OSCR

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and "Shadow" Proteins.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § Methods › Statistical Analyses ↔ codes/04.01.new_method.py, lines 1075–1138 · score 0.83 · silhouette_score, intra cluster, CAP matrices, NetworkX, MCL, inflation
  2. [2] § Methods › The Active Place Avoidance Task ↔ codes/01.behavior_analysis.py, lines 451–471 · score 0.75 · shock zone, Conflict Yoked, Conflict Trained, Standard Yoked, Standard Trained, retested
  3. [3] § Results › Experiment 2 – Transcriptional Profiling of Memory › Shadow Proteins: Differential Gene Expression Does Not Identify PKMζ and Related Proteins in Long‐term Memory Persistence ↔ codes/02.DEG_analysis.R, lines 486–551 · score 0.72 · Camk2a, candidate genes, Fosl2, translation, Gria2, Gria1
  4. [4] § Results › Experiment 2 – Transcriptional Profiling of Memory › Shadow Proteins: Differential Gene Expression Does Not Identify PKMζ and Related Proteins in Long‐term Memory Persistence ↔ codes/04.01.new_method.py, lines 1469–1546 · score 0.70 · Camk2a, Y0, Y2, Fosl2, Gria2, Gria1
  5. [5] § Results › Experiment 2 – Transcriptional Profiling of Memory › Characterizing Memory‐related Behavior ↔ codes/01.behavior_analysis.py, lines 451–471 · score 0.68 · Time1stEntr, pTimeShockZone, NumEntrances, conflict training, behavior, treatment
  6. [6] § Results › Experiment 2 – Transcriptional Profiling of Memory › Characterizing Memory‐related Behavior ↔ codes/01.behavior_analysis.py, lines 181–210 · score 0.61 · conflict yoked, Conflict trained, standard yoked, Standard trained, Rt, retest
  7. [7] § Methods › Statistical Analyses ↔ codes/01.behavior_analysis.py, lines 498–527 · score 0.55 · way ANOVAs, conflict trained, v1, phase, treatment, retention
  8. [8] § Results › Experiment 2 – Transcriptional Profiling of Memory › Spatial Memory Drives Hippocampal‐subfield Specific Patterns of Transcriptome Variation ↔ codes/02.DEG_analysis.R, lines 486–551 · score 0.51 · candidate genes, Gria2, Grin1, synapses, Gria1, Prkcb
  9. [9] § Results › Experiment 2 – Transcriptional Profiling of Memory › Shadow Proteins: Differential Gene Expression Does Not Identify PKMζ and Related Proteins in Long‐term Memory Persistence ↔ codes/04.01.new_method.py, lines 1469–1546 · score 0.51 · graph, edges, weighted, Grin1, Gria1, Prkcb

