Mouse Aging clustering analysis and annotation

The Mouse Aging dataset [Jin et al] is a comprehensive single-cell RNA sequencing (scRNA-seq) dataset containing ~1.2 million high-quality single-cell transcriptomes of brain cells from young adult and aged mice of both sexes, from regions spanning the forebrain, midbrain, and hindbrain. High-resolution de novo clustering of all cells results in 847 cell clusters and reveals at least 14 age-biased clusters that are mostly glial types. Clusters in the study were mapped to the whole mouse brain taxonomy (WMB-taxonomy, Yao et al.) to provide class, subclass and supertype annotations. At the broader cell subclass and supertype levels, age-associated gene expression signatures were analyzed resulting in a list of 2,449 differentially expressed genes (age-DE genes) for many neuronal and non-neuronal cell types.

The purpose of this set of notebooks is to provide an overview of the data, the file organization, and how to combine data and metadata through example use cases.

You need to be connected to the internet to run this notebook or connected to a cache that has the Aging Mouse data downloaded already. Make sure to have run the getting_started notebook as well if you haven’t already done so and familiarized yourself with the cache object.

The notebook presented here shows quick visualizations from precomputed metadata in the atlas. For examples on accessing the expression matrices, specifically selecting genes from expression matrices, see the general_acessing_10x_snRNASeq_tutorial.ipynb tutorial/example.

import pandas as pd
from pathlib import Path
import numpy as np
import anndata
import matplotlib.pyplot as plt

from abc_atlas_access.abc_atlas_cache.abc_project_cache import AbcProjectCache

We will interact with the data using the AbcProjectCache. This cache object tracks which data has been downloaded 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 have downloaded the data in your system.

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

abc_cache.current_manifest

'releases/20241130/manifest.json'

Data overview

Cell metadata

Essential cell metadata is stored as a 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. The barcoded cell sample label or name is unique in the database.

Each cell is associated with a library label, library method, donor label, donor sex, dissection region_of_interest_label, the corresponding coarse anatomical division label and the matrix_prefix identifying which data package this cell is part of.

Further, each cell is associated with a cluster alias representing which cluster this cell is a member of and (x, y) coordinates of the cells in the UMAP.

Below, we load the first of the metadata used in this tutorial. This pattern of loading metadata is repeated throughout the tutorials.

abc_cache.list_metadata_files('Zeng-Aging-Mouse-10Xv3')

['cell_annotation_colors',
 'cell_cluster_annotations',
 'cell_metadata',
 'cluster',
 'donor',
 'example_genes_all_cells_expression',
 'library',
 'value_sets']

abc_cache.list_metadata_files('Zeng-Aging-Mouse-WMB-taxonomy')

['aging_degenes',
 'cell_cluster_mapping_annotations',
 'cell_cross_mapping_annotations',
 'cluster_mapping',
 'cluster_mapping_pivot']

cell = abc_cache.get_metadata_dataframe(
    directory='Zeng-Aging-Mouse-10Xv3',
    file_name='cell_metadata',
    dtype={'cell_label': str,
           'wmb_cluster_alias': 'Int64'}
)
cell.set_index('cell_label', inplace=True)
print("Number of cells = ", len(cell))
cell.head()

Number of cells =  1162565

	cell_barcode	gene_count	umi_count	doublet_score	x	y	cluster_alias	cell_in_wmb_study	wmb_cluster_alias	library_label	...	donor_label	population_sampling	donor_genotype	donor_sex	donor_age	donor_age_category	donor_in_wmb_study	feature_matrix_label	dataset_label	abc_sample_id
cell_label
AAACCCAAGGACACTG-367_B04	AAACCCAAGGACACTG	6176	26113.0	0.09	-2.288070	3.757430	273	True	1067	L8TX_200924_01_G05	...	Snap25-IRES2-Cre;Ai14-544400	unbiased	Snap25-IRES2-Cre/wt;Ai14(RCL-tdT)/wt	F	P58	adult	True	Zeng-Aging-Mouse-10Xv3	Zeng-Aging-Mouse-10Xv3	5ec753a4-d8b7-4ddf-a169-cc678ae9a073
AAACCCAAGTAAACGT-367_B04	AAACCCAAGTAAACGT	3717	10926.0	0.05	1.104098	11.232103	787	True	14939	L8TX_200924_01_G05	...	Snap25-IRES2-Cre;Ai14-544400	unbiased	Snap25-IRES2-Cre/wt;Ai14(RCL-tdT)/wt	F	P58	adult	True	Zeng-Aging-Mouse-10Xv3	Zeng-Aging-Mouse-10Xv3	7eca8d84-e6ad-4e09-98df-06eefd4b5bc3
AAACCCACATTGAGCT-367_B04	AAACCCACATTGAGCT	1358	2054.0	0.02	-2.465144	17.637245	833	True	5255	L8TX_200924_01_G05	...	Snap25-IRES2-Cre;Ai14-544400	unbiased	Snap25-IRES2-Cre/wt;Ai14(RCL-tdT)/wt	F	P58	adult	True	Zeng-Aging-Mouse-10Xv3	Zeng-Aging-Mouse-10Xv3	c69b1ea1-ce82-4396-a9c4-9e16eef19e9a
AAACGAAAGCCGTAAG-367_B04	AAACGAAAGCCGTAAG	3103	6899.0	0.03	1.328812	10.103676	787	True	14947	L8TX_200924_01_G05	...	Snap25-IRES2-Cre;Ai14-544400	unbiased	Snap25-IRES2-Cre/wt;Ai14(RCL-tdT)/wt	F	P58	adult	True	Zeng-Aging-Mouse-10Xv3	Zeng-Aging-Mouse-10Xv3	6a4b7938-43aa-4ccd-a1e2-3b9ed9fd3aac
AAACGAAAGCTACAAA-367_B04	AAACGAAAGCTACAAA	4459	16420.0	0.09	16.493496	2.265491	815	True	5230	L8TX_200924_01_G05	...	Snap25-IRES2-Cre;Ai14-544400	unbiased	Snap25-IRES2-Cre/wt;Ai14(RCL-tdT)/wt	F	P58	adult	True	Zeng-Aging-Mouse-10Xv3	Zeng-Aging-Mouse-10Xv3	55093777-2fe9-4f8a-b0e5-68a430f142d0

