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Neural Excitation-Inhibition Imbalance in Cervical Spondylotic Myelopathy.

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  1. [1] § Results › Transcriptional Patterns Associated with Regional Changes in MSN Strength ↔ PLSanalysis.py, lines 160–212 · score 0.60 · n_bootstrap, n_permutations, brain region scores, SE, variance, fit

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The authors' code

Python · 403 lines · 12 KB · no license · 1 match

  1. import pandas as pd
  2. import numpy as np
  6. gene_expression_df = pd.read_csv(
  7. r'D:\data\1\CSM_gene\dgene.csv',
  8. index_col=0
  9. )
  11. t_value = pd.read_csv(
  12. r'D:\data\1\CSM_gene\t.csv',
  13. header=None
  14. )
  15. from scipy.stats import percentileofscore
  16. from sklearn.cross_decomposition import PLSRegression
  17. from sklearn.utils import resample
  18. import matplotlib.pyplot as plt
  19. import numpy as np
  20. import pandas as pd
  21. from tqdm import tqdm
  23. import numpy as np
  24. import random
  25. from sklearn.utils import resample
  27. np.random.seed(1234)
  28. random.seed(1234)
  31. pls = PLSRegression(n_components = 15)
  34. gene_expression_matrix = gene_expression_df.values
  37. assert gene_expression_matrix.shape[0] == t_value.shape[0], \
  41. pls.fit(gene_expression_matrix, t_value)
  44. pls_loadings_all_components = pls.x_weights_
  47. pls1_loadings = pls_loadings_all_components[:, 0]
  50. n_permutations = 1000
  51. random_loadings = np.zeros((n_permutations, pls1_loadings.shape[0]))
  54. for i in tqdm(range(n_permutations), desc="Permutation Test", ncols=100):
  56. shuffled_network_vector = np.random.permutation(t_value)
  57. pls.fit(gene_expression_matrix, shuffled_network_vector)
  58. random_loadings[i, :] = pls.x_weights_[:, 0]
  61. p_values = np.array([
  62. min(
  63. (100 - percentileofscore(random_loadings[:, j], pls1_loadings[j])) / 100,
  64. percentileofscore(random_loadings[:, j], pls1_loadings[j]) / 100
  65. )
  66. for j in range(pls1_loadings.shape[0])
  67. ])
  70. n_bootstrap = 1000 # bootstrap
  71. bootstrap_loadings = np.zeros((n_bootstrap, pls1_loadings.shape[0]))
  74. for i in tqdm(range(n_bootstrap), desc="Bootstrap Resampling", ncols=100):
  76. sampled_matrix, sampled_network_vector = resample(gene_expression_matrix, t_value, replace=True, random_state=1234 + i)
  77. pls.fit(sampled_matrix, sampled_network_vector)
  78. bootstrap_loadings[i, :] = pls.x_weights_[:, 0]
  81. bootstrap_se = bootstrap_loadings.std(axis=0)
  84. z_values = pls1_loadings / bootstrap_se
  87. loadings_df = pd.DataFrame({
  88. 'Gene': gene_expression_df.columns,
  89. 'PLS1_Loading': pls1_loadings,
  90. 'p_value': p_values,
  91. 'Z_value': z_values
  92. })
  95. loadings_df['Absolute_Loading'] = loadings_df['PLS1_Loading'].abs()
  96. ranked_genes_df = loadings_df.sort_values('Absolute_Loading', ascending=False).reset_index(drop=True)
  97. ranked_genes_df['Rank'] = ranked_genes_df.index + 1
  99. # Identify PLS1+ and PLS1- genes.
  100. pls1_plus = ranked_genes_df[ranked_genes_df['PLS1_Loading'] > 0]
  101. pls1_minus = ranked_genes_df[ranked_genes_df['PLS1_Loading'] < 0]
  104. print(ranked_genes_df.head())
  107. plt.figure(figsize=(10, 6))
  108. plt.barh(ranked_genes_df['Gene'][:20], ranked_genes_df['Absolute_Loading'][:20], color='skyblue')
  109. plt.xlabel('PLS1 Loading (Absolute Value)')
  110. plt.ylabel('Gene')
  111. plt.title('Top 20 Genes with Highest PLS1 Loadings')
  112. plt.gca().invert_yaxis()
  113. plt.tight_layout()
  116. plt.show()
  118. output_csv = r'D:\data\1\CSM_gene\PLS1\pls1_result.csv'
  119. ranked_genes_df.to_csv(output_csv, index=False)
  120. print(f"PLS1 {output_csv}")
  123. pls1_plus_csv = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1+_genes.csv'
  124. pls1_minus_csv = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1-_genes.csv'
  126. pls1_plus.to_csv(pls1_plus_csv, index=False)
  127. pls1_minus.to_csv(pls1_minus_csv, index=False)
  129. print(f"PLS1+ {pls1_plus_csv}")
  130. print(f"PLS1- {pls1_minus_csv}")
  132. pls.fit(gene_expression_matrix, t_value)
  135. brain_region_scores_pls1 = pls.x_scores_[:, 0]
  138. brain_regions_df = pd.DataFrame({
  139. 'Brain_Region': gene_expression_df.index,
  140. 'PLS1_Score': brain_region_scores_pls1
  141. })
  144. output_path = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1_brain_region_results.csv'
  145. brain_regions_df.to_csv(output_path, index=False)
  148. print(brain_regions_df.head())
  150. brain_region_scores_pls1 = pls.x_scores_[:, 0]
  153. brain_regions_df = pd.DataFrame({
  154. 'Brain_Region': gene_expression_df.index,
  155. 'PLS1_Score': brain_region_scores_pls1
  156. })
  160. n_permutations = 10000
  161. random_scores = np.zeros((n_permutations, brain_region_scores_pls1.shape[0]))
  164. for i in tqdm(range(n_permutations), desc="Permutation Test", ncols=100):
  166. shuffled_network_vector = np.random.permutation(t_value)
  167. pls.fit(gene_expression_matrix, shuffled_network_vector)
  168. random_scores[i, :] = pls.x_scores_[:, 0]
  171. p_values = np.array([
  172. min(
  173. (100 - percentileofscore(random_scores[:, j], brain_region_scores_pls1[j])) / 100,
  174. percentileofscore(random_scores[:, j], brain_region_scores_pls1[j]) / 100
  175. )
  176. for j in range(brain_region_scores_pls1.shape[0])
  177. ])
  180. n_bootstrap = 10000
  181. bootstrap_scores = np.zeros((n_bootstrap, brain_region_scores_pls1.shape[0]))
  184. for i in tqdm(range(n_bootstrap), desc="Bootstrap Resampling", ncols=100):
  186. sampled_matrix, sampled_network_vector = resample(gene_expression_matrix, t_value, replace=True)
  187. pls.fit(sampled_matrix, sampled_network_vector)
  188. bootstrap_scores[i, :] = pls.x_scores_[:, 0]
  191. bootstrap_se = bootstrap_scores.std(axis=0)
  194. z_values = brain_region_scores_pls1 / bootstrap_se
  197. brain_regions_df['p_value'] = p_values
  198. brain_regions_df['Z_value'] = z_values
  201. output_path = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1_brain_region_pls1_results.csv'
  202. brain_regions_df.to_csv(output_path, index=False)
  205. print(brain_regions_df.head())
  207. x_scores = pls.x_scores_
  208. explained_variance = np.var(x_scores, axis=0)
  209. explained_variance_ratio = explained_variance / explained_variance.sum()
  212. cumulative_variance_ratio = np.cumsum(explained_variance_ratio)
  215. plt.figure(figsize=(10, 6))
  216. plt.plot(
  217. range(1, len(cumulative_variance_ratio) + 1),
  218. cumulative_variance_ratio,
  219. marker='o',
  220. markersize=8,
  221. color='skyblue',
  222. linestyle='-',
  223. linewidth=2.5
  224. )
  225. plt.xlabel('Number of PLS Component ', fontsize=14)
  226. plt.ylabel('Cumulative Variance Explained', fontsize=14)
  227. plt.title('Cumulative Variance Explained by PLS Components', fontsize=16, fontweight='bold')
  228. plt.xticks(range(1, len(cumulative_variance_ratio) + 1))
  229. plt.yticks(np.arange(0, 1.1, 0.1))
  230. plt.grid(False)
  231. plt.tight_layout()
  233. output_path = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1_ranked_genes_with_p_values_and_Z_LineChart_plot.pdf'
  234. plt.savefig(output_path, format='pdf', dpi=600)
  236. plt.show()
  238. print(cumulative_variance_ratio)
  240. x_scores = pls.x_scores_
  241. explained_variance = np.var(x_scores, axis=0)
  242. explained_variance_ratio = explained_variance / explained_variance.sum() * 100
  245. plt.figure(figsize=(10, 6))
  246. plt.bar(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, color='skyblue')
  247. plt.xlabel('PLS Component')
  248. plt.ylabel('Percentage of Variance Explained (%)')
  249. plt.title('Variance Explained by Each PLS Component')
  250. plt.xticks(range(1, len(explained_variance_ratio) + 1))
  251. plt.tight_layout()
  254. plt.show()
  255. save_path = r"D:\data\1\CSM_gene\PLS1"
  256. import os
  258. x_scores = pls.x_scores_
  259. explained_variance = np.var(x_scores, axis=0)
  260. explained_variance_ratio = explained_variance / explained_variance.sum() * 100
  263. cumulative_variance_ratio = np.cumsum(explained_variance_ratio)
  266. plt.figure(figsize=(10, 6))
  267. plt.bar(range(1, len(explained_variance_ratio) + 1), explained_variance_ratio, color='skyblue')
  268. plt.xlabel('PLS Component')
  269. plt.ylabel('Percentage of Variance Explained (%)')
  270. plt.title('Individual Variance Explained by Each PLS Component')
  271. plt.xticks(range(1, len(explained_variance_ratio) + 1))
  272. plt.tight_layout()
  273. individual_variance_file = os.path.join(save_path, 'significantTvalue_15component_PLS3_individual_variance.pdf')
  274. plt.savefig(individual_variance_file, format='pdf', dpi=300, bbox_inches='tight')
  275. plt.close()
  278. plt.figure(figsize=(10, 6))
  279. plt.plot(range(1, len(cumulative_variance_ratio) + 1), cumulative_variance_ratio,
  280. marker='o', color='red', linestyle='-')
  281. plt.xlabel('Number of Components')
  282. plt.ylabel('Cumulative Percentage of Variance Explained (%)')
  283. plt.title('Cumulative Variance Explained by PLS Components')
  284. plt.xticks(range(1, len(cumulative_variance_ratio) + 1))
  285. plt.grid(True, linestyle='--', alpha=0.7)
  286. plt.axhline(y=80, color='gray', linestyle='--', alpha=0.5)
  287. plt.axhline(y=95, color='gray', linestyle='--', alpha=0.5)
  288. plt.tight_layout()
  289. cumulative_variance_file = os.path.join(save_path, 'significantTvalue_15component_PLS1_cumulative_variance.pdf')
  290. plt.savefig(cumulative_variance_file, format='pdf', dpi=300, bbox_inches='tight')
  291. plt.close()
  294. import pandas as pd
  297. data_dict = {
  298. 'Component': range(1, len(explained_variance_ratio) + 1),
  299. 'Individual_Variance_Explained(%)': explained_variance_ratio,
  300. 'Cumulative_Variance_Explained(%)': cumulative_variance_ratio
  301. }
  303. df = pd.DataFrame(data_dict)
  306. csv_file = os.path.join(save_path, 'significantTvalue_15component_PLS1_variance_explained.csv')
  307. df.to_csv(csv_file, index=False)
  310. print("complete")
  311. print("1. individual_variance.pdf ")
  312. print("2. cumulative_variance.pdf ")
  313. print("3. pls_variance_explained.csv ")
  316. print("\nCSV")
  317. print(df.head())
  319. lower_percentile = np.percentile(ranked_genes_df['Z_value'], 5)
  320. upper_percentile = np.percentile(ranked_genes_df['Z_value'], 95)
  323. pls1_plus_genes = ranked_genes_df[ranked_genes_df['Z_value'] >= upper_percentile]
  324. pls1_minus_genes = ranked_genes_df[ranked_genes_df['Z_value'] <= lower_percentile]
  327. print(f"PLS1+ Genes (Top 5%): {pls1_plus_genes.shape[0]}")
  328. print(pls1_plus_genes[['Gene', 'Z_value']])
  329. print(f"PLS1- Genes (Bottom 5%): {pls1_minus_genes.shape[0]}")
  330. print(pls1_minus_genes[['Gene', 'Z_value']])
  333. pls1_plus_output_path = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1_ranked_genes_with_p_values_and_Z_PLS1+.csv'
  334. pls1_plus_genes.to_csv(pls1_plus_output_path, index=False)
  337. pls1_minus_output_path = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1_ranked_genes_with_p_values_and_Z_PLS1-.csv'
  338. pls1_minus_genes.to_csv(pls1_minus_output_path, index=False)
  340. print(f"PLS1+ {pls1_plus_output_path}")
  341. print(f"PLS1- {pls1_minus_output_path}")
  344. output_path = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1_ranked_genes_with_p_values_and_Z.csv'
  345. ranked_genes_df.to_csv(output_path, index=False)
  346. print(f"结果已保存至 {output_path}")
  349. plt.figure(figsize=(10, 6))
  350. plt.hist(ranked_genes_df['Z_value'], bins=50, color='skyblue', edgecolor='black')
  351. plt.title('Distribution of Z-values for PLS1 Genes')
  352. plt.xlabel('Z-value')
  353. plt.ylabel('Frequency')
  354. plt.tight_layout()
  355. plt.show()
  356. import matplotlib.pyplot as plt
  357. import numpy as np
  360. plt.rcParams['font.family'] = 'Arial'
  363. ranked_genes_df['neg_log10_p_value'] = -np.log10(ranked_genes_df['p_value'])
  366. colors = ranked_genes_df.apply(
  367. lambda row: 'red' if row['Z_value'] >= 1 and row['p_value'] < 0.05
  368. else ('skyblue' if row['Z_value'] <= -1 and row['p_value'] < 0.05 else 'grey'),
  369. axis=1
  370. )
  373. edge_colors = ranked_genes_df.apply(
  374. lambda row: 'darkred' if row['Z_value'] >= 1 and row['p_value'] < 0.05
  375. else ('darkblue' if row['Z_value'] <= -1 and row['p_value'] < 0.05 else 'black'),
  376. axis=1
  377. )
  380. plt.figure(figsize=(6, 6))
  381. plt.scatter(ranked_genes_df['Z_value'], ranked_genes_df['neg_log10_p_value'], c=colors, alpha=0.6, linewidths=1.5)
  382. plt.axhline(-np.log10(0.05), color='black', linestyle='--', label='p = 0.05')
  383. plt.axvline(1, color='orange', linestyle='--', label='PLS1+ Threshold')
  384. plt.axvline(-1, color='green', linestyle='--', label='PLS1- Threshold')
  387. x_min, x_max = int(ranked_genes_df['Z_value'].min()) - 1, int(ranked_genes_df['Z_value'].max()) + 1
  388. plt.xticks(np.arange(x_min, x_max + 1, 1))
  391. plt.xlabel('Z Value (PLS1 Loadings)', fontsize=14)
  392. plt.ylabel('-log10(p Value)', fontsize=14)
  393. plt.title('Volcano Plot of PLS1+ and PLS1- Genes', fontsize=16, fontweight='bold')
  394. plt.legend()
  395. plt.tight_layout()
  399. output_path = r'D:\data\1\CSM_gene\PLS1\significantTvalue_15component_PLS1_ranked_genes_with_p_values_and_Z_volcano_plot.pdf'
  400. plt.savefig(output_path, format='pdf', dpi=600)
  402. plt.show()
  403. print(f"{output_path}")

PLSanalysis.py at commit e6f623a, no license · at the source

Overview

Authors: Hongqing Wu1,2, Kun Zhu1,2, Haoxiang Wang1,2, Qiu Guo1,2, Zhichao Luo1, Sichen Bian1,2, Bingyong Xie1,2, Haoyu Ni1,2, Yuanyuan Wu3, Yongqiang Yu4, Fulong Dong1,2
ORCID iDs: Yongqiang Yu, Fulong Dong
  1. Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
  2. Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
  3. Department of Medical Imaging, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
  4. Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
Institutions: Anhui Medical University (China); First Affiliated Hospital of Anhui Medical University (China)
Journal: Communications medicine, volume 6, issue 1, article 469
Dates: received 13 July 2025; accepted 11 May 2026; published online 1 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s43856-026-01661-z · PMID 42225937 · PMCID PMC13526813 · OpenAlex W7163009954
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Statistics, Preprocessing, Connectivity, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Neurodegeneration, Spinal cord diseases
Topic: Cervical and Thoracic Myelopathy (Surgery, Medicine), according to OpenAlex
Funding: Anhui Medical University
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

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younghoo/brain-templates-atlases

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State: the link answers, verified on 27 September 2026
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Commit: d886cf9160f647dc51f04c4075580d7f68975ede, 16 May 2023
Languages: Shell (1)
Size: 29 files, 1 script
Software Heritage: archived
Found in: the text, “Construction of Morphometric Similarity Networks”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/younghoo/brain-templates-atlases

zhukun990427/CSM-MSN-Code

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Evidence: files inventoried
Commit: e6f623ae5546b2ba798022cf16f04b126084bbb7, 3 February 2026
Languages: Python (1), MATLAB (1)
Size: 4 files, 2 scripts
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Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/zhukun990427/CSM-MSN-Code

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Data

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Version 2, 28 September 2026

  • Funding: added Anhui Medical University

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Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 63 references.

Cite

This paper

Wu, H., Zhu, K., Wang, H., Guo, Q., Luo, Z., Bian, S., Xie, B., Ni, H., Wu, Y., Yu, Y., & Dong, F. (2026). Neural Excitation-Inhibition Imbalance in Cervical Spondylotic Myelopathy. Communications medicine, 6(1), 469. https://doi.org/10.1038/s43856-026-01661-z

BibTeX

@article{wu2026neural,
author = {Wu, Hongqing and Zhu, Kun and Wang, Haoxiang and Guo, Qiu and Luo, Zhichao and Bian, Sichen and Xie, Bingyong and Ni, Haoyu and Wu, Yuanyuan and Yu, Yongqiang and Dong, Fulong},
title = {{Neural Excitation-Inhibition Imbalance in Cervical Spondylotic Myelopathy}},
journal = {Communications medicine},
year = {2026},
month = jun,
volume = {6},
number = {1},
pages = {469},
publisher = {Nature Publishing Group},
issn = {2730-664X},
doi = {10.1038/s43856-026-01661-z},
url = {https://doi.org/10.1038/s43856-026-01661-z},
pmid = {42225937},
pmcid = {PMC13526813}
}

RIS

TY - JOUR
AU - Wu, Hongqing
AU - Zhu, Kun
AU - Wang, Haoxiang
AU - Guo, Qiu
AU - Luo, Zhichao
AU - Bian, Sichen
AU - Xie, Bingyong
AU - Ni, Haoyu
AU - Wu, Yuanyuan
AU - Yu, Yongqiang
AU - Dong, Fulong
TI - Neural Excitation-Inhibition Imbalance in Cervical Spondylotic Myelopathy
T2 - Communications medicine
J2 - Commun Med (Lond)
PY - 2026
DA - 2026/06/01
VL - 6
IS - 1
SP - 469
SN - 2730-664X
PB - Nature Publishing Group
DO - 10.1038/s43856-026-01661-z
UR - https://doi.org/10.1038/s43856-026-01661-z
LA - en
ER -

CSL-JSON

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"id": "10.1038/s43856-026-01661-z",
"type": "article-journal",
"title": "Neural Excitation-Inhibition Imbalance in Cervical Spondylotic Myelopathy",
"container-title": "Communications medicine",
"author": [
{
"family": "Wu",
"given": "Hongqing"
},
{
"family": "Zhu",
"given": "Kun"
},
{
"family": "Wang",
"given": "Haoxiang"
},
{
"family": "Guo",
"given": "Qiu"
},
{
"family": "Luo",
"given": "Zhichao"
},
{
"family": "Bian",
"given": "Sichen"
},
{
"family": "Xie",
"given": "Bingyong"
},
{
"family": "Ni",
"given": "Haoyu"
},
{
"family": "Wu",
"given": "Yuanyuan"
},
{
"family": "Yu",
"given": "Yongqiang"
},
{
"family": "Dong",
"given": "Fulong"
}
],
"container-title-short": "Commun Med (Lond)",
"volume": "6",
"issue": "1",
"page": "469",
"DOI": "10.1038/s43856-026-01661-z",
"PMID": "42225937",
"PMCID": "PMC13526813",
"ISSN": "2730-664X",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s43856-026-01661-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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