Paper

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The authors' code

Python · 719 lines · 26 KB · no license · 4 matches

  2. import pandas as pd
  3. import numpy as np
  4. import matplotlib.pyplot as plt
  5. from matplotlib import rcParams
  7. from tqdm import tqdm
  8. import copy
  9. import seaborn as sns
  11. from sklearn import decomposition
  12. PCA = decomposition.PCA
  13. import scipy.stats as stats
  18. datapath = './data/'
  19. plotpath = './figures/'
  21. # for pdf saving, text to vector
  22. rcParams['pdf.fonttype'] = 42
  23. rcParams['ps.fonttype'] = 42 # In case of saving as eps
  26. # Arial font as default sans-serif font
  27. rcParams['font.family'] = 'sans-serif'
  28. rcParams['font.sans-serif'] = ['Arial']
  31. # facor levels
  32. levelstreatment = ['standard.yoked' , 'standard.trained' ,
  33. 'conflict.yoked' , 'conflict.trained' ]
  34. levelstreatmentlegend = ['standard yoked' , 'standard trained' ,
  35. 'conflict yoked' , 'conflict trained' ]
  36. levelstraining = ["yoked", "trained"]
  37. levelssubfield = ["DG", "CA3", "CA1"]
  40. treatment_names = dict({
  41. 'standard.yoked' : "standard yoked",
  42. 'standard.trained' :"standard trained",
  43. 'conflict.yoked' : "conflict yoked",
  44. 'conflict.trained' : "conflict trained"
  45. })
  48. treatmentcolors = dict({ "standard.yoked" : "#404040",
  49. "standard.trained" : "#ca0020",
  50. "conflict.yoked" : "#969696",
  51. "conflict.trained" : "#f4a582"})
  54. colorvalsubfield = dict({"DG" : "#d95f02",
  55. "CA3" : "#1b9e77",
  56. "CA1" : "#7570b3"})
  59. trainingcolors = dict({"trained" : "darkred",
  60. "yoked" : "black"})
  63. allcolors = copy.deepcopy(treatmentcolors)
  66. allcolors.update(colorvalsubfield)
  67. allcolors.update(trainingcolors)
  68. allcolors['NS'] = "#d9d9d9"
  70. colData = pd.read_csv(datapath+ "00_colData.csv")
  72. behavior = pd.read_csv(datapath + '00_behaviordata.csv')
  73. behavior['trial'] = ['Pre' if a == 'Hab' else a for a in list(behavior['trial'])]
  74. behavior[behavior.trial =='Pre'].groupby('treatment').count()
  76. trialnameandnumber = behavior[['trial','trialNum']].drop_duplicates()
  77. dfshocks = (
  78. behavior.groupby(['treatment', 'trial', 'trialNum'], as_index=False)
  79. .agg(
  80. m=('NumShock', lambda x: np.around(np.mean(x), 2)), # get the average
  81. se=('NumShock', lambda x: np.around(np.std(x, ddof=1) / np.sqrt(len(x)),2)) # standard deviation
  82. )
  83. .assign(measure="NumShock") # to new column
  84. )
  88. # New naming
  89. groups = ['standard.yoked', 'standard.trained', 'conflict.yoked', 'conflict.trained']
  90. colors = [treatmentcolors[a] for a in dfshocks['treatment']]
  91. dfshocks['treatment_col'] = colors
  92. reorders = ['Pre','T1','T2','T3','Retest','T4_C1','T5_C2', 'T6_C3', 'Retention']
  94. dfshocks_save = copy.deepcopy(dfshocks)
  96. dfshocks_save['treatment'] = pd.Categorical(dfshocks_save['treatment'], groups)
  97. dfshocks_save['trial'] = pd.Categorical(dfshocks_save['trial'], reorders)
  99. dfshocks_save = dfshocks_save.sort_values(['treatment','trial'])
  100. dfshocks_save = dfshocks_save.reset_index(drop = True)
  102. dfshocks_save[['treatment','trial','trialNum','m','se']].to_csv(datapath + '01.behav_table_stat.csv')
  107. # Plot for behavioral traits
  108. fig1, axes = plt.subplots(nrows=4, ncols=1, figsize=(7, 10))
  109. axes = axes.flatten()
  112. for i, treatment in enumerate(groups):
  113. subset = dfshocks[dfshocks["treatment"] == treatment]
  114. subset['trial'] = pd.Categorical(subset['trial'], categories=reorders, ordered=True)
  115. subset = subset.sort_values('trial')
  116. subset = subset.reset_index(drop=True)
  117. axes[i].plot(subset["trial"], subset["m"], label=treatment,
  118. color=treatmentcolors[treatment], linewidth=3)
  119. #for _, row in subset.iterrows():
  120. # axes[i].errorbar(row["trial"], row["m"], yerr=row["se"], fmt="none",
  121. # color=treatmentcolors[row["treatment"]], capsize=2, linewidth=0.8)
  122. for _, row in subset.iterrows():
  123. axes[i].text(row["trial"], row["m"] + 1, f"{int(np.around(row['m']))}",
  124. ha='center', va='bottom', fontsize=14, color=treatmentcolors[row["treatment"]])
  125. #
  126. axes[i].set_xticklabels(["P", "T1", "T2", "T3", "Rt", "T4", "T5", "T6", "Rn"])
  127. axes[i].set_yticks([])
  128. axes[i].set_ylim(0, max(subset.m) + 3)
  129. axes[i].set_ylabel("")
  130. axes[i].set_xlabel("")
  131. axes[i].set_title(treatment.replace('.', ' '), loc = 'left')
  132. axes[i].spines['top'].set_visible(False)
  133. axes[i].spines['right'].set_visible(False)
  134. axes[i].spines['bottom'].set_visible(False)
  136. axes[3].spines['bottom'].set_visible(True) # additional edition for the last plot
  138. fig1.suptitle("Number of Shocks", fontsize=14, x=0.02, y=0.5, rotation=90, va='center')
  140. plt.tight_layout()
  141. plt.savefig(plotpath + '01.bhv_shock.png', dpi = 300)
  142. plt.savefig(plotpath + '01.bhv_shock.pdf', dpi = 300, bbox_inches='tight')
  143. plt.savefig(plotpath + '01.bhv_shock.tiff', dpi = 300, bbox_inches='tight')
  144. plt.savefig(plotpath + '01.bhv_shock.eps', dpi = 300, bbox_inches='tight')
  145. plt.close()
  152. # Get PCA result from behavioral data
  154. behavior['train_col'] = [trainingcolors[a] for a in list(behavior['training'])]
  155. behavior['treat_col'] = [treatmentcolors[a] for a in list(behavior['treatment'])]
  157. ind_a = list(behavior.columns).index('TotalPath')
  158. ind_b = list(behavior.columns).index('ShockPerEntrance')
  160. behav_columns = list(behavior.columns)[ind_a : ind_b+1]
  162. def makepcadf (behav_table) :
  163. Z = behav_table[behav_columns]
  164. Z = Z.loc[:, Z.var(skipna=True) != 0]
  165. Z_scaled = (Z - Z.mean()) / Z.std()