5 rows × 27 columns

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 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 = 997258 
Number of unique gene_count = 9838 
Number of unique umi_count = 81632 
Number of unique doublet_score = 874 
Number of unique x = 1146862 
Number of unique y = 1146950 
Number of unique cluster_alias = 847 
Number of unique cell_in_wmb_study = 2 [False, True]
Number of unique wmb_cluster_alias = 2656 
Number of unique library_label = 287 
Number of unique alignment_job_id = 287 
Number of unique library_method = 2 ['10Xv3', '10xV3.1']
Number of unique barcoded_cell_sample_label = 287 
Number of unique enrichment_population = 6 ['Calcein-positive, Hoechst-positive', 'Hoechst-positive', 'No FACS', 'RFP-positive, Calcein-positive, Hoechst-positive', 'RFP-positive, DAPI-negative', 'RFP-positive, Hoechst-positive']
Number of unique region_of_interest_label = 16 ['HPF - ENT', 'HPF - HIP', 'HPF - PAR-POST-PRE-SUB-ProS', 'HY - HY', 'Iscortex  - ACA', 'Isocortex  - AI', 'Isocortex - PL-ILA-ORB', 'Isocortex - RSP', 'MB - PAG-RAmb', 'MB - VTA-SN', 'P - Pmot/sat-ant', 'P - Pmot/sat-post', 'PAL - PAL', 'STR - STRd', 'STR - STRv', 'STR - sAMY']
Number of unique anatomical_division_label = 7 ['HPF', 'HY', 'Isocortex', 'MB', 'P', 'PAL', 'STR']
Number of unique library_in_wmb_study = 2 [False, True]
Number of unique donor_label = 108 
Number of unique population_sampling = 2 ['Snap25+ neurons', 'unbiased']
Number of unique donor_genotype = 3 ['Ai14(RCL-tdT)/wt', 'Snap25-IRES2-Cre/wt;Ai14(RCL-tdT)/wt', 'wt/wt']
Number of unique donor_sex = 2 ['F', 'M']
Number of unique donor_age = 30 
Number of unique donor_age_category = 2 ['adult', 'aged']
Number of unique donor_in_wmb_study = 2 [False, True]
Number of unique feature_matrix_label = 1 ['Zeng-Aging-Mouse-10Xv3']
Number of unique dataset_label = 1 ['Zeng-Aging-Mouse-10Xv3']
Number of unique abc_sample_id = 1162565 

Cell metadata colors

Below we load a DataFrame containing name and color mappings pre-cell for donor sex, region of interest, and anatomical division. This DataFrame is indexed by cell the same as the above cell_metadata. This allows us to label the UMAP with the previously mentioned distinct labels.

cell_colors = abc_cache.get_metadata_dataframe(
    directory='Zeng-Aging-Mouse-10Xv3',
    file_name='cell_annotation_colors'
).set_index('cell_label')
cell_colors.head()

	anatomical_division_color	anatomical_division_order	donor_age_category_color	donor_age_category_order	donor_sex_color	donor_sex_order	region_of_interest_color	region_of_interest_order
cell_label
AAACCCAAGGACACTG-367_B04	#98D6F9	2	#9ACA3C	0	#FAAA5B	0	#98D6F9	7
AAACCCAAGTAAACGT-367_B04	#98D6F9	2	#9ACA3C	0	#FAAA5B	0	#98D6F9	7
AAACCCACATTGAGCT-367_B04	#98D6F9	2	#9ACA3C	0	#FAAA5B	0	#98D6F9	7
AAACGAAAGCCGTAAG-367_B04	#98D6F9	2	#9ACA3C	0	#FAAA5B	0	#98D6F9	7
AAACGAAAGCTACAAA-367_B04	#98D6F9	2	#9ACA3C	0	#FAAA5B	0	#98D6F9	7

We will merge these two DataFrames to create one that contains both cell metadata and color information, ready to plot on a UMAP. We also shuffle the cell metadata to get a more representative plot in the UMAP. This is done as the default DataFrame is sorted cell_id which correlates with some of the metadata (e.g. age).

cell_extended = cell.join(cell_colors, rsuffix='_cl_colors')
cell_extended = cell_extended.sample(frac=1, random_state=12345)
cell_extended.head()

