1_ctx_th_connection_and_gene_exp_cluster

[2]:

import numpy as np
import umap
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_digits
from sklearn.cluster import KMeans
from matplotlib import cm
import scanpy as sc
import seaborn as sns
import warnings
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
warnings.filterwarnings('ignore')

[3]:

# load adata and cell type

adata = sc.read_h5ad('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/adata_processed.h5ad')
allen_region = pd.read_csv('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/allen_region.csv')
adata.obs['region'] = allen_region['region'].values
# add cell type
meta = pd.read_csv('/mnt/Data16Tc/home/haichao/code/SpaCon/Data/N_20231213_zxw/mouse_3/cell_metadata_with_cluster_annotation.csv')
meta = meta.set_index('cell_label')
meta = meta.loc[adata.obs.index.to_list()]
adata.obs['cell_type'] = meta['class'].to_list()
adata.obs

[3]:

	brain_section_label	x	y	z	x_ccf	y_ccf	z_ccf	region	cell_type
cell_label
198904341065180396762707397604803217407	Zhuang-ABCA-3.023	49.206853	44.877634	12.168155	4.920685	4.487763	1.216815	SSs1	33 Vascular
252199681526991424029643077826220097990	Zhuang-ABCA-3.023	48.973992	44.813761	12.179006	4.897399	4.481376	1.217901	SSs1	33 Vascular
277720971126854564514249564750701518375	Zhuang-ABCA-3.023	48.791066	44.577722	12.192707	4.879107	4.457772	1.219271	SSs1	33 Vascular
31551867344111790264292067056219852271	Zhuang-ABCA-3.023	48.830489	44.426120	12.195078	4.883049	4.442612	1.219508	SSs1	33 Vascular
131102494428104399865219008178262036485	Zhuang-ABCA-3.023	48.308843	43.028156	12.267879	4.830884	4.302816	1.226788	SSs1	34 Immune
...	...	...	...	...	...	...	...	...	...
318102106429791409781741726367984532777	Zhuang-ABCA-3.009	131.090716	69.334275	41.436743	13.109072	6.933427	4.143674	MDRNd	30 Astro-Epen
35262847161560382172299767067854387528	Zhuang-ABCA-3.009	131.216032	69.494070	41.351034	13.121603	6.949407	4.135103	MDRNd	33 Vascular
75415866509570969932943497000463821106	Zhuang-ABCA-3.009	131.415152	70.764504	40.800403	13.141515	7.076450	4.080040	sctd	24 MY Glut
12350978322417280063239916106423065862	Zhuang-ABCA-3.009	131.646167	71.182557	40.595995	13.164617	7.118256	4.059599	sctd	24 MY Glut
327554758863546024460748891922509519354	Zhuang-ABCA-3.009	131.658892	71.414675	40.501356	13.165889	7.141468	4.050136	sctd	24 MY Glut

1566842 rows × 9 columns

[4]:

# Filtering glutamatergic excitatory neurons

adata = adata[adata.obs['cell_type'].str.contains('Glut')]
# adata = adata[adata.obs['cell_type'].str.contains('GABA')]

adata.obs

[4]:

	brain_section_label	x	y	z	x_ccf	y_ccf	z_ccf	region	cell_type
cell_label
207252950882079766503645227815929952400	Zhuang-ABCA-3.023	50.597984	41.393473	12.239274	5.059798	4.139347	1.223927	SSs2/3	01 IT-ET Glut
311894855078226645952213910865897976013	Zhuang-ABCA-3.023	50.420950	41.271525	12.251970	5.042095	4.127152	1.225197	SSs1	01 IT-ET Glut
125208524519663791324346814779771999476	Zhuang-ABCA-3.023	50.959183	43.276307	12.158869	5.095918	4.327631	1.215887	SSs2/3	01 IT-ET Glut
12594778395225515056477813574460470379	Zhuang-ABCA-3.023	49.836112	42.209685	12.238386	4.983611	4.220968	1.223839	SSs1	01 IT-ET Glut
148621603142722639702356861951538418099	Zhuang-ABCA-3.023	51.023440	42.722536	12.174236	5.102344	4.272254	1.217424	SSs2/3	01 IT-ET Glut
...	...	...	...	...	...	...	...	...	...
235686740179650165428728286428984964004	Zhuang-ABCA-3.009	131.552253	69.048948	41.463859	13.155225	6.904895	4.146386	MDRNd	24 MY Glut
286400092677302831060643120351923719874	Zhuang-ABCA-3.009	131.640659	70.151099	41.013819	13.164066	7.015110	4.101382	rust	24 MY Glut
75415866509570969932943497000463821106	Zhuang-ABCA-3.009	131.415152	70.764504	40.800403	13.141515	7.076450	4.080040	sctd	24 MY Glut
12350978322417280063239916106423065862	Zhuang-ABCA-3.009	131.646167	71.182557	40.595995	13.164617	7.118256	4.059599	sctd	24 MY Glut
327554758863546024460748891922509519354	Zhuang-ABCA-3.009	131.658892	71.414675	40.501356	13.165889	7.141468	4.050136	sctd	24 MY Glut