  166. pca = PCA(n_components=2)
  167. principal_components = pca.fit_transform(Z_scaled)
  168. PC1_ratio = pca.explained_variance_ratio_[0]
  169. PC2_ratio = pca.explained_variance_ratio_[1]
  170. pc_df = pd.DataFrame(principal_components)
  171. pc_df.columns = ['PC1','PC2']
  172. pc_df['ID'] = list(behav_table['ID'])
  173. pc_df['treatment'] = list(behav_table['treatment'])
  174. pc_df['training'] = list(behav_table['training'])
  175. pc_df['trialNum'] = list(behav_table['trialNum'])
  176. pc_df['Day'] = list(behav_table['Day'])
  177. return pc_df, PC1_ratio, PC2_ratio
  181. pca_total = makepcadf(behavior)
  183. pca_T3_all = makepcadf(behavior[behavior.trial=='T3'])
  184. pca_T3_std = makepcadf(behavior[(behavior.trial=='T3') & (behavior.treatment.isin(['standard.yoked','standard.trained']))])
  185. pca_T3_cft = makepcadf(behavior[(behavior.trial=='T3') & (behavior.treatment.isin(['conflict.yoked','conflict.trained']))])
  187. pca_Rt_all = makepcadf(behavior[behavior.trial=='Retest'])
  188. pca_Rt_std = makepcadf(behavior[(behavior.trial=='Retest') & (behavior.treatment.isin(['standard.yoked','standard.trained']))])
  189. pca_Rt_cft = makepcadf(behavior[(behavior.trial=='Retest') & (behavior.treatment.isin(['conflict.yoked','conflict.trained']))])
  191. pca_T3Rt_all = makepcadf(behavior[behavior.trial.isin(['T3','Retest'])])
  192. pca_T3Rt_std = makepcadf(behavior[(behavior.trial.isin(['T3','Retest'])) & (behavior.treatment.isin(['standard.yoked','standard.trained']))])
  193. pca_T3Rt_cft = makepcadf(behavior[(behavior.trial.isin(['T3','Retest'])) & (behavior.treatment.isin(['conflict.yoked','conflict.trained']))])
  195. pca_T6_all = makepcadf(behavior[behavior.trial=='T6_C3'])
  196. pca_T6_std = makepcadf(behavior[(behavior.trial=='T6_C3') & (behavior.treatment.isin(['standard.yoked','standard.trained']))])
  197. pca_T6_cft = makepcadf(behavior[(behavior.trial=='T6_C3') & (behavior.treatment.isin(['conflict.yoked','conflict.trained']))])
  199. pca_Rn_all = makepcadf(behavior[behavior.trial=='Retention'])
  200. pca_Rn_std = makepcadf(behavior[(behavior.trial=='Retention') & (behavior.treatment.isin(['standard.yoked','standard.trained']))])
  201. pca_Rn_cft = makepcadf(behavior[(behavior.trial=='Retention') & (behavior.treatment.isin(['conflict.yoked','conflict.trained']))])
  203. pca_T6Rn_all = makepcadf(behavior[behavior.trial.isin(['T6_C3','Retention'])])
  204. pca_T6Rn_std = makepcadf(behavior[(behavior.trial.isin(['T6_C3','Retention'])) & (behavior.treatment.isin(['standard.yoked','standard.trained']))])
  205. pca_T6Rn_cft = makepcadf(behavior[(behavior.trial.isin(['T6_C3','Retention'])) & (behavior.treatment.isin(['conflict.yoked','conflict.trained']))])
  208. this_pca_list = [pca_T3_all ,pca_T3_std ,pca_T3_cft ,pca_Rt_all ,pca_Rt_std ,pca_Rt_cft ,pca_T3Rt_all ,pca_T3Rt_std ,pca_T3Rt_cft ,pca_T6_all ,pca_T6_std ,pca_T6_cft ,pca_Rn_all ,pca_Rn_std ,pca_Rn_cft ,pca_T6Rn_all ,pca_T6Rn_std ,pca_T6Rn_cft ]
  209. title_list = ["T3.all","T3.std","T3.cft","Rt.all","Rt.std","Rt.cft","T3Rt.all","T3Rt.std","T3Rt.cft","T6.all","T6.std","T6.cft","Rn.all","Rn.std","Rn.cft","T6Rn.all","T6Rn.std","T6Rn.cft"]
  210. index_num = sum([[b+6*a for a in range(3)] for b in range(6)], [])
  214. # Original total used pc
  215. fig1, axes = plt.subplots(nrows=1, ncols=1, figsize=(3, 3))
  217. my_pca, pc1_perc, pc2_perc = pca_total
  219. sns.scatterplot(x='PC1', y='PC2', data=my_pca, ax = axes,
  220. hue = 'treatment', palette = treatmentcolors,
  221. s = 30, legend = False)
  222. axes.set_ylabel('PC2 : {}%'.format(round(pc2_perc*100, 1)))
  223. axes.set_xlabel('PC1 : {}%'.format(round(pc1_perc*100, 1)))
  224. axes.set_xticklabels('')
  225. axes.set_yticklabels('')
  227. plt.tight_layout()
  228. plt.savefig(plotpath+'01.PC_original.png', dpi = 300)
  229. plt.savefig(plotpath+'01.PC_original.pdf', dpi = 300)
  230. plt.savefig(plotpath+'01.PC_original.tiff', dpi = 300)
  231. plt.savefig(plotpath+'01.PC_original.eps', dpi = 300)
  232. plt.close()
  236. # Get total PC figures
  237. fig1, axes = plt.subplots(nrows=3, ncols=6, figsize=(18, 9))
  238. axes = axes.flatten()
  240. for i in range(18):
  241. my_pca, pc1_perc, pc2_perc = this_pca_list[i]
  242. my_title = title_list[i]
  243. index = index_num[i]
  244. sns.scatterplot(x='PC1', y='PC2', data=my_pca, ax = axes[index],
  245. hue = 'treatment', palette = treatmentcolors,
  246. size = 30, legend = False)
  247. axes[index].set_ylabel('PC2 : {}%'.format(round(pc2_perc*100, 1)))
  248. axes[index].set_xlabel('PC1 : {}%'.format(round(pc1_perc*100, 1)))
  249. axes[index].set_title(my_title, loc = 'left')
  250. axes[index].set_xticklabels('')
  251. axes[index].set_yticklabels('')
  253. plt.tight_layout()
  254. plt.savefig(plotpath+'01.PC_series.png', dpi = 300)
  255. plt.savefig(plotpath+'01.PC_series.pdf', dpi = 300)
  256. plt.savefig(plotpath+'01.PC_series.tiff', dpi = 300)
  257. plt.savefig(plotpath+'01.PC_series.eps', dpi = 300)
  259. plt.close()
  266. ##### Use the best one which covers the most #####
  268. pca_Rn_behav = behavior[behavior.trial=='Retention']
  269. Z = pca_Rn_behav[behav_columns]
  270. Z = Z.loc[:, Z.var(skipna=True) != 0]
  271. Z_scaled = (Z - Z.mean()) / Z.std()
  272. pca = PCA(n_components=2)
  273. principal_components = pca.fit_transform(Z_scaled)
  274. PC1_ratio = pca.explained_variance_ratio_[0]
  275. PC2_ratio = pca.explained_variance_ratio_[1]
  276. pc_df = pd.DataFrame(principal_components)
  277. pc_df.columns = ['PC1','PC2']
  278. pc_df['ID'] = list(pca_Rn_behav['ID'])
  279. pc_df['treatment'] = list(pca_Rn_behav['treatment'])
  280. pc_df['training'] = list(pca_Rn_behav['training'])
  281. pc_df['trialNum'] = list(pca_Rn_behav['trialNum'])