	cell_barcode	gene_count	umi_count	doublet_score	x	y	cluster_alias	cell_in_wmb_study	wmb_cluster_alias	library_label	...	dataset_label	abc_sample_id	anatomical_division_color	anatomical_division_order	donor_age_category_color	donor_age_category_order	donor_sex_color	donor_sex_order	region_of_interest_color	region_of_interest_order
cell_label
GCCTGTTGTGAATTAG-135_B01	GCCTGTTGTGAATTAG	6777	37834.0	0.200000	-1.185296	1.881103	278	True	1079	L8TX_190716_01_D07	...	Zeng-Aging-Mouse-10Xv3	b801b060-42b7-465c-a768-1a9b8dd9de7f	#98D6F9	2	#9ACA3C	0	#FAAA5B	0	#80CDF8	8
TCCGAAAGTGAAGCGT-761_A04	TCCGAAAGTGAAGCGT	3396	9585.0	0.030303	14.007499	-0.220336	817	False	<NA>	L8TX_210805_01_H01	...	Zeng-Aging-Mouse-10Xv3	a59a8bc8-3c52-4d77-bebf-4ebf31e8d079	#7ED04B	1	#CC6828	1	#FAAA5B	0	#7ED04B	4
TTACCATGTCGTGGTC-327_A06	TTACCATGTCGTGGTC	4294	10527.0	0.020000	6.550781	-1.378886	804	False	<NA>	L8TX_200813_01_H10	...	Zeng-Aging-Mouse-10Xv3	2e5da533-b920-4707-aba1-d27f04ce5018	#8599CC	3	#CC6828	1	#735AA6	1	#8599CC	10
ACGGGTCGTACGAGCA-385_D06	ACGGGTCGTACGAGCA	2943	6957.0	0.000000	13.650183	0.289086	817	False	<NA>	L8TX_201008_01_A12	...	Zeng-Aging-Mouse-10Xv3	8b63cdce-4d15-4e88-b154-c29fd0e23ff1	#E64438	4	#CC6828	1	#735AA6	1	#E64438	11
CTTAGGATCTGTCCCA-301_B04	CTTAGGATCTGTCCCA	7907	47406.0	0.037037	6.583878	-10.639808	152	False	<NA>	L8TX_200723_01_B10	...	Zeng-Aging-Mouse-10Xv3	8c290798-5c6e-42d9-a906-bfb69ec4399b	#7ED04B	1	#CC6828	1	#FAAA5B	0	#72D569	6

5 rows × 35 columns

UMAP spatial embedding: Donor and anatomic information

Now that we’ve merged the color information into the main cells data, we can plot the Uniform Manifold Approximation and Projection (UMAP) for all the cells in the dataset. The UMAP is a dimension reduction technique that can be used for visualizing and exploring large-dimension datasets. The x and y columns of the cell metadata table represent the coordinate of all cells in the UMAP in Figure 1 of the manuscript.

We define a small helper function plot umap to visualize the cells on the UMAP. In this example will plot associated cell information colorized by dissection region of interest, neurotransmitter identity, sex, taxonomy level, age category, and age depletion/enrichment.

def plot_umap(xx, yy, cc=None, val=None, fig_width=8, fig_height=8, cmap=None, labels=None):

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

    if labels is not None:
        from matplotlib.patches import Rectangle
        unique_colors = cc.unique()
        rects = []
        for color in unique_colors:
            rects.append(Rectangle((0, 0), 1, 1, fc=color))

        legend = ax.legend(rects, labels, loc=0)
        # ax.add_artist(legend)

    ax.axis('equal')
    ax.set_xticks([])
    ax.set_yticks([])
    
    return fig, ax

Below we plot the UMAPS for each of anatomical division, dissection, sex, and age.

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['anatomical_division_color'],
    labels=cell_extended['anatomical_division_label'].unique()
)
res = ax.set_title("Anatomical Division")
plt.show()

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['region_of_interest_color']
)
res = ax.set_title("Dissection Region Of Interest")
plt.show()

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['donor_sex_color'],
    labels=cell_extended['donor_sex'].unique()
)
res = ax.set_title("Donor Sex")
plt.show()

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['donor_age_category_color'],
    labels=cell_extended['donor_age_category'].unique()
)
res = ax.set_title("Donor Age")
plt.show()

Cluster Metadata

We load information on the individual clusters including the age ratio, odds, and neurotransmitter information. This metadata is indexed on cluster_alias which is also a column in the cell_metadata that allows us to merge the information into that DataFrame.

cluster_info = abc_cache.get_metadata_dataframe(
    directory='Zeng-Aging-Mouse-10Xv3',
    file_name='cluster'
).set_index('cluster_alias')
cluster_info.head()

	number_of_cells	cluster_label	cluster_order	cluster_name	cluster_color	max_region_of_interest_label	proportion_max_region_of_interest_label	number_of_adult_cells	number_of_aged_cells	proportion_adult_cells	proportion_aged_cells	odds_ratio	log2_odds_ratio	cluster_age_bias	max_region_of_interest_color	cluster_age_bias_color	neurotransmitter_combined_label	neurotransmitter_label	neurotransmitter_color
cluster_alias
1	851	CS20241021_0001	0	1_CLA-EPd-CTX Car3 Glut_1	#f7c27a	Isocortex - AI	0.831962	183	668	0.215041	0.784959	1.453797	0.539826	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
2	330	CS20241021_0002	1	2_IT EP-CLA Glut_1	#5c1a34	Isocortex - AI	0.969697	160	170	0.484848	0.515152	0.421595	-1.246071	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
3	589	CS20241021_0003	2	3_IT EP-CLA Glut_1	#b96cf0	Isocortex - AI	0.894737	240	349	0.407470	0.592530	0.576896	-0.793616	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
4	207	CS20241021_0004	3	4_IT EP-CLA Glut_1	#bff691	Isocortex - AI	0.971014	107	100	0.516908	0.483092	0.371035	-1.430373	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
5	2141	CS20241021_0005	4	5_IT EP-CLA Glut_1	#39fbfa	Isocortex - AI	0.987389	655	1486	0.305932	0.694068	0.900735	-0.150825	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF

Now that we have our data loaded and indexed, we can merge the tables together to create a single DataFrame allowing us to plot various information onto the UMAP and beyond.