530094 rows × 9 columns

[5]:

th_regions = ['AD', 'AMd', 'AMv', 'AV', 'CL', 'CM', 'IAD', 'IAM', 'IGL', 'IMD', 'LD', 'LGv', 'LH', 'LP', 'MD', 'MGd', 'MGm', 'MGv', 'MH', 'PCN', 'PF', 'PIL', 'PO', 'POL',
                'PP', 'PR', 'PT', 'PVT', 'PoT', 'RE', 'RH', 'RT', 'SGN', 'SMT', 'SPA', 'SPFm', 'SPFp', 'VAL', 'VM', 'VPL', 'VPLpc', 'VPM', 'VPMpc', 'Xi']
ctx_regions = ['ACAd', 'ACAv', 'AId', 'AIp', 'AIv', 'AUDd', 'AUDp', 'AUDpo',
                'AUDv', 'ECT', 'FRP', 'GU', 'ILA', 'MOp', 'MOs', 'ORBl', 'ORBm',
                'ORBvl', 'PERI', 'PL', 'RSPagl', 'RSPd', 'RSPv', 'SSp-bfd',
                'SSp-ll', 'SSp-m', 'SSp-n', 'SSp-tr', 'SSp-ul', 'SSp-un', 'SSs',
                'TEa', 'VISC', 'VISa', 'VISal', 'VISam', 'VISl', 'VISli', 'VISp',
                'VISpl', 'VISpm', 'VISpor', 'VISrl']

# Filtering thalamic nuclei
adata_sc_sel = adata[adata.obs['region'].isin(th_regions)]
sc.pp.normalize_total(adata_sc_sel, target_sum=1e4)
sc.pp.log1p(adata_sc_sel)
adata_sc_sel

[5]:

AnnData object with n_obs × n_vars = 11632 × 1122
    obs: 'brain_section_label', 'x', 'y', 'z', 'x_ccf', 'y_ccf', 'z_ccf', 'region', 'cell_type'
    uns: 'log1p'

ctx to th connection cluster

[6]:

conn = pd.read_csv('../data/ctx2th_ave_connection_strength.csv', index_col=0)
conn = conn.transpose()
conn

[6]:

	ACAd	ACAv	AId	AIp	AIv	AUDd	AUDp	AUDpo	AUDv	ECT	...	VISa	VISal	VISam	VISl	VISli	VISp	VISpl	VISpm	VISpor	VISrl
AD	0.000152	0.000192	7.619523e-06	0.000010	5.887578e-06	8.007340e-06	1.649951e-05	3.103780e-05	0.000015	0.000025	...	0.000170	2.369635e-05	0.000322	4.295822e-05	5.189147e-05	7.696756e-05	3.874516e-05	0.000292	7.026182e-05	2.951606e-05
AMd	0.000664	0.000746	6.691810e-05	0.000014	4.837233e-05	5.128748e-06	7.461110e-06	1.335113e-05	0.000007	0.000012	...	0.000224	1.118673e-05	0.000421	1.502842e-05	1.854714e-05	7.710190e-05	1.683196e-05	0.000361	3.370710e-05	3.270745e-05
AMv	0.000771	0.000820	1.228778e-04	0.000021	8.823841e-05	9.028019e-06	1.033547e-05	1.672509e-05	0.000010	0.000018	...	0.000252	1.404998e-05	0.000445	1.600437e-05	2.015853e-05	7.672693e-05	1.626044e-05	0.000354	3.496936e-05	3.920881e-05
AV	0.000480	0.000474	1.013730e-05	0.000001	5.824439e-06	1.495290e-05	1.333239e-05	2.317443e-05	0.000005	0.000005	...	0.000264	2.638206e-05	0.000499	2.391140e-05	2.765813e-05	9.161290e-05	2.125825e-05	0.000429	3.275231e-05	4.204660e-05
CL	0.000601	0.000533	9.876311e-05	0.000044	5.617855e-05	4.381585e-05	2.893944e-05	4.043519e-05	0.000030	0.000046	...	0.000267	4.858192e-05	0.000373	3.168155e-05	3.748191e-05	5.555781e-05	1.795354e-05	0.000216	3.810383e-05	7.053605e-05
CM	0.000154	0.000158	3.049474e-04	0.000152	3.050938e-04	4.447017e-06	2.623114e-06	1.758501e-06	0.000013	0.000038	...	0.000017	1.545642e-06	0.000026	1.236815e-06	1.604649e-06	4.150310e-06	1.266425e-06	0.000017	2.776284e-06	4.047243e-06
IAD	0.000534	0.000577	6.229742e-05	0.000018	4.651523e-05	3.388965e-06	5.776069e-06	9.366712e-06	0.000007	0.000013	...	0.000157	6.828661e-06	0.000258	1.073826e-05	1.372449e-05	3.977765e-05	9.783908e-06	0.000176	2.286415e-05	2.615695e-05
IAM	0.000407	0.000462	7.161324e-05	0.000025	5.872044e-05	4.505570e-06	6.958557e-06	1.079134e-05	0.000008	0.000015	...	0.000100	6.632828e-06	0.000161	8.867795e-06	1.213848e-05	2.539850e-05	7.891840e-06	0.000101	2.436560e-05	1.847467e-05
IGL	0.000056	0.000083	1.004325e-06	0.000002	9.865555e-07	4.479001e-04	4.958735e-04	7.268359e-04	0.000302	0.000173	...	0.000869	8.568048e-04	0.001036	8.799476e-04	9.301578e-04	8.452453e-04	7.478799e-04	0.001060	9.557927e-04	8.330460e-04
IMD	0.000075	0.000106	5.153220e-04	0.000309	6.609953e-04	2.648984e-06	1.873951e-06	5.663784e-07	0.000015	0.000056	...	0.000003	4.350422e-07	0.000004	1.966642e-07	2.633820e-07	5.282716e-07	1.343521e-07	0.000002	4.778532e-07	6.684651e-07
LD	0.000489	0.000518	4.249831e-06	0.000002	2.572958e-06	1.374043e-04	1.348493e-04	2.698632e-04	0.000055	0.000054	...	0.001306	4.566688e-04	0.002117	4.509734e-04	4.352127e-04	6.663605e-04	3.373972e-04	0.001797	3.810647e-04	5.688850e-04
LGv	0.000084	0.000107	4.184993e-07	0.000001	4.032791e-07	3.592279e-04	4.208136e-04	6.828435e-04	0.000240	0.000156	...	0.000836	8.694233e-04	0.000987	1.023182e-03	1.043101e-03	9.509905e-04	9.423530e-04	0.001049	1.052003e-03	8.329139e-04
LH	0.000102	0.000153	2.734611e-05	0.000013	2.699298e-05	3.924288e-06	5.615375e-06	6.320725e-06	0.000009	0.000017	...	0.000023	3.949801e-06	0.000037	4.087569e-06	5.569459e-06	7.428656e-06	3.175474e-06	0.000029	7.957854e-06	3.783382e-06
LP	0.000288	0.000296	6.197639e-06	0.000020	3.744213e-06	4.537852e-04	5.480817e-04	8.224930e-04	0.000377	0.000308	...	0.001147	8.127701e-04	0.001515	8.759366e-04	1.010362e-03	7.472354e-04	7.065590e-04	0.001195	1.220962e-03	7.001120e-04