  282. pc_df['Day'] = list(pca_Rn_behav['Day'])
  284. pc1_res = pca.components_[0]
  285. pc2_res = pca.components_[1]
  286. contribution_percentage_pc1 = (pc1_res**2) / np.sum(pc1_res**2) * 100
  287. contribution_percentage_pc2 = (pc2_res**2) / np.sum(pc2_res**2) * 100
  289. PC1_feature_perc = pd.DataFrame({
  290. 'behav' : list(Z.columns),
  291. 'percent' : contribution_percentage_pc1 })
  293. PC1_feature_perc = PC1_feature_perc.sort_values('percent', ascending = False)
  296. PC2_feature_perc = pd.DataFrame({
  297. 'behav' : list(Z.columns),
  298. 'percent' : contribution_percentage_pc2 })
  300. PC2_feature_perc = PC2_feature_perc.sort_values('percent', ascending = False)
  302. # check t-test within PC
  303. PC1_t = stats.ttest_ind(pc_df[pc_df.training=='trained']['PC1'] , pc_df[pc_df.training=='yoked']['PC1'])
  304. PC2_t = stats.ttest_ind(pc_df[pc_df.training=='trained']['PC2'] , pc_df[pc_df.training=='yoked']['PC2'])
  305. print(np.round(PC1_t, 4))
  306. print(np.round(PC2_t, 4))
  310. # Make figure
  311. def plot_behavior_feature(ax, titi, varname, namestr, ylims):
  312. df = (behavior.groupby(['treatment', 'trial', 'trialNum'])
  313. .agg(
  314. m=(varname, 'mean'),
  315. se=(varname, lambda x: np.std(x, ddof=1) / np.sqrt(len(x))) # 표준 오차
  316. )
  317. .reset_index())
  318. df['measure'] = varname
  319. #print(dfa.describe())
  320. for treatment, group in df.groupby('treatment'):
  321. color = treatmentcolors[treatment] # 색상 가져오기
  322. ax.errorbar(group['trialNum'], group['m'], yerr=group['se'], fmt='o',
  323. label=treatment, capsize=3, markersize=4, color=color) # color 매개변수 추가
  324. sns.lineplot(data=group, x='trialNum', y='m', ax=ax, color=color, legend=None)
  325. sns.scatterplot(data=group, x='trialNum', y='m', ax=ax, s=30, color=color, legend=None)
  326. #
  327. ax.set_ylabel(namestr, fontsize = 8)
  328. ax.set_xlabel("")
  329. ax.set_xticks(list(range(1,10)))
  330. ax.set_xticklabels(["P", "T1", "T2", "T3", "Rt", "T4", "T5", "T6", "Rn"], fontsize = 8)
  331. ax.set_yticks(ax.get_yticks())
  332. ax.set_yticklabels(ax.get_yticklabels(), fontsize = 8)
  333. ax.set_ylim(ylims)
  334. ax.set_title(titi, fontsize= 12, loc = 'left')
  335. ax.spines['top'].set_visible(False)
  336. ax.spines['right'].set_visible(False)
  337. #return ax, df
  341. fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(7, 5))
  343. plot_behavior_feature(axes[0][0], 'a', 'NumEntrances', 'NumEntrances' , [0, 35])
  344. plot_behavior_feature(axes[0][1], 'b','Time1stEntr', 'Time1stEntr (min)' , [0, 400])
  345. plot_behavior_feature(axes[0][2], 'c','pTimeShockZone', 'pTimeShockZone' , [0, 0.35])
  347. pc_df['treat_col'] = [treatmentcolors[a] for a in list(pc_df.treatment)]
  348. sns.scatterplot(
  349. x='PC1', y='PC2', data=pc_df,
  350. ax=axes[1][0], hue='treatment',
  351. palette=treatmentcolors,
  352. s=80 , alpha = 1, legend = False )
  354. axes[1][0].set_ylabel('PC2 : {}% variance explained'.format(round(PC2_ratio*100, 1)), fontsize= 8)
  355. axes[1][0].set_xlabel('PC1 : {}% variance explained'.format(round(PC1_ratio*100, 1)), fontsize= 8)
  356. axes[1][0].set_xticklabels('')
  357. axes[1][0].set_yticklabels('')
  358. axes[1][0].set_title('d', fontsize = 12, loc = 'left')
  361. sns.barplot(
  362. y = 'percent', x = 'behav',
  363. data = PC1_feature_perc.iloc[0:8],
  364. ax = axes[1][1]
  365. )
  367. axes[1][1].set_xlabel('Estimates of memory', fontsize= 8)
  368. axes[1][1].set_ylabel('PC1 % contrib.', fontsize= 8)
  369. axes[1][1].set_xticks(list(range(8)))
  370. axes[1][1].set_yticks([0,2,4,6,8])
  371. axes[1][1].set_xticklabels(axes[1][1].get_xticklabels(), ha='right', fontsize = 8, rotation = 45)
  372. axes[1][1].set_yticklabels(axes[1][1].get_yticklabels(),fontsize = 8)
  373. axes[1][1].set_title('e', fontsize = 12, loc = 'left')
  375. for spine in axes[1][1].spines.values():
  376. spine.set_visible(False)
  379. sns.barplot(
  380. y = 'percent', x = 'behav',
  381. data = PC2_feature_perc.iloc[0:8],
  382. ax = axes[1][2]
  383. )
  385. axes[1][2].set_xlabel('Estimates of activity' , fontsize= 8)
  386. axes[1][2].set_ylabel('PC2 % contrib.', fontsize= 8)
  387. axes[1][2].set_xticks(list(range(8)))
  388. axes[1][2].set_yticks([0,5,10,15,20])
  389. axes[1][2].set_xticklabels(axes[1][2].get_xticklabels(), ha='right', fontsize = 8, rotation = 45)
  390. axes[1][2].set_yticklabels(axes[1][2].get_yticklabels(),fontsize = 8)
  391. axes[1][2].set_title('f', fontsize = 12, loc = 'left')
  393. for spine in axes[1][2].spines.values():
  394. spine.set_visible(False)
  396. plt.tight_layout()
  397. plt.savefig(plotpath + '01.behav_bar.pdf', dpi = 300)
  398. plt.savefig(plotpath + '01.behav_bar.png', dpi = 300)
  399. plt.savefig(plotpath + '01.behav_bar.tiff', dpi = 300)
  400. plt.savefig(plotpath + '01.behav_bar.eps', dpi = 300) # alpha problem
  402. plt.close()
  409. # Now all the stats for supplementary materials
  410. #-----------------
  412. # Maybe it should be done like this:
  413. # Q1: are the groups different? 1-way ANOVA of groups on Pre
  414. # Q2: are the groups different during initial training T1-T3? 2-way ANOVA of groups X trial
  415. # Q3: do the groups differ in initial recall? 1-way ANOVA of groups on Rt
  416. # Q4: Do the groups differ in subsequent training? T4-T6 2-way ANOVA of groups X trial
  417. # Q5: do the groups differ in subsequent recall? 1-way ANOVA of groups on Rn
  420. import pandas as pd
  421. import statsmodels.api as sm
  422. from scipy import stats
  423. from statsmodels.formula.api import ols
  426. def run_anova_v1(be_for_ano, trial_filter, formulas):
  427. results = []
  428. trial_phase = be_for_ano[be_for_ano['trial'].isin(trial_filter)]
  429. trial_phase['treatment'] = pd.Categorical(trial_phase['treatment'])
  430. trial_phase['trial'] = pd.Categorical(trial_phase['trial'], categories = trial_filter)
  431. for name, formula in formulas.items():
  432. model = ols(formula, data=trial_phase).fit()
  433. anova_result = sm.stats.anova_lm(model, typ=2)
  434. anova_result['name'] = name
  435. results.append(anova_result)
  436. combined_result = pd.concat(results)
  437. combined_result['trial'] = '_'.join(trial_filter)
  438. print(combined_result)
  439. return combined_result.drop('Residual', axis=0)
  442. # Format and annotate tables
  443. def format_anova_table(table):
  444. table['p'] = round(table['PR(>F)'], 3)
  445. table['F'] = round(table['F'], 2)
  446. table['df'] = table['df'].astype(int)
  447. table['sig'] = ['***' if p < 0.001 else '**' if p < 0.01 else '*' if p < 0.05 else ' ' for p in table['p']]
  448. return table
  451. # Define the models and phases
  452. formulas_single = {
  453. 'NumEntrances ~ treatment': 'NumEntrances ~ C(treatment)',
  454. 'pTimeShockZone ~ treatment': 'pTimeShockZone ~ C(treatment)',
  455. 'Time1stEntr ~ treatment': 'Time1stEntr ~ C(treatment)'
  456. }
  458. formulas_double = {
  459. 'NumEntrances': 'NumEntrances ~ C(trial) + C(treatment) + C(trial):C(treatment)',
  460. 'pTimeShockZone': 'pTimeShockZone ~ C(trial) + C(treatment) + C(trial):C(treatment)',
  461. 'Time1stEntr': 'Time1stEntr ~ C(trial) + C(treatment) + C(trial):C(treatment)'
  462. }
  464. phases_single = ['Pre', 'Retention', 'Retest']
  465. phase_groups = [['T1', 'T2', 'T3'], ['T4_C1', 'T5_C2', 'T6_C3']]
  468. yoked_for_ano = copy.deepcopy(behavior)
  469. trained_for_ano = copy.deepcopy(behavior)
  470. yoked_for_ano = yoked_for_ano[yoked_for_ano.treatment.isin(['conflict.yoked','standard.yoked'])]
  471. trained_for_ano = trained_for_ano[trained_for_ano.treatment.isin(['conflict.trained','standard.trained'])]
  475. # Run single ANOVA - Yoked & Trained
  477. Yoked_results_single = []
  478. for phase in phases_single:
  479. result = run_anova_v1(yoked_for_ano, [phase], formulas_single)
  480. Yoked_results_single.append(result.loc['C(treatment)'])
  482. Yoked_table_1 = pd.concat(Yoked_results_single)
  483. Yoked_table_1['trial'] = ['Pre-training'] * 3 + ['Retention'] * 3 + ['Retest'] * 3
  486. Trained_results_single = []
  487. for phase in phases_single:
  488. result = run_anova_v1(trained_for_ano, [phase], formulas_single)
  489. Trained_results_single.append(result.loc['C(treatment)'])
  491. Trained_table_1 = pd.concat(Trained_results_single)
  492. Trained_table_1['trial'] = ['Pre-training'] * 3 + ['Retention'] * 3 + ['Retest'] * 3
  498. # Run two-way ANOVA - Yoked & Trained
  499. desired_order = ['Pre-training', 'Initial Training (T1~T3)', 'Retest', 'Conflict Training (T4~T6)', 'Retention', 'All trials', 'Retention only']
  502. Yoked_table_2 = pd.concat([run_anova_v1(yoked_for_ano, phase, formulas_double) for phase in phase_groups])
  503. Yoked_table_2['trial'] = ['Initial Training (T1~T3)'] * 9 + ['Conflict Training (T4~T6)'] * 9
  505. Yoked_table_1 = format_anova_table(Yoked_table_1)[['trial', 'name', 'df', 'F', 'p', 'sig']]
  506. Yoked_table_2['name2'] = ['trial', 'treatment', 'trial*treatment'] * 6
  507. Yoked_table_2['name'] = Yoked_table_2['name'] + ' ~ ' + Yoked_table_2['name2']
  508. Yoked_table_2 = format_anova_table(Yoked_table_2)[['trial', 'name', 'df', 'F', 'p', 'sig']]
  510. Yoked_table_3 = pd.concat([Yoked_table_1, Yoked_table_2]).reset_index(drop=True)
  511. Yoked_table_3['trial'] = pd.Categorical(Yoked_table_3['trial'], categories=desired_order)
  512. Yoked_table_3 = Yoked_table_3.sort_values(['trial','name'])
  513. Yoked_table_3.to_csv(datapath + '01.behav_anova_onlyYoked.csv')
  516. Trained_table_2 = pd.concat([run_anova_v1(trained_for_ano, phase, formulas_double) for phase in phase_groups])
  517. Trained_table_2['trial'] = ['Initial Training (T1~T3)'] * 9 + ['Conflict Training (T4~T6)'] * 9
  519. Trained_table_1 = format_anova_table(Trained_table_1)[['trial', 'name', 'df', 'F', 'p', 'sig']]
  520. Trained_table_2['name2'] = ['trial', 'treatment', 'trial*treatment'] * 6
  521. Trained_table_2['name'] = Trained_table_2['name'] + ' ~ ' + Trained_table_2['name2']
  522. Trained_table_2 = format_anova_table(Trained_table_2)[['trial', 'name', 'df', 'F', 'p', 'sig']]
  524. Trained_table_3 = pd.concat([Trained_table_1, Trained_table_2]).reset_index(drop=True)
  525. Trained_table_3['trial'] = pd.Categorical(Trained_table_3['trial'], categories=desired_order)
  526. Trained_table_3 = Trained_table_3.sort_values(['trial','name'])
  527. Trained_table_3.to_csv(datapath + '01.behav_anova_onlyTrain.csv')
  534. # Run ANOVA for all PCs
  535. def run_anova_pcv1(be_for_ano, formulas):
  536. results = []
  537. tmp_ano = copy.deepcopy(be_for_ano)
  538. tmp_ano['treatment'] = pd.Categorical(tmp_ano['treatment'])
  539. for name, formula in formulas.items():
  540. model = ols(formula, data=tmp_ano).fit()
  541. anova_result = sm.stats.anova_lm(model, typ=2)
  542. # typ1 : order matters, typ2 : take only Main effect, typ3 : also check interaction among conditions
  543. anova_result['name'] = name
  544. results.append(anova_result)
  545. combined_result = pd.concat(results)
  546. print(combined_result)
  547. return combined_result.drop('Residual', axis=0)
  551. yoked_only_pca_tot = pca_total[0][pca_total[0].treatment.isin(['conflict.yoked','standard.yoked'])]
  552. trained_only_pca_tot = pca_total[0][pca_total[0].treatment.isin(['conflict.trained','standard.trained'])]
  553. yoked_only_pca_rn = pca_Rn_all[0][pca_Rn_all[0].treatment.isin(['conflict.yoked','standard.yoked'])]
  554. trained_only_pca_rn = pca_Rn_all[0][pca_Rn_all[0].treatment.isin(['conflict.trained','standard.trained'])]
  556. pc_formulas = {'PC1 ~ treatment': 'PC1 ~ C(treatment)', 'PC2 ~ treatment': 'PC2 ~ C(treatment)'}
  558. pc_tots = [yoked_only_pca_tot, trained_only_pca_tot, yoked_only_pca_rn, trained_only_pca_rn]
  559. pc_names = ['All trials - yoked', 'All trials - trained', 'Retention only - yoked', 'Retention only - trained']
  560. pc_res = []
  562. for i in range(4) :
  563. pcto = pc_tots[i]
  564. title = pc_names[i]
  565. pcs = run_anova_pcv1(pcto, pc_formulas).loc['C(treatment)']
  566. pcs['trial'] = title
  567. pcs['name'] = ['PC1 ~ treatment', 'PC2 ~ treatment']
  568. pc_res.append(pcs)
  571. table_pcs = format_anova_table(pd.concat(pc_res))
  572. table_pcs2 = table_pcs[['trial', 'name', 'df', 'F', 'p', 'sig']].reset_index(drop=True)
  574. table_pcs2.to_csv(datapath+'01.PC1_anova.csv')
  587. # Table 3
  588. # Yoked vs trained
  590. def run_anova_v2(be_for_ano, trial_filter, formulas):
  591. results = []
  592. trial_phase = be_for_ano[be_for_ano['trial'].isin(trial_filter)]
  593. trial_phase['training'] = pd.Categorical(trial_phase['training'])
  594. trial_phase['trial'] = pd.Categorical(trial_phase['trial'], categories = trial_filter)
  595. for name, formula in formulas.items():
  596. model = ols(formula, data=trial_phase).fit()
  597. anova_result = sm.stats.anova_lm(model, typ=2)
  598. anova_result['name'] = name
  599. results.append(anova_result)
  600. combined_result = pd.concat(results)
  601. combined_result['trial'] = '_'.join(trial_filter)
  602. print(combined_result)
  603. return combined_result.drop('Residual', axis=0)
  606. # Format and annotate tables
  607. def format_anova_table(table):
  608. table['p'] = round(table['PR(>F)'], 3)
  609. table['F'] = round(table['F'], 2)
  610. table['df'] = table['df'].astype(int)
  611. table['sig'] = ['***' if p < 0.001 else '**' if p < 0.01 else '*' if p < 0.05 else ' ' for p in table['p']]
  612. return table
  615. # Define the models and phases
  616. formulas_single = {
  617. 'NumEntrances ~ training': 'NumEntrances ~ C(training)',
  618. 'pTimeShockZone ~ training': 'pTimeShockZone ~ C(training)',
  619. 'Time1stEntr ~ training': 'Time1stEntr ~ C(training)'
  620. }
  622. formulas_double = {
  623. 'NumEntrances': 'NumEntrances ~ C(trial) + C(training) + C(trial):C(training)',
  624. 'pTimeShockZone': 'pTimeShockZone ~ C(trial) + C(training) + C(trial):C(training)',
  625. 'Time1stEntr': 'Time1stEntr ~ C(trial) + C(training) + C(trial):C(training)'
  626. }
  628. phases_single = ['Pre', 'Retention', 'Retest']
  629. phase_groups = [['T1', 'T2', 'T3'], ['T4_C1', 'T5_C2', 'T6_C3']]
  632. tot_for_ano = copy.deepcopy(behavior)
  636. # Run single ANOVA - Yoked & Trained
  638. Tot_results_single = []
  639. for phase in phases_single:
  640. result = run_anova_v2(tot_for_ano, [phase], formulas_single)
  641. Tot_results_single.append(result.loc['C(training)'])
  643. Tot_table_1 = pd.concat(Tot_results_single)
  644. Tot_table_1['trial'] = ['Pre-training'] * 3 + ['Retention'] * 3 + ['Retest'] * 3
  647. # Run two-way ANOVA - Yoked & Trained
  648. desired_order = ['Pre-training', 'Initial Training (T1~T3)', 'Retest', 'Conflict Training (T4~T6)', 'Retention', 'All trials', 'Retention only']
  651. Tot_table_2 = pd.concat([run_anova_v2(tot_for_ano, phase, formulas_double) for phase in phase_groups])
  652. Tot_table_2['trial'] = ['Initial Training (T1~T3)'] * 9 + ['Conflict Training (T4~T6)'] * 9
  654. Tot_table_1 = format_anova_table(Tot_table_1)[['trial', 'name', 'df', 'F', 'p', 'sig']]
  655. Tot_table_2['name2'] = ['trial', 'train', 'trial*train'] * 6
  656. Tot_table_2['name'] = Tot_table_2['name'] + ' ~ ' + Tot_table_2['name2']
  657. Tot_table_2 = format_anova_table(Tot_table_2)[['trial', 'name', 'df', 'F', 'p', 'sig']]
  659. Tot_table_3 = pd.concat([Tot_table_1, Tot_table_2]).reset_index(drop=True)
  660. Tot_table_3['trial'] = pd.Categorical(Tot_table_3['trial'], categories=desired_order)
  661. Tot_table_3 = Tot_table_3.sort_values(['trial','name'])
  662. Tot_table_3.to_csv(datapath + '01.behav_anova_YKvsTR.csv')
  666. # Run ANOVA for all PCs
  667. def run_anova_pcv2(be_for_ano, formulas):
  668. results = []
  669. tmp_ano = copy.deepcopy(be_for_ano)
  670. tmp_ano['training'] = pd.Categorical(tmp_ano['training'])
  671. for name, formula in formulas.items():
  672. model = ols(formula, data=tmp_ano).fit()
  673. anova_result = sm.stats.anova_lm(model, typ=2)
  674. # typ1 : order matters, typ2 : take only Main effect, typ3 : also check interaction among conditions
  675. anova_result['name'] = name
  676. results.append(anova_result)
  677. combined_result = pd.concat(results)
  678. print(combined_result)
  679. return combined_result.drop('Residual', axis=0)
  682. TOT_pca_tot = pca_total[0]
  683. TOT_pca_rn = pca_Rn_all[0]
  685. pc_formulas = {'PC1 ~ training': 'PC1 ~ C(training)', 'PC2 ~ training': 'PC2 ~ C(training)'}
  687. pc_tots = [TOT_pca_tot, TOT_pca_rn]
  688. pc_names = ['All trials','Retention only']
  689. pc_res = []
  691. for i in range(2) :
  692. pcto = pc_tots[i]
  693. title = pc_names[i]
  694. pcs = run_anova_pcv2(pcto, pc_formulas).loc['C(training)']
  695. pcs['trial'] = title
  696. pcs['name'] = ['PC1 ~ training', 'PC2 ~ training']
  697. pc_res.append(pcs)
  700. table_pcs = format_anova_table(pd.concat(pc_res))
  701. table_pcs2 = table_pcs[['trial', 'name', 'df', 'F', 'p', 'sig']].reset_index(drop=True)
  703. table_pcs2.to_csv(datapath+'01.PC1_anova_YKTR.csv')
  707. # Just to save and fix the PC1
  709. NEW_PC1 = pca_Rn_all[0]
  710. NEW_PC1 = NEW_PC1.sort_values('PC1')[['ID','treatment','trialNum','PC1','PC2']]
  711. NEW_PC1 = NEW_PC1.reset_index(drop = True)
  713. NEW_PC1.to_csv(datapath+'00.NEW_PC1.csv') # for the reference

01.behavior_analysis.py at commit a6bee7c, no license · at the source

Overview

Authors: Jiyeon Han1,2, Alejandro Grau‐Perales1, Rayna M Harris3, Edith Lesburguères1, Hsin‐Yi Kao1, Asit Pal2,4, Juan Marcos Alarcon5, Todd C Sacktor6, Stefano Martiniani1,2,4,7, Hans A Hofmann3, André A Fenton1,8
ORCID iDs: Jiyeon Han, Stefano Martiniani, Hans A Hofmann, André A Fenton
  1. Center for Neural Science, New York University, New York, New York, USA
  2. Center for Soft Matter Physics, Department of Physics, New York University, New York, New York, USA
  3. Department of Integrative Biology and Institute for Neuroscience, The University of Texas. Austin, Austin, Texas, USA
  4. Simons Center For Computational Physical Chemistry, Department of Chemistry, New York University, New York, New York, USA
  5. Department of Pathology. State University of New York, Downstate Health Sciences University, Brooklyn, New York, USA
  6. Departments of Physiology and Pharmacology, Anesthesiology, and Neurology, State University of New York, Downstate Health Sciences University, Brooklyn, New York, USA
  7. Courant Institute of Mathematical Sciences, New York University, New York, New York, USA
  8. Neuroscience Institute at the New York University Langone Medical Center, New York, New York, USA
Institutions: New York University (United States); The University of Texas at Austin (United States); SUNY Downstate Health Sciences University (United States); Courant Institute of Mathematical Sciences (United States); NYU Langone Health (United States)
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), volume 13, issue 28, article e21254
Dates: received 24 October 2025; accepted 23 February 2026; published online 13 March 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/advs.202521254 · PMID 41824523 · PMCID PMC13185853 · OpenAlex W7135220679
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), rat (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging
Keywords: engram, gene co‐expression networks, immunohistochemistry, transcriptome, trisynaptic pathway
MeSH: Avoidance Learning*, Hippocampus*, Memory, Long-Term*, Protein Kinase C*, Animals, Gene Expression Profiling, Protein Kinase C zeta, Rats, Transcriptome (* major topic)
Topic: Hippo pathway signaling and YAP/TAZ (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Chan Zuckerberg Initiative (CP2-1-0000000614, CP2‐1‐0000000614); NIMH NIH HHS (R01 MH132204, R01 MH115304); National Institutes of Health (R01MH115304, R01MH132204); NIH HHS (R01MH132204, R01MH115304); Simons Foundation (839534); UT Austin Continuing Fellowship to RMH
Citations: cited by 4 papers (Europe PMC); 183 references in the paper