# Join the cluster information in by joining on the Aging dataset's cluster_alias column.
cell_extended = cell_extended.join(cluster_info, on='cluster_alias', rsuffix='_cl_info')
cell_extended.head()

	cell_barcode	gene_count	umi_count	doublet_score	x	y	cluster_alias	cell_in_wmb_study	wmb_cluster_alias	library_label	...	proportion_adult_cells	proportion_aged_cells	odds_ratio	log2_odds_ratio	cluster_age_bias	max_region_of_interest_color	cluster_age_bias_color	neurotransmitter_combined_label	neurotransmitter_label	neurotransmitter_color
cell_label
GCCTGTTGTGAATTAG-135_B01	GCCTGTTGTGAATTAG	6777	37834.0	0.200000	-1.185296	1.881103	278	True	1079	L8TX_190716_01_D07	...	0.577949	0.422051	1.013716	0.019653	unassigned	#80CDF8	#DADEDF	GABA	GABA	#FF3358
TCCGAAAGTGAAGCGT-761_A04	TCCGAAAGTGAAGCGT	3396	9585.0	0.030303	14.007499	-0.220336	817	False	<NA>	L8TX_210805_01_H01	...	0.454494	0.545506	1.046448	0.065500	unassigned	#8599CC	#DADEDF	No-NT	No-NT	#666666
TTACCATGTCGTGGTC-327_A06	TTACCATGTCGTGGTC	4294	10527.0	0.020000	6.550781	-1.378886	804	False	<NA>	L8TX_200813_01_H10	...	0.370570	0.629430	1.741208	0.800089	unassigned	#80C0E2	#DADEDF	No-NT	No-NT	#666666
ACGGGTCGTACGAGCA-385_D06	ACGGGTCGTACGAGCA	2943	6957.0	0.000000	13.650183	0.289086	817	False	<NA>	L8TX_201008_01_A12	...	0.454494	0.545506	1.046448	0.065500	unassigned	#8599CC	#DADEDF	No-NT	No-NT	#666666
CTTAGGATCTGTCCCA-301_B04	CTTAGGATCTGTCCCA	7907	47406.0	0.037037	6.583878	-10.639808	152	False	<NA>	L8TX_200723_01_B10	...	0.403409	0.596591	0.587232	-0.767999	unassigned	#72D569	#DADEDF	Glut	Glut	#2B93DF

5 rows × 54 columns

UMAP: Taxonomy information

Below are UMAPs for labels associated with the taxonomy, namely: neurotransmitter and cluster. Note that the colors for neurotransmitter (and for class, subclass, and supertype) are the same as those from the WMB taxonomy.

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['neurotransmitter_color'],
    labels=cell_extended['neurotransmitter_label'].unique()
)
res = ax.set_title("Neuortransmitter Identity")
plt.show()

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

Aging information

We plot aging related information on our UMAP, showing the different age-related variables and they are expressed in different locations of the UMAP. Below we show the location of the cells from the adult and aged mice.

Next, we show the log of the cluster level odds ratio for a given cluster to be age enriched (brown) or depleted (blue-green).

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    val=cell_extended['log2_odds_ratio'],
    cmap='BrBG_r'
)
res = ax.set_title("log2 Odds Ratio")
plt.show()

Finally, we show the clusters that are identified as aging-enriched and aging-depleted based on their odds ratio. (-2.5 > log2_odds_ratio, log2_odds_ratio > 2.5).

fig, ax = plot_umap(
    cell_extended['x'],
    cell_extended['y'],
    cc=cell_extended['cluster_age_bias_color'],
    labels=cell_extended['cluster_age_bias'].unique()
)
res = ax.set_title("Age Bias")
plt.show()

Taxonomy Mapping

The following metadata maps all cells to the clusters and above levels in the taxonomy hierarchy. This includes all names and colors for each part of the taxonomy. As show in the above cluster tutorial for this dataset, each cluster in the Aging Dataset is nested within a supertype from the WMB dataset allowing one to walk all the way up to the class level.

cell_cluster_mapping = abc_cache.get_metadata_dataframe(
    directory='Zeng-Aging-Mouse-WMB-taxonomy',
    file_name='cell_cluster_mapping_annotations'
).set_index('cell_label')
cell_cluster_mapping.head()

	cluster_alias	cluster_label	cluster_order	cluster_name	cluster_color	class_name	subclass_name	supertype_name	class_color	subclass_color	supertype_color
cell_label
AAACCCAAGGACACTG-367_B04	273	CS20241021_0273	272	273_STR D1 Gaba_8	#669ff8	09 CNU-LGE GABA	061 STR D1 Gaba	0272 STR D1 Gaba_8	#B199FF	#005B66	#660F13
AAACCCAAGTAAACGT-367_B04	787	CS20241021_0787	786	787_Astro-TE NN_3	#daf288	30 Astro-Epen	319 Astro-TE NN	1163 Astro-TE NN_3	#594a26	#3DCCB1	#a8afa5
AAACCCACATTGAGCT-367_B04	833	CS20241021_0833	832	833_Endo NN_1	#096577	33 Vascular	333 Endo NN	1193 Endo NN_1	#858881	#994567	#00992A
AAACGAAAGCCGTAAG-367_B04	787	CS20241021_0787	786	787_Astro-TE NN_3	#daf288	30 Astro-Epen	319 Astro-TE NN	1163 Astro-TE NN_3	#594a26	#3DCCB1	#a8afa5
AAACGAAAGCTACAAA-367_B04	815	CS20241021_0815	814	815_MOL NN_4	#c4dd47	31 OPC-Oligo	327 Oligo NN	1184 MOL NN_4	#03045E	#99FFBC	#99CAFF

cell_extended = cell_extended.join(cell_cluster_mapping, rsuffix='_cl_map')