MD	0.000269	0.000260	4.223403e-04	0.000122	4.409378e-04	3.972758e-06	2.444553e-06	1.862257e-06	0.000010	0.000028	...	0.000026	2.075441e-06	0.000035	9.125018e-07	1.194986e-06	3.742693e-06	5.212484e-07	0.000018	1.489977e-06	5.346532e-06
MGd	0.000006	0.000006	4.138417e-06	0.000018	5.092190e-06	1.370971e-03	2.030689e-03	1.555737e-03	0.002156	0.000981	...	0.000236	5.957695e-04	0.000107	3.715169e-04	5.884749e-04	1.483566e-04	1.423061e-04	0.000079	6.546437e-04	1.855289e-04
MGm	0.000098	0.000092	9.269953e-06	0.000106	8.027737e-06	6.044557e-04	6.525721e-04	5.064571e-04	0.000679	0.000324	...	0.000250	3.010070e-04	0.000216	1.563492e-04	2.241432e-04	7.875042e-05	6.039701e-05	0.000114	2.255879e-04	1.741915e-04
MGv	0.000014	0.000014	3.641458e-06	0.000035	4.308976e-06	1.386466e-03	1.792402e-03	1.285505e-03	0.001912	0.000785	...	0.000255	5.595335e-04	0.000142	3.047580e-04	4.719067e-04	1.228973e-04	1.120924e-04	0.000093	4.859480e-04	1.984513e-04
MH	0.000006	0.000020	1.140038e-06	0.000002	9.041835e-07	5.921155e-07	7.945028e-07	1.165024e-06	0.000001	0.000002	...	0.000002	1.007101e-06	0.000003	1.536386e-06	1.894867e-06	1.410430e-06	1.537721e-06	0.000003	2.361166e-06	6.432470e-07
PCN	0.000129	0.000077	4.909851e-04	0.000182	4.074414e-04	1.138335e-05	3.342644e-06	1.527520e-06	0.000018	0.000037	...	0.000015	2.878176e-06	0.000014	6.444761e-07	7.306620e-07	1.536464e-06	2.292821e-07	0.000006	3.976918e-07	7.208488e-06
PF	0.000332	0.000307	2.923657e-04	0.000222	2.497150e-04	8.224716e-05	3.574478e-05	3.965936e-05	0.000041	0.000063	...	0.000148	5.855558e-05	0.000159	1.465902e-05	1.827707e-05	1.723299e-05	2.593885e-06	0.000070	8.039294e-06	6.720717e-05
PIL	0.000094	0.000090	4.543945e-05	0.000174	4.551988e-05	6.713279e-04	6.391818e-04	4.925301e-04	0.000660	0.000453	...	0.000194	3.049723e-04	0.000140	1.502271e-04	2.205857e-04	6.166135e-05	6.529530e-05	0.000074	2.261566e-04	1.370516e-04
PO	0.000159	0.000102	2.125703e-04	0.000237	1.440922e-04	1.015075e-04	4.448542e-05	4.486687e-05	0.000073	0.000114	...	0.000182	6.995570e-05	0.000182	2.545733e-05	2.778704e-05	2.931024e-05	9.100669e-06	0.000082	1.714313e-05	1.009701e-04
POL	0.000194	0.000174	2.934526e-05	0.000058	1.894002e-05	4.916248e-04	4.057512e-04	3.905389e-04	0.000309	0.000135	...	0.000497	3.527876e-04	0.000546	1.614274e-04	2.023932e-04	1.266951e-04	5.954121e-05	0.000286	1.398459e-04	2.661806e-04
PP	0.000054	0.000058	6.608591e-06	0.000108	4.657731e-06	9.965551e-04	9.757414e-04	8.059254e-04	0.000908	0.000492	...	0.000285	5.198091e-04	0.000209	3.102234e-04	4.296206e-04	1.424547e-04	1.574262e-04	0.000143	4.595614e-04	2.347075e-04
PR	0.000345	0.000534	7.168793e-05	0.000089	6.519891e-05	2.437460e-05	2.362251e-05	2.556900e-05	0.000034	0.000074	...	0.000085	2.290209e-05	0.000139	1.397124e-05	1.765865e-05	2.640149e-05	8.851335e-06	0.000109	1.655532e-05	1.801183e-05
PT	0.000103	0.000234	3.647708e-05	0.000036	3.049506e-05	1.662613e-06	5.080007e-06	6.254634e-06	0.000010	0.000021	...	0.000015	2.578893e-06	0.000028	7.649383e-06	1.036695e-05	9.467975e-06	1.106287e-05	0.000027	2.015822e-05	2.790195e-06
PVT	0.000061	0.000162	5.087197e-05	0.000112	6.205588e-05	1.895285e-06	3.650877e-06	2.446327e-06	0.000015	0.000045	...	0.000004	1.210090e-06	0.000006	2.229057e-06	2.904354e-06	2.274457e-06	2.982967e-06	0.000006	5.376556e-06	8.233745e-07
PoT	0.000220	0.000198	9.930714e-05	0.000266	7.676983e-05	3.366202e-04	2.481721e-04	2.215150e-04	0.000242	0.000185	...	0.000292	2.077052e-04	0.000284	8.315387e-05	1.090200e-04	5.730652e-05	2.801180e-05	0.000132	8.839658e-05	1.790629e-04
