended, value_sets, 'age_bin')
# Add species common name color and order
extract_value_set(cell_extended, value_sets, 'species_genus')
# Add species scientific name color and order
extract_value_set(cell_extended, value_sets, 'species_scientific_name')
# Add donor sex color and order
extract_value_set(cell_extended, value_sets, 'donor_sex')
cell_extended.head()

	cell_barcode	barcoded_cell_sample_label	library_label	alignment_id	log.qc.score.cr6	synchronized_age	donor_label	dataset_label	feature_matrix_label	donor_species	...	synchronized_age_color	synchronized_age_order	age_bin_color	age_bin_order	species_genus_color	species_genus_order	species_scientific_name_color	species_scientific_name_order	donor_sex_color	donor_sex_order
cell_label
AAACCCAAGGATTTAG-898_A02	AAACCCAAGGATTTAG	898_A02	L8TX_211028_01_G04	1157582505	338.986629	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	#E6865C	3	#D38F38	2	#c941a7	1	#ffa300	1	#ADC4C3	2
AAACCCACACATTCTT-898_A02	AAACCCACACATTCTT	898_A02	L8TX_211028_01_G04	1157582505	425.642457	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	#E6865C	3	#D38F38	2	#c941a7	1	#ffa300	1	#ADC4C3	2
AAACCCACACTTACAG-898_A02	AAACCCACACTTACAG	898_A02	L8TX_211028_01_G04	1157582505	342.366675	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	#E6865C	3	#D38F38	2	#c941a7	1	#ffa300	1	#ADC4C3	2
AAACGAATCACTCTTA-898_A02	AAACGAATCACTCTTA	898_A02	L8TX_211028_01_G04	1157582505	353.204604	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	#E6865C	3	#D38F38	2	#c941a7	1	#ffa300	1	#ADC4C3	2
AAACGAATCGACCAAT-898_A02	AAACGAATCGACCAAT	898_A02	L8TX_211028_01_G04	1157582505	355.052147	E13.5	C67BL6J-606627	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	#E6865C	3	#D38F38	2	#c941a7	1	#ffa300	1	#ADC4C3	2

5 rows × 40 columns

UMAP spatial embedding

Now that we’ve merged our donor and library metadata into the main cells data, our next step is to plot these values in the Uniform Manifold Approximation and Projection (UMAP) for cells in the dataset. The UMAP is a dimension reduction technique that can be used for visualizing and exploring large-dimension datasets.

Below we load this 2-D embedding for a sub selection of our cells and merge the x-y coordinates into the extended cell metadata we are creating.

cell_2d_embedding_coordinates = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-taxonomy',
    file_name='cell_2d_embedding_coordinates'
).set_index('cell_label')
cell_2d_embedding_coordinates.head()

cell_2d_embedding_coordinates.csv: 100%|██████████| 25.7M/25.7M [00:00<00:00, 31.2MMB/s]

	x	y
cell_label
AAACCCAAGGATTTAG-898_A02	17.504883	4.327579
AAACCCACACATTCTT-898_A02	9.755900	5.404129
AAACCCACACTTACAG-898_A02	10.902691	4.540480
AAACGAATCACTCTTA-898_A02	13.066967	4.577918
AAACGAATCGACCAAT-898_A02	18.801212	-2.231872

cell_extended = cell_extended.join(cell_2d_embedding_coordinates)
cell_extended = cell_extended.sample(frac=1) # shuffle the rows for plotting purposes

del cell_2d_embedding_coordinates

We define a small helper function plot_umap to visualize the cells on the UMAP. In the examples below we will plot associated cell information colorized by donor age, sex, and region of interest.

def plot_umap(
    xx: np.ndarray,
    yy: np.ndarray,
    cc: np.ndarray = None,
    val: np.ndarray = None,
    fig_width: float = 8,
    fig_height: float = 8,
    cmap: Optional[plt.Colormap] = None,
    labels: np.ndarray = None,
    term_orders: np.ndarray = None,
    colorbar: bool = False,
    sizes: np.ndarray = None
 ) -> Tuple[plt.Figure, plt.Axes]:
    """
    Plot a scatter plot of the UMAP coordinates.

    Parameters
    ----------
    xx : array-like
        x-coordinates of the points to plot.
    yy : array-like
        y-coordinates of the points to plot.
    cc : array-like, optional
        colors of the points to plot. If None, the points will be colored by the values in `val`.
    val : array-like, optional
        values of the points to plot. If None, the points will be colored by the values in `cc`.
    fig_width : float, optional
        width of the figure in inches. Default is 8.
    fig_height : float, optional
        height of the figure in inches. Default is 8.
    cmap : str, optional
        colormap to use for coloring the points. If None, the points will be colored by the values in `cc`.
    labels : array-like, optional
        labels for the points to plot. If None, no labels will be added to the plot.
    term_orders : array-like, optional
        order of the labels for the legend. If None, the labels will be ordered by their appearance in `labels`.
    colorbar : bool, optional
        whether to add a colorbar to the plot. Default is False.
    sizes : array-like, optional
        sizes of the points to plot. If None, all points will have the same size.
    """
    if sizes is None:
        sizes = 1
    fig, ax = plt.subplots()
    fig.set_size_inches(fig_width, fig_height)

    if cmap is not None:
        scatt = ax.scatter(xx, yy, c=val, s=0.5, marker='.', cmap=cmap, alpha=sizes)
    elif cc is not None:
        scatt = ax.scatter(xx, yy, c=cc, s=0.5, marker='.', alpha=sizes)

    if labels is not None:
        from matplotlib.patches import Rectangle
        unique_label_colors = (labels + ',' + cc).unique()
        unique_labels = np.array([label_color.split(',')[0] for label_color in unique_label_colors])
        unique_colors = np.array([label_color.split(',')[1] for label_color in unique_label_colors])

        if term_orders is not None:
            unique_order = term_orders.unique()
            term_order = np.argsort(unique_order)
            unique_labels = unique_labels[term_order]
            unique_colors = unique_colors[term_order]
            
        rects = []
        for color in unique_colors:
            rects.append(Rectangle((0, 0), 1, 1, fc=color))

        legend = ax.legend(rects, unique_labels, loc=1)
        # ax.add_artist(legend)

    if colorbar:
        fig.colorbar(scatt, ax=ax)
    
    return fig, ax

Plot the various donor and library metadata available.

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['donor_sex_color'],
    labels=cell_extended['donor_sex'],
    term_orders=cell_extended['donor_sex_order'],
    fig_width=12,
    fig_height=12
)
res = ax.set_title("donor_sex")
plt.show()

Below we show the region of interest for the cells in the dataset.

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['region_of_interest_label_color'],
    labels=cell_extended['region_of_interest_label'],
    term_orders=cell_extended['region_of_interest_label_order'],
    fig_width=12,
    fig_height=12
)
res = ax.set_title("region_of_interest_label")
plt.show()

The UMAP with donor age.

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['donor_age_color'],
    labels=cell_extended['donor_age'],
    term_orders=cell_extended['donor_age_order'],
    fig_width=12,
    fig_height=12
)
res = ax.set_title("donor_age")
plt.show()

Taxonomy Information

The final set of metadata we load into our extended cell metadata file maps the cells into their assigned cluster in the taxonomy. We additionally load metadata for the clusters and compute useful information, such as the number of cells in each taxon at each level of the taxonomy.

First, we load information associated with the lowest level in the taxonomy in the taxonomy. This includes a useful alias value for each cluster as well as the number of cells in each subcluster.

cluster = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-taxonomy',
    file_name='cluster',
    dtype={'number_of_cells': 'Int64'}
).rename(columns={'label': 'cluster_annotation_term_label'}).set_index('cluster_annotation_term_label')
cluster.head()

cluster.csv: 100%|██████████| 20.5k/20.5k [00:00<00:00, 210kMB/s]

	cluster_alias	number_of_cells
cluster_annotation_term_label
CS20260131_SCLU_0001	1	77
CS20260131_SCLU_0002	2	126
CS20260131_SCLU_0003	3	184
CS20260131_SCLU_0004	4	186
CS20260131_SCLU_0005	5	25

Next, we load the table that describes the levels in the taxonomy from class at the highest to subcluster at the lowest level.

cluster_annotation_term_set = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-taxonomy',
    file_name='cluster_annotation_term_set'
).rename(columns={'label': 'cluster_annotation_term_label'})
cluster_annotation_term_set

cluster_annotation_term_set.csv: 100%|██████████| 268/268 [00:00<00:00, 2.76kMB/s]

	name	cluster_annotation_term_label	description	order	parent_term_set_label
0	class	CCN20260131_LEVEL_0	class	0	NaN
1	subclass	CCN20260131_LEVEL_1	subclass	1	CCN20260131_LEVEL_0
2	cluster	CCN20260131_LEVEL_2	cluster	2	CCN20260131_LEVEL_1
3	subcluster	CCN20260131_LEVEL_3	subcluster	3	CCN20260131_LEVEL_2

For the subclusters, we load information on the annotations for each subcluster. This also includes the term order and color information which we will use to plot later. Note the inclusion of the CCN20230722_label column. This value points to the equivalent taxon in the Whole Mouse Brain taxonomy if it exists.

cluster_annotation_term = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-taxonomy',
    file_name='cluster_annotation_term',
).rename(columns={'label': 'cluster_annotation_term_label'}).set_index('cluster_annotation_term_label')
cluster_annotation_term

cluster_annotation_term.csv: 100%|██████████| 129k/129k [00:00<00:00, 1.10MMB/s]

	name	cluster_annotation_term_set_label	cluster_annotation_term_set_name	color_hex_triplet	term_order	term_set_order	parent_term_label	parent_term_name	parent_term_set_label	CCN20230722_label
cluster_annotation_term_label
CS20260131_CLAS_009	Astro-Epen	CCN20260131_LEVEL_0	class	#594a26	9	0	NaN	NaN	NaN	CS20230722_CLAS_30
CS20260131_CLAS_013	CNU-MGE GABA	CCN20260131_LEVEL_0	class	#450099	13	0	NaN	NaN	NaN	CS20230722_CLAS_08
CS20260131_CLAS_002	CR Glut	CCN20260131_LEVEL_0	class	#919900	2	0	NaN	NaN	NaN	NaN
CS20260131_CLAS_011	CTX-CGE GABA	CCN20260131_LEVEL_0	class	#CCFF33	11	0	NaN	NaN	NaN	CS20230722_CLAS_06
CS20260131_CLAS_012	CTX-MGE GABA	CCN20260131_LEVEL_0	class	#f954ee	12	0	NaN	NaN	NaN	CS20230722_CLAS_07
...	...	...	...	...	...	...	...	...	...	...
CS20260131_SCLU_051	RG_5	CCN20260131_LEVEL_3	subcluster	#9e9ac8	51	3	CS20260131_CLUS_004	RG	CCN20260131_LEVEL_2	NaN
CS20260131_SCLU_052	RG_6	CCN20260131_LEVEL_3	subcluster	#bcbddc	52	3	CS20260131_CLUS_004	RG	CCN20260131_LEVEL_2	NaN
CS20260131_SCLU_053	RG_7	CCN20260131_LEVEL_3	subcluster	#dadaeb	53	3	CS20260131_CLUS_004	RG	CCN20260131_LEVEL_2	NaN
CS20260131_SCLU_054	RG_8	CCN20260131_LEVEL_3	subcluster	#636363	54	3	CS20260131_CLUS_004	RG	CCN20260131_LEVEL_2	NaN
CS20260131_SCLU_055	RG_9	CCN20260131_LEVEL_3	subcluster	#969696	55	3	CS20260131_CLUS_004	RG	CCN20260131_LEVEL_2	NaN

917 rows × 10 columns

Finally, we load the cluster to cluster annotation membership table. Each row in this table is a mapping between the subclusters and every level of the taxonomy it belongs to, including itself. We’ll use this table in a groupby to allow us to count up the number of clusters at each taxonomy level and sum the number of cells in each taxon in the taxonomy a all levels.

cluster_to_cluster_annotation_membership = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-taxonomy',
    file_name='cluster_to_cluster_annotation_membership'
).set_index('cluster_annotation_term_label')
membership_with_cluster_info = cluster_to_cluster_annotation_membership.join(
    cluster.reset_index().set_index('cluster_alias')[['number_of_cells']],
    on='cluster_alias'
)
membership_with_cluster_info = membership_with_cluster_info.join(cluster_annotation_term, rsuffix='_anno_term').reset_index()
membership_groupby = membership_with_cluster_info.groupby(
    ['cluster_alias', 'cluster_annotation_term_set_name']
)
membership_with_cluster_info.head()

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cluster_to_cluster_annotation_membership.csv: 100%|██████████| 189k/189k [00:00<00:00, 1.45MMB/s] 

	cluster_annotation_term_label	cluster_annotation_term_set_name	cluster_annotation_term_name	cluster_alias	cluster_annotation_term_set_label	number_of_cells	name	cluster_annotation_term_set_label_anno_term	cluster_annotation_term_set_name_anno_term	color_hex_triplet	term_order	term_set_order	parent_term_label	parent_term_name	parent_term_set_label	CCN20230722_label
0	CS20260131_CLAS_001	class	NEC	1	CCN20260131_LEVEL_0	77	NEC	CCN20260131_LEVEL_0	class	#6BAED6	1	0	NaN	NaN	NaN	NaN
1	CS20260131_CLAS_001	class	NEC	2	CCN20260131_LEVEL_0	126	NEC	CCN20260131_LEVEL_0	class	#6BAED6	1	0	NaN	NaN	NaN	NaN
2	CS20260131_CLAS_001	class	NEC	3	CCN20260131_LEVEL_0	184	NEC	CCN20260131_LEVEL_0	class	#6BAED6	1	0	NaN	NaN	NaN	NaN
3	CS20260131_CLAS_001	class	NEC	4	CCN20260131_LEVEL_0	186	NEC	CCN20260131_LEVEL_0	class	#6BAED6	1	0	NaN	NaN	NaN	NaN
4	CS20260131_CLAS_001	class	NEC	5	CCN20260131_LEVEL_0	25	NEC	CCN20260131_LEVEL_0	class	#6BAED6	1	0	NaN	NaN	NaN	NaN

From the membership table, we create three tables via a groupby. First the name of each cluster and its parents.

# term_sets = abc_cache.get_metadata_dataframe(directory='WHB-taxonomy', file_name='cluster_annotation_term_set').set_index('label')
cluster_details = membership_groupby['cluster_annotation_term_name'].first().unstack()
cluster_details = cluster_details[cluster_annotation_term_set['name']] # order columns
cluster_details.fillna('Other', inplace=True)
cluster_details.head()

cluster_annotation_term_set_name	class	subclass	cluster	subcluster
cluster_alias
1	NEC	NEC	NEC	NEC_1
2	NEC	NEC	NEC	NEC_2
3	NEC	NEC	NEC	NEC_3
4	NEC	NEC	NEC	NEC_4
5	NEC	NEC	NEC	NEC_5

Next the plotting order of each of the taxons and their parents.

cluster_order = membership_groupby['term_order'].first().unstack()
cluster_order.rename(
    columns={'class': 'class_order',
             'subclass': 'subclass_order',
             'cluster': 'cluster_order',
             'subcluster': 'subcluster_order'},
    inplace=True
)
cluster_order.head()

cluster_annotation_term_set_name	class_order	cluster_order	subclass_order	subcluster_order
cluster_alias
1	1	1	1	1
2	1	1	1	2
3	1	1	1	3
4	1	1	1	4
5	1	1	1	5

Finally, the colors we will use to plot for each of the unique taxons at all levels.

cluster_colors = membership_groupby['color_hex_triplet'].first().unstack()
cluster_colors = cluster_colors[cluster_annotation_term_set['name']]
cluster_colors.head()

cluster_annotation_term_set_name	class	subclass	cluster	subcluster
cluster_alias
1	#6BAED6	#50B4F0	#9e9ac8	#6baed6
2	#6BAED6	#50B4F0	#9e9ac8	#9ecae1
3	#6BAED6	#50B4F0	#9e9ac8	#c6dbef
4	#6BAED6	#50B4F0	#9e9ac8	#e6550d
5	#6BAED6	#50B4F0	#9e9ac8	#fd8d3c

Next, we bring it all together by loading the mapping of cells to subcluster and join into our final metadata table.

cell_to_cluster_membership = abc_cache.get_metadata_dataframe(
    directory='Developing-Mouse-Vis-Cortex-taxonomy',
    file_name='cell_to_cluster_membership',
).set_index('cell_label')
cell_to_cluster_membership.head()

cell_to_cluster_membership.csv:   0%|          | 0.00/28.5M [00:00<?, ?MB/s]

cell_to_cluster_membership.csv: 100%|██████████| 28.5M/28.5M [00:01<00:00, 26.1MMB/s]  

	cluster_alias	label
cell_label
AAACCCAAGGATTTAG-898_A02	50	CS20260131_SCLU_0050
AAACCCACACATTCTT-898_A02	122	CS20260131_SCLU_0122
AAACCCACACTTACAG-898_A02	118	CS20260131_SCLU_0118
AAACGAATCACTCTTA-898_A02	108	CS20260131_SCLU_0108
AAACGAATCGACCAAT-898_A02	690	CS20260131_SCLU_0690

We merge this table with information from our taxons.

cell_extended = cell_extended.join(cell_to_cluster_membership, rsuffix='_cell_to_cluster_membership')
cell_extended = cell_extended.join(cluster_details, on='cluster_alias')
cell_extended = cell_extended.join(cluster_colors, on='cluster_alias', rsuffix='_color')
cell_extended = cell_extended.join(cluster_order, on='cluster_alias')

del cell_to_cluster_membership

cell_extended.head()

	cell_barcode	barcoded_cell_sample_label	library_label	alignment_id	log.qc.score.cr6	synchronized_age	donor_label	dataset_label	feature_matrix_label	donor_species	...	cluster	subcluster	class_color	subclass_color	cluster_color	subcluster_color	class_order	cluster_order	subclass_order	subcluster_order
cell_label
CCTCCAAAGTAAGAGG-482_A06	CCTCCAAAGTAAGAGG	482_A06	L8TX_210107_01_D09	1157582376	241.8102	P21	Snap25-IRES2-Cre;Ai14-562459	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	5312_Microglia NN_1	5312_Microglia NN_1_3	#825f45	#CC1F4E	#a55194	#dadaeb	15	146	39	712
GAGGCAACAGCTTTCC-479_A03	GAGGCAACAGCTTTCC	479_A03	L8TX_210107_01_A09	1157582337	431.3020	P56	Snap25-IRES2-Cre;Ai14-562811	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	741_Pvalb Gaba_3	741_Pvalb Gaba_3_8	#f954ee	#b13667	#c7e9c0	#969696	12	106	31	617
ATCGGATCACCCATAA-441_B01	ATCGGATCACCCATAA	441_B01	L8TX_201120_01_H09	1178468076	457.6961	P56	Snap25-IRES2-Cre;Ai14-553679	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	437_L6 CT CTX Glut_1	437_L6 CT CTX Glut_1_15	#61e2a4	#34661F	#c7e9c0	#cedb9c	6	13	11	204
AAGAACACATTGCCGG-418_C04	AAGAACACATTGCCGG	418_C04	L8TX_201105_01_C02	1157582427	416.2293	P28	Snap25-IRES2-Cre;Ai14-555355	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	118_L2/3 IT CTX Glut_4	118_L2/3 IT CTX Glut_4_2	#FA0087	#E9530F	#de9ed6	#c7e9c0	7	45	18	453
GTGAGCCGTCTACACA-634_A03	GTGAGCCGTCTACACA	634_A03	L8TX_210506_01_D07	1157582451	370.9672	P5	Snap25-IRES2-Cre;Ai14-582040	Developing-Mouse-Vis-Cortex-10X	Developing-Mouse-Vis-Cortex-10X	NCBITaxon:10090	...	439_L6 CT CTX Glut_1	439_L6 CT CTX Glut_1_6	#61e2a4	#34661F	#756bb1	#d6616b	6	14	11	211

5 rows × 56 columns

print_column_info(cell_extended)

Number of unique cell_barcode = 527176 
Number of unique barcoded_cell_sample_label = 91 
Number of unique library_label = 91 
Number of unique alignment_id = 91 
Number of unique log.qc.score.cr6 = 496566 
Number of unique synchronized_age = 35 
Number of unique donor_label = 53 
Number of unique dataset_label = 1 ['Developing-Mouse-Vis-Cortex-10X']
Number of unique feature_matrix_label = 1 ['Developing-Mouse-Vis-Cortex-10X']
Number of unique donor_species = 1 ['NCBITaxon:10090']
Number of unique species_scientific_name = 1 ['Mus musculus']
Number of unique species_genus = 1 ['Mouse']
Number of unique donor_sex = 2 ['Female', 'Male']
Number of unique donor_age_value = 38 
Number of unique donor_age_unit = 1 ['days']
Number of unique donor_age_reference_point = 2 ['birth', 'conception']
Number of unique donor_age = 35 
Number of unique age_bin = 18 ['0_1', '10', '11', '12_13', '14_15', '16', '17_19', '2', '20_28', '3', '4', '54_68', '5_6', '7_8', '9', 'E11.5_E12.5', 'E13.5_E16.5', 'E17_E18.5']
Number of unique library_technique = 2 ['10xV3.1;GEXOnly', '10xV3;GEXOnly']
Number of unique barcoded_cell_sample_label_library_table = 91 
Number of unique enrichment_population = 5 ['Calcein-positive, Hoechst-positive', 'Hoechst-positive', 'No FACS', 'RFP-positive, DAPI-negative', 'RFP-positive, Hoechst-positive']
Number of unique cell_specimen_type = 1 ['Cells']
Number of unique parcellation_term_identifier = 5 ['MBA:22|MBA:669', 'MBA:343|MBA:567', 'MBA:385', 'MBA:669', 'MBA:997']
Number of unique region_of_interest_name = 5 ['Cerebrum - Brain stem', 'Primary visual area', 'Visual areas', 'Visual areas - Posterior parietal association areas', 'brain']
Number of unique region_of_interest_label = 5 ['CH-BS', 'VIS', 'VIS-PTLp', 'VISp', 'WholeBrain']
Number of unique donor_label_library_table = 53 
Number of unique region_of_interest_label_color = 5 ['#0059CC', '#08858C', '#26AEFF', '#B0F0FF', '#bebebe']
Number of unique region_of_interest_label_order = 5 [1, 3, 4, 5, 6]
Number of unique donor_age_color = 35 
Number of unique donor_age_order = 35 
Number of unique synchronized_age_color = 35 
Number of unique synchronized_age_order = 35 
Number of unique age_bin_color = 18 ['#00CC00', '#00D4E6', '#0F0F99', '#18F2E1', '#2C49AD', '#31CCA0', '#34D916', '#4EAD73', '#5A93C6', '#6B49F9', '#73BF7F', '#7FEC3C', '#82C5D9', '#AE43C6', '#BCD233', '#C30000', '#D38F38', '#F99389']
Number of unique age_bin_order = 18 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]
Number of unique species_genus_color = 1 ['#c941a7']
Number of unique species_genus_order = 1 [1]
Number of unique species_scientific_name_color = 1 ['#ffa300']
Number of unique species_scientific_name_order = 1 [1]
Number of unique donor_sex_color = 2 ['#565353', '#ADC4C3']
Number of unique donor_sex_order = 2 [1, 2]
Number of unique x = 564967 
Number of unique y = 565338 
Number of unique cluster_alias = 714 
Number of unique label = 714 
Number of unique class = 15 ['Astro-Epen', 'CNU-MGE GABA', 'CR Glut', 'CTX-CGE GABA', 'CTX-MGE GABA', 'Glioblast', 'IMN', 'IP', 'IT Glut', 'Immune', 'NEC', 'OPC-Oligo', 'RG', 'Vascular', 'nonIT Glut']
Number of unique subclass = 40 
Number of unique cluster = 148 
Number of unique subcluster = 714 
Number of unique class_color = 15 ['#03045E', '#16f2f2', '#450099', '#594a26', '#61e2a4', '#637939', '#6BAED6', '#825f45', '#858881', '#919900', '#CCFF33', '#CEDB9C', '#FA0087', '#FDAE6B', '#f954ee']
Number of unique subclass_color = 40 
Number of unique cluster_color = 49 
Number of unique subcluster_color = 40 
Number of unique class_order = 15 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
Number of unique cluster_order = 148 
Number of unique subclass_order = 40 
Number of unique subcluster_order = 714 

Plotting the taxonomy

Now that we have our cells with associated taxonomy information, we’ll plot them into the UMAP we showed previously.

Below we plot the taxonomy mapping of the cells for each level in the taxonomy.

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['class_color'],
    labels=cell_extended['class'],
    term_orders=cell_extended['class_order'],
    fig_width=12,
    fig_height=12
)
res = ax.set_title("class")
plt.show()

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['subclass_color'],
    labels=cell_extended['subclass'],
    term_orders=cell_extended['subclass_order'],
    fig_width=12,
    fig_height=12
)
res = ax.set_title("subclass")
plt.show()

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['cluster_color'],
    fig_width=12,
    fig_height=12
)
res = ax.set_title("cluster")
plt.show()

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['subcluster_color'],
    fig_width=18,
    fig_height=18
)
res = ax.set_title("subcluster")
plt.show()

Aggregating cluster and cells counts per term per species.

Let’s investigate the taxonomy information a bit more. In this section, we’ll create bar plots showing the number of subclusters and cells at each level in the taxonomy.

First, we need to compute the number of subclusters that are in each of the cell type taxons above it. This is accomplished by a simple groupby in Pandas.

term_cluster_count = membership_with_cluster_info.reset_index().groupby(
        ['cluster_annotation_term_label']
    )[['cluster_alias']].count()
term_cluster_count.columns = ['number_of_subclusters']
term_cluster_count.head()

	number_of_subclusters
cluster_annotation_term_label
CS20260131_CLAS_001	26
CS20260131_CLAS_002	20
CS20260131_CLAS_003	30
CS20260131_CLAS_004	29
CS20260131_CLAS_005	56

term_cell_count = membership_with_cluster_info.reset_index().groupby(
    ['cluster_annotation_term_label']
)[['number_of_cells']].sum()
term_cell_count.head()

	number_of_cells
cluster_annotation_term_label
CS20260131_CLAS_001	4605
CS20260131_CLAS_002	1611
CS20260131_CLAS_003	5656
CS20260131_CLAS_004	4957
CS20260131_CLAS_005	31718

# Join counts with the term dataframe
term_with_counts = cluster_annotation_term.join(term_cluster_count)
term_with_counts = term_with_counts.join(term_cell_count)
term_with_counts.head()

	name	cluster_annotation_term_set_label	cluster_annotation_term_set_name	color_hex_triplet	term_order	term_set_order	parent_term_label	parent_term_name	parent_term_set_label	CCN20230722_label	number_of_subclusters	number_of_cells
cluster_annotation_term_label
CS20260131_CLAS_009	Astro-Epen	CCN20260131_LEVEL_0	class	#594a26	9	0	NaN	NaN	NaN	CS20230722_CLAS_30	25	32116
CS20260131_CLAS_013	CNU-MGE GABA	CCN20260131_LEVEL_0	class	#450099	13	0	NaN	NaN	NaN	CS20230722_CLAS_08	3	208
CS20260131_CLAS_002	CR Glut	CCN20260131_LEVEL_0	class	#919900	2	0	NaN	NaN	NaN	NaN	20	1611
CS20260131_CLAS_011	CTX-CGE GABA	CCN20260131_LEVEL_0	class	#CCFF33	11	0	NaN	NaN	NaN	CS20230722_CLAS_06	52	17567
CS20260131_CLAS_012	CTX-MGE GABA	CCN20260131_LEVEL_0	class	#f954ee	12	0	NaN	NaN	NaN	CS20230722_CLAS_07	91	38296

Below we create a function to plot the subcluster and cell counts in a bar graph, coloring by the associated taxon level.

def bar_plot_by_level_and_type(df: pd.DataFrame, level: str, fig_width: float = 8.5, fig_height: float = 4):
    """Plot the number of cells by the specified level.

    Parameters
    ----------
    df : pd.DataFrame
        DataFrame containing cluster annotation terms with counts.
    level : str
        The level of the taxonomy to plot (e.g., 'Neighborhood', 'Class', 'Subclass', 'Group', 'Cluster').
    fig_width : float, optional
        Width of the figure in inches. Default is 8.5.
    fig_height : float, optional
        Height of the figure in inches. Default is 4.
    """

    fig, ax = plt.subplots(1, 2)
    fig.set_size_inches(fig_width, fig_height)

    for idx, ctype in enumerate(['subclusters', 'cells']):

        pred = (df['cluster_annotation_term_set_name'] == level)
        sort_order = np.argsort(df[pred]['term_order'])
        names = df[pred]['name'].iloc[sort_order]
        counts = df[pred]['number_of_%s' % ctype].iloc[sort_order]
        colors = df[pred]['color_hex_triplet'].iloc[sort_order]
        
        ax[idx].barh(names, counts, color=colors)
        ax[idx].set_title('Number of %s by %s' % (ctype,level))
        ax[idx].set_xlabel('Number of %s' % ctype)
        if ctype == 'cells':
            ax[idx].set_xscale('log')
        
        if idx > 0:
            ax[idx].set_yticklabels([])

    return fig, ax

Now let’s plot the counts the taxonomy levels class and subclass.

fig, ax = bar_plot_by_level_and_type(term_with_counts, 'class')
plt.show()

fig, ax = bar_plot_by_level_and_type(
    term_with_counts,
    'subclass',
    fig_height=8
)
plt.show()

Cluster and subcluster count vs donor age.

Below we compute the number of subclusters and clusters for each of the unique values of age in the dataset. We again compute this by Pandas groupbys.

age_per_subcluster = cell_extended.reset_index().groupby(
        ['donor_age_order']
    )[['cluster_alias']].nunique()
age_per_subcluster.columns = ['number_of_subclusters']
age_per_subcluster.head()

	number_of_subclusters
donor_age_order
1	65
2	68
3	150
4	132
6	144

age_per_cluster = cell_extended.reset_index().groupby(
        ['donor_age_order']
    )[['cluster']].nunique()
age_per_cluster.columns = ['number_of_clusters']
age_per_cluster.head()

	number_of_clusters
donor_age_order
1	12
2	13
3	28
4	26
6	26

We finally plot the number of (sub)clusters for each unique age, showing that both are increasing as a function of age.

age_values = value_sets[
    value_sets['field'] == 'synchronized_age'
]

figure, ax = plt.subplots(1, 1)
fig.set_size_inches(10, 8)
plt.plot(
    range(35), age_per_subcluster['number_of_subclusters'], linestyle='-', marker='o', label='number of subclusters'
)
plt.plot(
    range(35), age_per_cluster['number_of_clusters'], linestyle='-', marker='o', label='number of clusters'
)
_ = ax.set_xticks(range(35))
_ = ax.set_xticklabels(age_values.reset_index().set_index('order').loc[age_per_cluster.index, 'label'], rotation=90)
ax.set_xlabel('synchronized_age')
ax.set_ylabel('count')
plt.title("Number of clusters and subclusters by synchronized_age")
plt.legend(loc=0)

<matplotlib.legend.Legend at 0x169890590>

Visualizing the developmental visual cortex taxonomy

Term sets: class, subclass, cluster, and subcluster define the Developing Mouse, Visual Cortex taxonomy. We can visualize the taxonomy as a sunburst diagram that shows the single inheritance hierarchy through a series of rings, that are sliced for each annotation term. Each ring corresponds to a level in the hierarchy. We have ordered the rings so that the class level. Rings are divided based on their hierarchical relationship to the parent slice.

levels = ['class', 'subclass', 'cluster', 'subcluster']
df = {}

# Copy the term order of the parent into each of the level below it.
if term_with_counts.index.name != 'cluster_annotation_term_label':
    term_with_counts = term_with_counts.set_index('cluster_annotation_term_label')
term_with_counts['parent_order'] = ""
for idx, row in term_with_counts.iterrows():
    if pd.isna(row['parent_term_label']):
        continue
    term_with_counts.loc[idx, 'parent_order'] = term_with_counts.loc[row['parent_term_label']]['term_order']

term_with_counts = term_with_counts.reset_index()
for lvl in levels:
    pred = term_with_counts['cluster_annotation_term_set_name'] == lvl
    df[lvl] = term_with_counts[pred]
    df[lvl] = df[lvl].sort_values(['parent_order', 'term_order'])

fig, ax = plt.subplots()
fig.set_size_inches(10, 10)
size = 0.15

for i, lvl in enumerate(levels):
    
    if lvl == 'class':
        ax.pie(df[lvl]['number_of_subclusters'],
               colors=df[lvl]['color_hex_triplet'],
               labels = df[lvl]['name'],
               rotatelabels=True,
               labeldistance=1.025,
               radius=1,
               wedgeprops=dict(width=size, edgecolor=None),
               startangle=0)
    else :
        ax.pie(df[lvl]['number_of_subclusters'],
               colors=df[lvl]['color_hex_triplet'],
               radius=1-i*size,
               wedgeprops=dict(width=size, edgecolor=None),
               startangle=0)
term_with_counts = term_with_counts.set_index('cluster_annotation_term_label')
plt.show()

In the next tutorial, we show how to access and use Deveolping Mouse gene expression data.