# Quick run through to drop duplicated columns
drop_cols = []
for col in cell_extended.columns:
    if col.endswith(('_cl_map', '_cl_colors', '_cl_info')):
        drop_cols.append(col)
cell_extended.drop(columns=drop_cols, inplace=True)

Let’s take a look at what columns and data are in our final combined DataFrame before our final UMAP plots.

print_column_info(cell_extended)

Number of unique cell_barcode = 997258 
Number of unique gene_count = 9838 
Number of unique umi_count = 81632 
Number of unique doublet_score = 874 
Number of unique x = 1146862 
Number of unique y = 1146950 
Number of unique cluster_alias = 847 
Number of unique cell_in_wmb_study = 2 [False, True]
Number of unique wmb_cluster_alias = 2656 
Number of unique library_label = 287 
Number of unique alignment_job_id = 287 
Number of unique library_method = 2 ['10Xv3', '10xV3.1']
Number of unique barcoded_cell_sample_label = 287 
Number of unique enrichment_population = 6 ['Calcein-positive, Hoechst-positive', 'Hoechst-positive', 'No FACS', 'RFP-positive, Calcein-positive, Hoechst-positive', 'RFP-positive, DAPI-negative', 'RFP-positive, Hoechst-positive']
Number of unique region_of_interest_label = 16 ['HPF - ENT', 'HPF - HIP', 'HPF - PAR-POST-PRE-SUB-ProS', 'HY - HY', 'Iscortex  - ACA', 'Isocortex  - AI', 'Isocortex - PL-ILA-ORB', 'Isocortex - RSP', 'MB - PAG-RAmb', 'MB - VTA-SN', 'P - Pmot/sat-ant', 'P - Pmot/sat-post', 'PAL - PAL', 'STR - STRd', 'STR - STRv', 'STR - sAMY']
Number of unique anatomical_division_label = 7 ['HPF', 'HY', 'Isocortex', 'MB', 'P', 'PAL', 'STR']
Number of unique library_in_wmb_study = 2 [False, True]
Number of unique donor_label = 108 
Number of unique population_sampling = 2 ['Snap25+ neurons', 'unbiased']
Number of unique donor_genotype = 3 ['Ai14(RCL-tdT)/wt', 'Snap25-IRES2-Cre/wt;Ai14(RCL-tdT)/wt', 'wt/wt']
Number of unique donor_sex = 2 ['F', 'M']
Number of unique donor_age = 30 
Number of unique donor_age_category = 2 ['adult', 'aged']
Number of unique donor_in_wmb_study = 2 [False, True]
Number of unique feature_matrix_label = 1 ['Zeng-Aging-Mouse-10Xv3']
Number of unique dataset_label = 1 ['Zeng-Aging-Mouse-10Xv3']
Number of unique abc_sample_id = 1162565 
Number of unique anatomical_division_color = 7 ['#70FF71', '#7ED04B', '#8599CC', '#98D6F9', '#E64438', '#FF64FF', '#FF9B88']
Number of unique anatomical_division_order = 7 [0, 1, 2, 3, 4, 5, 6]
Number of unique donor_age_category_color = 2 ['#9ACA3C', '#CC6828']
Number of unique donor_age_category_order = 2 [0, 1]
Number of unique donor_sex_color = 2 ['#735AA6', '#FAAA5B']
Number of unique donor_sex_order = 2 [0, 1]
Number of unique region_of_interest_color = 15 ['#1AA698', '#219866', '#2FA850', '#32B825', '#40A666', '#72D569', '#7ED04B', '#80C0E2', '#80CDF8', '#8599CC', '#98D6F9', '#E64438', '#FF90FF', '#FFAE6F', '#FFC395']
Number of unique region_of_interest_order = 16 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
Number of unique number_of_cells = 502 
Number of unique cluster_label = 847 
Number of unique cluster_order = 847 
Number of unique cluster_name = 847 
Number of unique cluster_color = 847 
Number of unique max_region_of_interest_label = 16 ['HPF - ENT', 'HPF - HIP', 'HPF - PAR-POST-PRE-SUB-ProS', 'HY - HY', 'Iscortex  - ACA', 'Isocortex  - AI', 'Isocortex - PL-ILA-ORB', 'Isocortex - RSP', 'MB - PAG-RAmb', 'MB - VTA-SN', 'P - Pmot/sat-ant', 'P - Pmot/sat-post', 'PAL - PAL', 'STR - STRd', 'STR - STRv', 'STR - sAMY']
Number of unique proportion_max_region_of_interest_label = 681 
Number of unique number_of_adult_cells = 395 
Number of unique number_of_aged_cells = 416 
Number of unique proportion_adult_cells = 823 
Number of unique proportion_aged_cells = 823 
Number of unique odds_ratio = 846 
Number of unique log2_odds_ratio = 846 
Number of unique cluster_age_bias = 3 ['aging-depleted', 'aging-enriched', 'unassigned']
Number of unique max_region_of_interest_color = 15 ['#1AA698', '#219866', '#2FA850', '#32B825', '#40A666', '#72D569', '#7ED04B', '#80C0E2', '#80CDF8', '#8599CC', '#98D6F9', '#E64438', '#FF90FF', '#FFAE6F', '#FFC395']
Number of unique cluster_age_bias_color = 3 ['#9BCB3C', '#CD6828', '#DADEDF']
Number of unique neurotransmitter_combined_label = 15 ['GABA', 'GABA-Chol', 'GABA-Dopa', 'GABA-Glyc', 'GABA-Hist', 'GABA-Sero', 'Glut', 'Glut-Chol', 'Glut-Dopa', 'Glut-GABA', 'Glut-GABA-Dopa', 'Glut-GABA-Glyc', 'Glut-GABA-Sero', 'Glut-Sero', 'No-NT']
Number of unique neurotransmitter_label = 9 ['Chol', 'Dopa', 'GABA', 'GABA-Glyc', 'Glut', 'Glut-GABA', 'Hist', 'No-NT', 'Sero']
Number of unique neurotransmitter_color = 9 ['#0a9964', '#2B93DF', '#533691', '#666666', '#73E785', '#820e57', '#FF3358', '#fcf04b', '#ff7621']
Number of unique class_name = 25 ['01 IT-ET Glut', '02 NP-CT-L6b Glut', '03 OB-CR Glut', '04 DG-IMN Glut', '05 OB-IMN GABA', '06 CTX-CGE GABA', '07 CTX-MGE GABA', '08 CNU-MGE GABA', '09 CNU-LGE GABA', '11 CNU-HYa GABA', '12 HY GABA', '13 CNU-HYa Glut', '14 HY Glut', '16 HY MM Glut', '19 MB Glut', '20 MB GABA', '21 MB Dopa', '22 MB-HB Sero', '23 P Glut', '24 MY Glut', '26 P GABA', '30 Astro-Epen', '31 OPC-Oligo', '33 Vascular', '34 Immune']
Number of unique subclass_name = 172 
Number of unique supertype_name = 432 
Number of unique class_color = 25 ['#007200', '#01d669', '#03045E', '#16f2f2', '#1b4332', '#38B000', '#450099', '#594a26', '#61e2a4', '#6b5ca5', '#70E000', '#72195a', '#825f45', '#858881', '#90E0EF', '#9EF01A', '#AA0DFE', '#B199FF', '#CCFF33', '#D00000', '#EC4067', '#F0A0FF', '#FA0087', '#FF6600', '#f954ee']
Number of unique subclass_color = 172 
Number of unique supertype_color = 432 