RE	0.000245	0.000365	4.118779e-05	0.000057	3.650209e-05	1.109817e-05	1.062494e-05	1.076608e-05	0.000020	0.000046	...	0.000054	9.486939e-06	0.000083	8.174288e-06	1.020787e-05	1.535013e-05	8.586715e-06	0.000057	1.477839e-05	1.231446e-05
RH	0.000323	0.000355	2.290540e-04	0.000125	2.070770e-04	2.147445e-05	1.247322e-05	1.318052e-05	0.000023	0.000049	...	0.000100	1.685108e-05	0.000148	1.075674e-05	1.148609e-05	2.507019e-05	7.163141e-06	0.000095	1.037849e-05	3.002259e-05
RT	0.000336	0.000331	1.370683e-04	0.000062	1.068166e-04	1.396533e-04	1.346184e-04	1.466915e-04	0.000116	0.000064	...	0.000372	1.466425e-04	0.000505	1.194276e-04	1.311705e-04	1.431161e-04	8.363944e-05	0.000360	1.234873e-04	1.596459e-04
SGN	0.000039	0.000036	1.212349e-06	0.000033	1.155121e-06	9.708160e-04	1.204355e-03	1.076860e-03	0.001139	0.000585	...	0.000304	5.648817e-04	0.000210	3.721666e-04	5.458286e-04	1.624372e-04	1.747654e-04	0.000123	6.187994e-04	2.306022e-04
SMT	0.000335	0.000342	3.431704e-04	0.000112	2.872273e-04	3.548723e-05	1.959870e-05	2.244632e-05	0.000026	0.000053	...	0.000105	3.033701e-05	0.000129	1.441583e-05	1.556353e-05	2.082781e-05	6.530777e-06	0.000065	9.591299e-06	4.418818e-05
SPA	0.000244	0.000346	1.559442e-04	0.000151	1.908120e-04	9.964673e-06	1.132277e-05	8.209178e-06	0.000035	0.000097	...	0.000030	5.998488e-06	0.000049	2.923863e-06	4.163565e-06	6.604036e-06	1.184191e-06	0.000030	5.183471e-06	4.657965e-06
SPFm	0.000361	0.000506	5.206138e-05	0.000067	5.164480e-05	5.544868e-05	5.396699e-05	5.161003e-05	0.000065	0.000100	...	0.000111	3.910392e-05	0.000165	2.007481e-05	2.814081e-05	2.671656e-05	1.007117e-05	0.000108	2.540315e-05	2.555836e-05
SPFp	0.000483	0.000453	1.473219e-04	0.000241	1.269824e-04	2.484715e-04	1.489215e-04	1.349840e-04	0.000152	0.000152	...	0.000314	1.504822e-04	0.000300	4.003079e-05	5.483582e-05	3.108448e-05	8.224509e-06	0.000114	3.178376e-05	1.516782e-04
VAL	0.000425	0.000314	2.663332e-04	0.000050	1.547514e-04	5.536264e-05	2.768267e-05	3.713600e-05	0.000021	0.000015	...	0.000219	5.177339e-05	0.000277	2.376908e-05	2.641484e-05	3.945724e-05	9.022712e-06	0.000152	1.411106e-05	7.628709e-05
VM	0.000482	0.000435	3.047505e-04	0.000105	2.542895e-04	2.697555e-05	1.330547e-05	1.394733e-05	0.000019	0.000038	...	0.000106	1.836394e-05	0.000133	5.678558e-06	7.094294e-06	1.460424e-05	1.669783e-06	0.000065	4.296176e-06	3.128889e-05
VPL	0.000161	0.000133	5.194950e-05	0.000088	3.739928e-05	1.386123e-04	9.940196e-05	9.289303e-05	0.000102	0.000051	...	0.000329	9.927339e-05	0.000363	4.804453e-05	5.609559e-05	5.795776e-05	2.087232e-05	0.000183	4.221700e-05	1.329029e-04
VPLpc	0.000242	0.000241	4.872967e-04	0.001075	5.321095e-04	6.394511e-05	2.915340e-05	2.226937e-05	0.000078	0.000142	...	0.000113	2.905744e-05	0.000146	7.872736e-06	1.001747e-05	1.928587e-05	2.375961e-06	0.000083	7.822995e-06	4.249422e-05
VPM	0.000039	0.000025	1.681293e-04	0.000161	1.229011e-04	1.281432e-04	6.375379e-05	5.103292e-05	0.000089	0.000044	...	0.000084	6.541547e-05	0.000058	2.321276e-05	2.689635e-05	1.637556e-05	1.015162e-05	0.000029	1.910489e-05	7.831720e-05
VPMpc	0.000153	0.000159	6.775987e-04	0.000897	7.926928e-04	4.565133e-05	2.844526e-05	2.008600e-05	0.000082	0.000198	...	0.000040	1.883211e-05	0.000047	6.423367e-06	8.770814e-06	7.163959e-06	2.530284e-06	0.000026	7.503612e-06	1.933129e-05
Xi	0.000309	0.000434	4.451510e-05	0.000081	4.349116e-05	7.773328e-06	8.470673e-06	6.383956e-06	0.000022	0.000054	...	0.000031	5.004218e-06	0.000047	3.549756e-06	4.567458e-06	7.711804e-06	3.077690e-06	0.000030	6.779844e-06	6.532870e-06