Abstract

Long‐term memory formation transiently activates Ca2+‐calmodulin kinase II and atypical protein kinase C isoform iota/lambda, whereas persistent activation of the other atypical PKC, protein kinase M zeta (PKMζ), together with its interacting partner, the scaffolding‐protein KIBRA (Wwc1), are necessary for maintaining potentiated synapses and memory. Here, we use immediate‐early gene (IEG) Arc activation during active place avoidance memory expression to tag memory‐activated neurons with EYFP‐ChR2. PKMζ immunohistochemistry identified persistently altered hippocampal somatodendritic domains. EYFP‐PKMζ colocalization persistently increases in the hippocampal trisynaptic pathway (dentate gyrus [DG]→CA3→CA1) tracing a 1‐month PKMζ engram. DG, CA3, and CA1 transcriptional profiling identifies that memory persistence correlates with upregulated IEGs Arc, Fos, and NPas4 in DG, but not with Prkcz, the PKMζ gene, or most genes known to be crucial for LTP and memory. This rules out strong memory‐related transcriptional but not translational regulation or altered stability of such “shadow proteins” like PKMζ that, despite being crucial for memory maintenance, evade detection by unbiased transcriptome profiling. In contrast, our method Correlation Signal Co‐cluster Reduction (C‐SCoRe) incorporates weak linear and non‐linear gene correlations and highlights network interaction changes predicting memory, and related IEG and Prkcz/Wwc1 expression. Manifold transcriptional relationships can reveal shadow molecular components of long‐term memory.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

sommerJY/APA_PKMZ

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a6bee7c09597cae106cd652954945bf64bce55e2, 24 October 2025
Languages: Python (6), R (2)
Size: 121 files, 8 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (6 files), NumPy (6 files), pandas (6 files), SciPy (6 files), seaborn (5 files), scikit-learn (4 files), ggplot2 (2 files), NetworkX (2 files), statsmodels (2 files), cowplot (1 file), DESeq2 (1 file), Plotly (1 file), tidyverse (1 file), UMAP (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
9 files
At the source: github.com/sommerJY/APA_PKMZ

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

Raw sequence data and differential gene expression data are available in NCBI's Gene Expression Omnibus Database (accession: GSE100225 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100225)). All data, code, and results are publicly available on GitHub (https://github.com/sommerJY/APA_PKMZ).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 9 MeSH terms, 6 funders, 171 references.

Cite

This paper

Han, J., Grau‐Perales, A., Harris, R. M., Lesburguères, E., Kao, H., Pal, A., Alarcon, J. M., Sacktor, T. C., Martiniani, S., Hofmann, H. A., & Fenton, A. A. (2026). Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and "Shadow" Proteins. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 13(28), e21254. https://doi.org/10.1002/advs.202521254

BibTeX

@article{han2026persistently,
author = {Han, Jiyeon and Grau‐Perales, Alejandro and Harris, Rayna M and Lesburguères, Edith and Kao, Hsin‐Yi and Pal, Asit and Alarcon, Juan Marcos and Sacktor, Todd C and Martiniani, Stefano and Hofmann, Hans A and Fenton, André A},
title = {{Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and "Shadow" Proteins}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = mar,
volume = {13},
number = {28},
pages = {e21254},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/advs.202521254},
url = {https://doi.org/10.1002/advs.202521254},
pmid = {41824523},
pmcid = {PMC13185853}
}

RIS

TY - JOUR
AU - Han, Jiyeon
AU - Grau‐Perales, Alejandro
AU - Harris, Rayna M
AU - Lesburguères, Edith
AU - Kao, Hsin‐Yi
AU - Pal, Asit
AU - Alarcon, Juan Marcos
AU - Sacktor, Todd C
AU - Martiniani, Stefano
AU - Hofmann, Hans A
AU - Fenton, André A
TI - Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and "Shadow" Proteins
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/03/13
VL - 13
IS - 28
SP - e21254
SN - 2198-3844
PB - Wiley
DO - 10.1002/advs.202521254
UR - https://doi.org/10.1002/advs.202521254
LA - en
ER -

CSL-JSON

{
"id": "10.1002/advs.202521254",
"type": "article-journal",
"title": "Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and \"Shadow\" Proteins",
"container-title": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
"author": [
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"family": "Han",
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"family": "Grau‐Perales",
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{
"family": "Harris",
"given": "Rayna M"
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{
"family": "Lesburguères",
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{
"family": "Kao",
"given": "Hsin‐Yi"
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{
"family": "Pal",
"given": "Asit"
},
{
"family": "Alarcon",
"given": "Juan Marcos"
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"issued": {
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}

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[4] doi:10.1038/s41467-026-76675-1 [code]
Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: WGCNA, UMAP, DESeq2, 10 other tools, genetics / omics, 1 reference
[5] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: UMAP, DESeq2, NetworkX, 10 other tools, genetics / omics, cellular / molecular, 1 reference
[6] doi:10.1038/s41467-026-72598-z [code]
Functional impact of genetic background on variable expressivity in neurodevelopmental disorders.
Journal: Nature communications
In common: WGCNA, DESeq2, NetworkX, 8 other tools, 3 references
[7] doi:10.1016/j.xcrm.2026.102651 [code]
Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.
Journal: Cell reports. Medicine
In common: UMAP, DESeq2, NetworkX, 9 other tools, genetics / omics, cellular / molecular
[8] doi:10.1016/j.xcrm.2026.102800 [code]
Recombinant dimeric PICK1 peptide inhibitors for long-term relief of chronic pain by AAV therapeutics.
Journal: Cell reports. Medicine
In common: WGCNA, UMAP, DESeq2, 9 other tools
[9] doi:10.1126/sciadv.aeg3223 [code]
The extreme diversity of retinal amacrine cells has deep evolutionary roots.
Journal: Science advances
In common: WGCNA, DESeq2, Plotly, 9 other tools, genetics / omics, cellular / molecular
[10] doi:10.7554/elife.93664 [code]
Drug-induced changes in connectivity to midbrain dopamine cells revealed by rabies monosynaptic tracing.
Journal: eLife
In common: UMAP, cowplot, statsmodels, 8 other tools, cellular / molecular, 2 references

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