Now that the information on the WMB taxonomy has been merged into our cell metadata, we plot them here. Note again that the colors here are the same as those in WMB.

fig, ax = plot_umap(cell_extended['x'], cell_extended['y'], cc=cell_extended['class_color'])
res = ax.set_title("Class")
plt.show()

fig, ax = plot_umap(cell_extended['x'], cell_extended['y'], cc=cell_extended['subclass_color'])
res = ax.set_title("Subclass")
plt.show()

fig, ax = plot_umap(cell_extended['x'], cell_extended['y'], cc=cell_extended['supertype_color'])
res = ax.set_title("Supertype")
plt.show()

Aggregating cluster and cells counts per term

Finally in this walkthrough of the data, we’ll explore the taxonomy a bit more.

Now that we have information associated in each cluster and the full hierarchy of the taxonomy loaded, we can merge the cluster_info and cell mapping tables on their index (cluster_alias), producing a dataset describing the taxonomy. We’ll use this to display counts of cells and clusters against various metadata.

We’ll need to load one more metadata containing the full mapping of Aging Mouse cluster to the WMB taxonomy.

cluster_mapping = abc_cache.get_metadata_dataframe(
    directory='Zeng-Aging-Mouse-WMB-taxonomy',
    file_name='cluster_mapping',
    dtype={'cluster_label': str}
).set_index('cluster_alias')
cluster_mapping.head()

	cluster_label	cluster_order	cluster_name	cluster_color	cluster_annotation_term_label	cluster_annotation_term_name	cluster_annotation_term_set_label	parent_term_label	parent_term_set_label	term_set_order	term_order	cluster_annotation_term_set_name	color_hex_triplet
cluster_alias
1	CS20241021_0001	0	1_CLA-EPd-CTX Car3 Glut_1	#f7c27a	CS20230722_SUPT_0001	0001 CLA-EPd-CTX Car3 Glut_1	CCN20230722_SUPT	CS20230722_SUBC_001	CCN20230722_SUBC	3	0	supertype	#99822E
2	CS20241021_0002	1	2_IT EP-CLA Glut_1	#5c1a34	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	supertype	#994563
3	CS20241021_0003	2	3_IT EP-CLA Glut_1	#b96cf0	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	supertype	#994563
4	CS20241021_0004	3	4_IT EP-CLA Glut_1	#bff691	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	supertype	#994563
5	CS20241021_0005	4	5_IT EP-CLA Glut_1	#39fbfa	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	supertype	#994563

Merge in cluster info to get the full set of colors and neurotransmitter identities.

membership = cluster_mapping.join(cluster_info, rsuffix='_cl_info')
membership.head()

	cluster_label	cluster_order	cluster_name	cluster_color	cluster_annotation_term_label	cluster_annotation_term_name	cluster_annotation_term_set_label	parent_term_label	parent_term_set_label	term_set_order	...	proportion_adult_cells	proportion_aged_cells	odds_ratio	log2_odds_ratio	cluster_age_bias	max_region_of_interest_color	cluster_age_bias_color	neurotransmitter_combined_label	neurotransmitter_label	neurotransmitter_color
cluster_alias
1	CS20241021_0001	0	1_CLA-EPd-CTX Car3 Glut_1	#f7c27a	CS20230722_SUPT_0001	0001 CLA-EPd-CTX Car3 Glut_1	CCN20230722_SUPT	CS20230722_SUBC_001	CCN20230722_SUBC	3	...	0.215041	0.784959	1.453797	0.539826	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
2	CS20241021_0002	1	2_IT EP-CLA Glut_1	#5c1a34	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	...	0.484848	0.515152	0.421595	-1.246071	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
3	CS20241021_0003	2	3_IT EP-CLA Glut_1	#b96cf0	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	...	0.407470	0.592530	0.576896	-0.793616	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
4	CS20241021_0004	3	4_IT EP-CLA Glut_1	#bff691	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	...	0.516908	0.483092	0.371035	-1.430373	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
5	CS20241021_0005	4	5_IT EP-CLA Glut_1	#39fbfa	CS20230722_SUPT_0003	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	...	0.305932	0.694068	0.900735	-0.150825	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF

5 rows × 32 columns

One final set of data to create is to calculate the number of clusters that are members of a given supertype, subclass, and class in the taxonomy.

# Count the number of clusters associated with each cluster annotation term
term_cluster_count = membership.groupby(['cluster_annotation_term_label'])[['cluster_label']].count()
term_cluster_count.columns = ['number_of_clusters']
term_cluster_count.head()

	number_of_clusters
cluster_annotation_term_label
CS20230722_CLAS_01	156
CS20230722_CLAS_02	38
CS20230722_CLAS_03	1
CS20230722_CLAS_04	6
CS20230722_CLAS_05	4

term_by_label = membership.set_index('cluster_annotation_term_label')
term_with_counts = term_by_label.join(term_cluster_count)
term_with_counts.head()

	cluster_label	cluster_order	cluster_name	cluster_color	cluster_annotation_term_name	cluster_annotation_term_set_label	parent_term_label	parent_term_set_label	term_set_order	term_order	...	proportion_aged_cells	odds_ratio	log2_odds_ratio	cluster_age_bias	max_region_of_interest_color	cluster_age_bias_color	neurotransmitter_combined_label	neurotransmitter_label	neurotransmitter_color	number_of_clusters
cluster_annotation_term_label
CS20230722_SUPT_0001	CS20241021_0001	0	1_CLA-EPd-CTX Car3 Glut_1	#f7c27a	0001 CLA-EPd-CTX Car3 Glut_1	CCN20230722_SUPT	CS20230722_SUBC_001	CCN20230722_SUBC	3	0	...	0.784959	1.453797	0.539826	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF	1
CS20230722_SUPT_0003	CS20241021_0002	1	2_IT EP-CLA Glut_1	#5c1a34	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	...	0.515152	0.421595	-1.246071	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF	5
CS20230722_SUPT_0003	CS20241021_0003	2	3_IT EP-CLA Glut_1	#b96cf0	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	...	0.592530	0.576896	-0.793616	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF	5
CS20230722_SUPT_0003	CS20241021_0004	3	4_IT EP-CLA Glut_1	#bff691	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	...	0.483092	0.371035	-1.430373	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF	5
CS20230722_SUPT_0003	CS20241021_0005	4	5_IT EP-CLA Glut_1	#39fbfa	0003 IT EP-CLA Glut_1	CCN20230722_SUPT	CS20230722_SUBC_002	CCN20230722_SUBC	3	2	...	0.694068	0.900735	-0.150825	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF	5

5 rows × 32 columns

Let’s visualize cluster and cells counts for of the classification levels using bar plots.

def bar_plot_by_level_and_type(df, level=None, fig_width = 8.5, fig_height = 4):
    
    fig, ax = plt.subplots(1, 2)
    fig.set_size_inches(fig_width, fig_height)
    
    for idx, ctype in enumerate(['clusters', 'cells']):

        if level.lower().startswith('neurotransmitter'):
            level = 'Neurotransmitter'
            names = df['neurotransmitter_label']
            pred = np.ones(len(df), dtype=bool)
            colors = df['neurotransmitter_color']
            counts = df['number_of_%s' % ctype]
        else:
            pred = (df['cluster_annotation_term_set_name'] == level)
            names = df[pred]['cluster_annotation_term_name']
            colors = df[pred]['color_hex_triplet']
            counts = df[pred]['number_of_%s' % ctype]
        
        ax[idx].barh(names, counts, color=colors)
        ax[idx].set_title('Number of %s by %s' % (ctype, level)),
        ax[idx].set_xscale('log')

        if idx > 0 :
            ax[idx].set_yticklabels([])

    plt.show()

Neurotransmitter cluster and cell counts

As seen below, most of the clusters in the study are glutamatergic, while the majority of cells are Nonneuronal.

bar_plot_by_level_and_type(term_with_counts, 'neurotransmitter_label')

Class level cluster and cell counts

Class “01 IT-ET” contains the largest number of clusters (~100), while class “30 Astro-Epen” contains the largest number of cells.

bar_plot_by_level_and_type(term_with_counts, 'class', 8.5, 6)

Visualizing cluster and term distributions

We can explore the relationship and distribution of clusters between term sets by creating a pivot table using pandas groupby function. Each row of the resulting DataFrame represents a cluster, each column represents a term set and the value in the table is the name of the term that has been associated with the cluster for that specific term set.

pivot = membership.groupby(['cluster_alias', 'cluster_annotation_term_set_name'])['cluster_annotation_term_name'].first().unstack()
pivot = pivot.join(cluster_info)
pivot.head()

	class	subclass	supertype	number_of_cells	cluster_label	cluster_order	cluster_name	cluster_color	max_region_of_interest_label	proportion_max_region_of_interest_label	...	proportion_adult_cells	proportion_aged_cells	odds_ratio	log2_odds_ratio	cluster_age_bias	max_region_of_interest_color	cluster_age_bias_color	neurotransmitter_combined_label	neurotransmitter_label	neurotransmitter_color
cluster_alias
1	01 IT-ET Glut	001 CLA-EPd-CTX Car3 Glut	0001 CLA-EPd-CTX Car3 Glut_1	851	CS20241021_0001	0	1_CLA-EPd-CTX Car3 Glut_1	#f7c27a	Isocortex - AI	0.831962	...	0.215041	0.784959	1.453797	0.539826	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
2	01 IT-ET Glut	002 IT EP-CLA Glut	0003 IT EP-CLA Glut_1	330	CS20241021_0002	1	2_IT EP-CLA Glut_1	#5c1a34	Isocortex - AI	0.969697	...	0.484848	0.515152	0.421595	-1.246071	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
3	01 IT-ET Glut	002 IT EP-CLA Glut	0003 IT EP-CLA Glut_1	589	CS20241021_0003	2	3_IT EP-CLA Glut_1	#b96cf0	Isocortex - AI	0.894737	...	0.407470	0.592530	0.576896	-0.793616	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
4	01 IT-ET Glut	002 IT EP-CLA Glut	0003 IT EP-CLA Glut_1	207	CS20241021_0004	3	4_IT EP-CLA Glut_1	#bff691	Isocortex - AI	0.971014	...	0.516908	0.483092	0.371035	-1.430373	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF
5	01 IT-ET Glut	002 IT EP-CLA Glut	0003 IT EP-CLA Glut_1	2141	CS20241021_0005	4	5_IT EP-CLA Glut_1	#39fbfa	Isocortex - AI	0.987389	...	0.305932	0.694068	0.900735	-0.150825	unassigned	#219866	#DADEDF	Glut	Glut	#2B93DF

5 rows × 22 columns

We can also obtain a cluster annotation color pivot table in the same way.

color = membership.groupby(['cluster_alias', 'cluster_annotation_term_set_name'])['color_hex_triplet'].first().unstack().fillna('#f9f9f9')
color.columns = ['%s_color' % x for x in color.columns]
color.head()

	class_color	subclass_color	supertype_color
cluster_alias
1	#FA0087	#64c2fc	#99822E
2	#FA0087	#1F665D	#994563
3	#FA0087	#1F665D	#994563
4	#FA0087	#1F665D	#994563
5	#FA0087	#1F665D	#994563

pivot = pivot.join(color)

Plotting distributions of clusters and cells by taxonomy level

For a given pair of term sets A and B, we compute a cluster count table where the rows are terms in term set A, columns are terms in term set B with the table values being the number of clusters that are shared between the terms.

def distribution(A, B, B_color) :
    
    AxB = pivot.groupby([A, B])[['cluster_name']].count()
    AxB.columns = ['number_of_clusters']
    AxB = AxB.unstack().fillna(0)
    B_names = pivot[B].unique()
    B_colors = pd.Series(data=pivot[B_color].unique(), index=B_names)
    
    return AxB, B_names, B_colors

Function stacked_bar_distribution takes the results of distribution as input to create distribution stacked bar plot.

def stacked_bar_distribution(AxB, B_names, B_colors, fig_width = 6, fig_height = 6):
    """
    """

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

    bottom = np.zeros(len(AxB))

    for i, col in enumerate(AxB.columns):
        ax.barh(AxB.index, AxB[col], left=bottom, label=col[1], color=B_colors.loc[col[1]])
        bottom += np.array(AxB[col])

    ax.set_title('Distribution of %s in each %s' % (AxB.columns.names[1], AxB.index.name))
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))

    plt.show()
    
    return fig, ax

Distribution of neurotransmitter clusters in each class

AxB, B_names, B_colors = distribution('class', 'neurotransmitter_label', 'neurotransmitter_color')
fig, ax = stacked_bar_distribution(AxB, B_names, B_colors, 6, 6)

Distribution of age enriched vs age depleted clusters

AxB, B_names, B_colors = distribution('class', 'cluster_age_bias', 'cluster_age_bias_color')
fig, ax = stacked_bar_distribution(AxB, B_names, B_colors, 6, 6)

Distribution of adult and aged cells

We can use the same groupby functionality as our distribution functions to this time sum up all the adult and age cells in each cluster and plot those versus class (or subclass, supertype). Note that there are ~600k aged cells vs ~400k adult cells in the data so an overall bias towards the aged cells is expected.

# Sum up the number of aged and adult cells.
n_adult = pivot.groupby(['class'])[['number_of_adult_cells']].sum()
n_aged = pivot.groupby(['class'])[['number_of_aged_cells']].sum()
adult_vs_aged = n_adult.join(n_aged)

# Rename columns and correctly label the aged/adult cells by color.
adult_vs_aged.columns = pd.MultiIndex.from_tuples(
    [('n_cells', 'adult'), ('n_cells', 'aged')],
    names=[None, 'n_cells'])
B_names = ['adult', 'aged']
B_colors = pd.Series(data=['#9ACA3C', '#CC6828'], index=B_names)

# Plot our barplot
fig, ax = stacked_bar_distribution(adult_vs_aged, B_names, B_colors, 6, 6)

Visualizing the mouse whole brain taxonomy

Term sets: class, subclass, supertype and cluster form a four-level mouse whole brain 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 is the outer most ring so that we can add in each class’s labels. Rings are sliced up and divided based on their hierarchical relationship to the parent slice. The angle of each slice is proportional to the number of clusters belonging to the term.

levels = ['class', 'subclass', 'supertype']
df = {}

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_term_label'])
    df[lvl] = df[lvl][~df[lvl].index.duplicated(keep='first')]

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_clusters'],
               colors=df[lvl]['color_hex_triplet'],
               labels=df[lvl]['cluster_annotation_term_name'],
               rotatelabels=True,
               labeldistance=1.025,
               radius=1,
               wedgeprops=dict(width=size, edgecolor=None),
               startangle=0)
    else:
        ax.pie(df[lvl]['number_of_clusters'],
               colors=df[lvl]['color_hex_triplet'],
               radius=1-i*size,
               wedgeprops=dict(width=size, edgecolor=None),
               startangle=0)

plt.show()

In part 2 we’ll focus on gene data including using the UMAP to plot gene expression locations as well as age Differential (ageDE) genes identified in Zeng et al.