44 rows × 43 columns

[7]:

g = sns.clustermap(conn, figsize=(12,12))

_images/1_ctx_th_connection_and_gene_exp_cluster_7_0.png

[8]:

from scipy.cluster import hierarchy
col_linkage = g.dendrogram_col.linkage
col_clusters = hierarchy.fcluster(col_linkage, t=0.5, criterion='distance')
reordered_cols = g.dendrogram_col.reordered_ind
col_labels = np.array(conn.columns)[reordered_cols]
col_labels

[8]:

array(['ECT', 'PERI', 'AUDp', 'AUDv', 'AUDd', 'AUDpo', 'TEa', 'VISal',
       'VISli', 'VISpor', 'VISl', 'VISpl', 'VISp', 'VISrl', 'VISam',
       'VISpm', 'VISa', 'RSPv', 'RSPagl', 'RSPd', 'AId', 'AIv', 'SSs',
       'GU', 'AIp', 'VISC', 'SSp-tr', 'SSp-m', 'SSp-n', 'MOp', 'SSp-bfd',
       'SSp-ll', 'SSp-ul', 'SSp-un', 'ILA', 'ORBm', 'PL', 'ORBl', 'ORBvl',
       'ACAd', 'ACAv', 'FRP', 'MOs'], dtype=object)

[9]:

# Use UMAP for 2D dimension reduction
reducer = umap.UMAP(n_components=2, random_state=43)
embedding = reducer.fit_transform(conn)

# Classification of UMAP results
n_clusters=8
kmeans = KMeans(n_clusters=n_clusters, random_state=44)
clusters = kmeans.fit_predict(embedding)

# Convert Embedding to DataFrame for easy operation
embedding_df = pd.DataFrame(embedding, index=conn.index, columns=['UMAP1', 'UMAP2'])
embedding_df['Cluster'] = clusters

color_list = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd','#8c564b', '#17becf', '#e377c2']

fig, ax = plt.subplots(figsize=(8, 7))
from scipy.spatial import ConvexHull
from shapely.geometry import Polygon
# Draw a 2D UMAP diagram with clustering results
for cluster in range(n_clusters):
    subset = embedding_df[embedding_df['Cluster'] == cluster]
    points = subset[['UMAP1', 'UMAP2']].values

    # Get color
    color = color_list[cluster]

    # Calculate and draw the expanded and smooth convex bag
    if len(points) > 2:  # Ne convex bags require at least 3 points
        hull = ConvexHull(points)
        hull_points = points[hull.vertices]

        # Create polygon and expand
        poly = Polygon(hull_points).buffer(0.3)
        # Make the polygon smoother
        smooth_poly = poly.buffer(0.2, resolution=16, join_style=1).buffer(-0.2, resolution=16, join_style=1)
        # Get the coordinates of polygonal shape
        x, y = smooth_poly.exterior.xy
        # Draw a smooth polygon
        ax.fill(x, y, alpha=0.3, fc=color, ec=color)

# Add sample name names as labels, and set label color to the same color scattering color
for sample in embedding_df.index:
    cluster = embedding_df.loc[sample, 'Cluster']
    x = embedding_df.loc[sample, 'UMAP1']
    y = embedding_df.loc[sample, 'UMAP2']
    plt.annotate(sample,
                 (x, y),
                 xytext=(0, 3),  # The text shifts 5 units in the Y direction
                 textcoords='offset points',  # Specify unit with offset
                 fontsize=15,
                #  color='black',
                 color=color_list[cluster],
                 ha='center',  # Horizontal alignment
                 va='bottom',  # Align in the bottom of the vertical direction (close to the offset point)
                 alpha=0.7
                 )
plt.xticks([])
plt.yticks([])
plt.xlabel('UMAP1')
plt.ylabel('UMAP2')
plt.tight_layout()
# plt.savefig('./conn_umap.pdf', format='pdf')

_images/1_ctx_th_connection_and_gene_exp_cluster_9_0.png

[10]:

embedding_df['Cluster'] = embedding_df['Cluster'].astype(str)
th_cluster_order = []
num = []
for i in range(8):
    tmp = embedding_df[embedding_df['Cluster'] == str(i)]
    th_cluster_order = th_cluster_order + tmp.index.tolist()
    num.append(len(tmp))
    print(tmp.index.tolist())
th_cluster_order

ctx_cluster_order = ['ECT', 'PERI', 'AUDp', 'AUDv', 'AUDd', 'AUDpo', 'TEa', 'VISal',
       'VISli', 'VISpor', 'VISl', 'VISpl', 'VISp', 'VISrl', 'VISam',
       'VISpm', 'VISa', 'RSPv', 'RSPagl', 'RSPd', 'AId', 'AIv', 'SSs',
       'GU', 'AIp', 'VISC', 'SSp-tr', 'SSp-m', 'SSp-n', 'MOp', 'SSp-bfd',
       'SSp-ll', 'SSp-ul', 'SSp-un', 'ILA', 'ORBm', 'PL', 'ORBl', 'ORBvl',
       'ACAd', 'ACAv', 'FRP', 'MOs']

['PCN', 'PF', 'VPLpc', 'VPMpc']
['AD', 'AMd', 'AMv', 'AV', 'IAD']
['MGd', 'MGm', 'MGv', 'PIL', 'POL', 'PP', 'SGN']
['IAM', 'LH', 'MH', 'PR', 'PT', 'PVT', 'RE', 'SPA', 'SPFm', 'Xi']
['IGL', 'LD', 'LGv', 'LP']
['CM', 'IMD', 'MD', 'RH', 'SMT', 'VM']
['PO', 'PoT', 'SPFp', 'VPL', 'VPM']
['CL', 'RT', 'VAL']

[11]:

# Define color mapping
cluster_labels = ['TH0']*num[0] + ['TH1']*num[1] + ['TH2']*num[2] + ['TH3']*num[3] + ['TH4']*num[4] + ['TH5']*num[5] + ['TH6']*num[6] + ['TH7']*num[7]

color_list = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd','#8c564b', '#17becf', '#e377c2']
cluster_colors = {'TH0': color_list[0], 'TH1': color_list[1], 'TH2': color_list[2],
                  'TH3': color_list[3], 'TH4': color_list[4], 'TH5': color_list[5], 'TH6': color_list[6], 'TH7': color_list[7]}
# Create Label Color
row_colors = pd.Series(cluster_labels).map(cluster_colors)

fig, ax = plt.subplots(figsize=(12, 10))
sns.heatmap(conn.loc[th_cluster_order, ctx_cluster_order], ax=ax, yticklabels=False)
# Add cluster segmentation line
prev_cluster = cluster_labels[0]
for idx, label in enumerate(cluster_labels):
    if label != prev_cluster:
        ax.axhline(idx, color='white', linewidth=2)
        prev_cluster = label

# Add row label color
idx = 0
for id, label in zip(th_cluster_order, cluster_labels):
    ax.text(-1.0, idx + 0.5, id, color='white', va='center', ha='right',fontweight='bold',
            bbox=dict(facecolor=cluster_colors[label], edgecolor='none', boxstyle='round,pad=0.15'))
    idx += 1

# plt.savefig('./Supp_fig/ctx2th_conn_ordered_by_umap.pdf', format='pdf')

_images/1_ctx_th_connection_and_gene_exp_cluster_11_0.png

gene exp umap

[12]:

value_to_label = dict(zip(embedding_df.index, embedding_df['Cluster']))
adata_sc_sel.obs['all_Glut_Neurons'] = adata_sc_sel.obs['region'].map(value_to_label).astype('str')
adata_sc_sel.obs['all_Glut_Neurons'] = 'TH_' + (adata_sc_sel.obs['all_Glut_Neurons'].astype(int)+1).astype(str)

[13]:

sc.tl.pca(adata_sc_sel)
sc.pp.neighbors(adata_sc_sel)
sc.tl.umap(adata_sc_sel, n_components=2)

[14]:

# sc.pl.umap(adata_sc_sel, color='all_Glut_Neurons', show=False,add_outline=True,legend_fontsize=12,legend_fontoutline=2, size=20)
# Create color list
# color_list = plt.cm.tab10(np.linspace(0, 1, n_clusters))
color_list = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd','#8c564b', '#17becf', '#e377c2']
# Create a color dictionary
unique_neurons = adata_sc_sel.obs['all_Glut_Neurons'].unique()
color_dict = dict(zip(sorted(unique_neurons), color_list))
# Draw UMAP diagram
sc.pl.umap(adata_sc_sel, color='all_Glut_Neurons', size=20, palette=color_dict, show=False)
# sc.pl.umap(adata_sc_sel, color='all_Glut_Neurons', size=20)
plt.tight_layout()
# plt.savefig('th_gene_exp_umap.pdf', format='pdf')

_images/1_ctx_th_connection_and_gene_exp_cluster_15_0.png

[15]:

for i in range(8):
    sub = f'TH_{i+1}'
    adata_sc_sel.obs[sub] = adata_sc_sel.obs['all_Glut_Neurons']
    adata_sc_sel.obs.loc[adata_sc_sel.obs['all_Glut_Neurons'] != sub, sub] = None
    sc.pl.umap(adata_sc_sel, color=sub, palette=color_dict, show=False)
    # plt.savefig(f'./Supp_fig/th_gene_exp_umap_cluster{i+1}.pdf', format='pdf')
    # plt.savefig(f'./Supp_fig/th_gene_exp_umap_cluster{i+1}.png', dpi=400)

_images/1_ctx_th_connection_and_gene_exp_cluster_16_0.png

_images/1_ctx_th_connection_and_gene_exp_cluster_16_1.png

_images/1_ctx_th_connection_and_gene_exp_cluster_16_2.png

_images/1_ctx_th_connection_and_gene_exp_cluster_16_3.png

_images/1_ctx_th_connection_and_gene_exp_cluster_16_4.png

_images/1_ctx_th_connection_and_gene_exp_cluster_16_5.png

_images/1_ctx_th_connection_and_gene_exp_cluster_16_6.png

_images/1_ctx_th_connection_and_gene_exp_cluster_16_7.png

[ ]: