OSCR

Drug-induced changes in connectivity to midbrain dopamine cells revealed by rabies monosynaptic tracing.

Code ↔ Paper

11 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 11 matches
  1. [1] § Materials and methods › Dimensionality reduction of RABV input data ↔ 10-gene-expression-analysis-cocaine.ipynb, lines 375–384 · score 0.74 · anterior cortex, DStr, LDT, PVH, LHb, VP
  2. [2] § Materials and methods › Experimental procedures › Stereotaxic surgery ↔ 2-DAT-Cre vs GAD2-Cre and vGluT2-Cre-euclidean.ipynb, lines 263–277 · score 0.71 · vGluT2, GAD2 Cre, DAT Cre, mPFC, cTRIO, Amygdala
  3. [3] § Materials and methods › Experimental procedures › Stereotaxic surgery ↔ 0-data-exploration-master-euclidean.ipynb, lines 2482–2537 · score 0.62 · vGluT2, mPFC, cTRIO, GAD2 Cre, DAT Cre, NAcLat
  4. [4] § Results › A single injection of an addictive drug changes brain-wide input patterns to VTADA cells ↔ 2-DAT-Cre vs GAD2-Cre and vGluT2-Cre-euclidean.ipynb, lines 263–277 · score 0.61 · vGluT2, GAD2 Cre, DAT Cre, isoflurane anesthetized, cocaine, cell
  5. [5] § Results › Exploring gene expression patterns that predict changes in RABV input labeling ↔ 11-gene-expression-analysis-comparison-controls.ipynb, lines 911–914 · score 0.57 · ligand gated ion, voltage gated ion, ion channel, gene expression
  6. [6] § Results › A single injection of an addictive drug changes brain-wide input patterns to VTADA cells ↔ 10-gene-expression-analysis-cocaine.ipynb, lines 375–384 · score 0.55 · DStr, PVH, LHb, VP, CeA, PO
  7. [7] § Results › Exploring gene expression patterns that predict changes in RABV input labeling ↔ 11-gene-expression-analysis-comparison-controls.ipynb, lines 1145–1186 · score 0.55 · exocytosis genes, linear regressions, endo, gene expression, ratios, drug
  8. [8] § Results › Dimensionality reduction methods identify differences in inputs of VTA cell populations ↔ 2-DAT-Cre vs GAD2-Cre and vGluT2-Cre-euclidean.ipynb, lines 540–550 · score 0.55 · Euclidean distance, GAD2 Cre, DAT Cre, PCA
  9. [9] § Results › Dimensionality reduction methods identify differences in inputs of VTA cell populations ↔ 2-DAT-Cre vs GAD2-Cre and vGluT2-Cre-euclidean.ipynb, lines 299–351 · score 0.54 · standard deviation, GAD2 Cre, DAT Cre, error, Dimensionality
  10. [10] § Results › Exploring gene expression patterns that predict changes in RABV input labeling ↔ 11-gene-expression-analysis-comparison-controls.ipynb, lines 911–914 · score 0.51 · ligand gated ion, voltage gated ion, ion channel, Gene Expression
  11. [11] § Results › Dimensionality reduction methods identify differences in inputs of VTA cell populations ↔ 10-gene-expression-analysis-cocaine.ipynb, lines 400–438 · score 0.50 · anterior cortex, LHb, VP, CeA, PO, EP

Paper

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The authors' code

Jupyter notebook · 687 lines · 27 KB · MIT · 4 matches

  1. # %%
  2. #import things we need
  3. import pandas as pd
  4. import numpy as np
  5. import matplotlib.pyplot as plt
  6. from sklearn.preprocessing import StandardScaler
  7. from sklearn.decomposition import PCA
  8. import umap
  9. import seaborn as sns
  10. from scipy.stats import zscore
  11. from matplotlib.patches import Ellipse
  12. import matplotlib.transforms as transforms
  14. # %% [markdown]
  15. # # Load data, rotate as needed, join dataframes together for ease of processing later
  17. # %%
  18. set_metric='euclidean'
  19. set_stdevs=1
  21. # %%
  22. #import data DAT-cre
  23. DATcre66T = pd.read_excel(r'data/8dat-crebrains.xlsx')
  24. #and import dat cre and gad2 also from 76 brain set
  25. DATcre66T_gad2 = pd.read_excel(r'data/12datcre-and-gad2.xlsx')
  27. # %%
  28. DATcre66T_gad2
  30. # %%
  31. VGlut2_cre = pd.read_excel(r'data/vGluT2-Cre.xlsx')
  33. # %%
  34. #import data (all DA cells)
  35. all_DA_cells = ['Cocaine','Control ketamine anesthesia','Control isoflurane anesthesia','Amphetamine',
  36. 'Nicotine','Morphine','Fluoxetine','MDMA group','MDMA isolated','Ethanol','Stress',
  37. 'GAD2-Cre isoflurane','GAD2-Cre Cocaine','GAD2-Cre isoflurane (lateral)']
  38. all_DA_cells_df = {} #empty dictionary
  39. num_drugs=len(all_DA_cells) #get number of drugs/conditions
  40. for drug in range(num_drugs):
  41. #read in the sheet with that drug name
  42. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',sheet_name=all_DA_cells[drug])
  43. #add id column with drug name and condition
  44. tempdf['condition']=all_DA_cells[drug]
  45. list1=list(tempdf['condition'])
  46. length=len(tempdf['condition'])
  47. list2=list(range(1,length+1))
  48. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  49. tempdf.dropna(inplace=True)
  50. all_DA_cells_df[drug]=tempdf #put into dictionary at index
  52. # %%
  53. # #import NAcMed data
  54. NAcMeds = ['cTRIO NAcMed cocaine 1 inj','NAcMed controls','cTRIO NAcMed C 5 inj',
  55. 'cTRIO NAcMed FC','cTRIO NAcMed ketamine anesthesi']
  56. NAcMeds_df = {} #empty dictionary
  57. num_drugs=len(NAcMeds) #get number of drugs/conditions
  58. for drug in range(num_drugs):
  59. #read in the sheet with that drug name
  60. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',sheet_name=NAcMeds[drug])
  61. #add id column with drug name and condition
  62. tempdf['condition']=NAcMeds[drug]
  63. list1=list(tempdf['condition'])
  64. length=len(tempdf['condition'])
  65. list2=list(range(1,length+1))
  66. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  67. tempdf.dropna(inplace=True)
  68. NAcMeds_df[drug]=tempdf #put into dictionary at index
  70. # %%
  71. NAcMed_data=pd.concat(
  72. NAcMeds_df,
  73. axis=0,
  74. join="outer",
  75. ignore_index=False,
  76. keys=None,
  77. levels=None,
  78. names=None,
  79. verify_integrity=False,
  80. copy=True,
  81. )
  83. # %%
  84. # #import NAcLat data
  85. NAcLats = ['cTRIO NAcLat cocaine','NAcLat iso saline controls','cTRIO NAcLat C 5 inj',
  86. 'cTRIO NAcLat FC','cTRIO NAcLat ketamine anesthesi']
  87. NAcLats_df = {} #empty dictionary
  88. num_drugs=len(NAcLats) #get number of drugs/conditions
  89. for drug in range(num_drugs):
  90. #read in the sheet with that drug name
  91. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',sheet_name=NAcLats[drug])
  92. #add id column with drug name and condition
  93. tempdf['condition']=NAcLats[drug]
  94. list1=list(tempdf['condition'])
  95. length=len(tempdf['condition'])
  96. list2=list(range(1,length+1))
  97. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  98. tempdf.dropna(inplace=True)
  99. NAcLats_df[drug]=tempdf #put into dictionary at index
  101. # %%
  102. #combine NAcLat data
  103. NAcLat_data=pd.concat(
  104. NAcLats_df,
  105. axis=0,
  106. join="outer",
  107. ignore_index=False,
  108. keys=None,
  109. levels=None,
  110. names=None,
  111. verify_integrity=False,
  112. copy=True,
  113. )
  115. # %%
  116. # #import DLS data
  117. DLSs = ['DLS_sal','DLS_coc1x','DLS_FC']
  118. DLSs_df = {} #empty dictionary
  119. num_drugs=len(DLSs) #get number of drugs/conditions
  120. for drug in range(num_drugs):
  121. #read in the sheet with that drug name
  122. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',
  123. sheet_name=DLSs[drug],skiprows=1)
  124. tempdf=tempdf.transpose()
  125. tempdf.columns = tempdf.iloc[0]
  126. tempdf = tempdf.iloc[1: , :]
  127. #add id column with drug name and condition
  128. tempdf['condition']=DLSs[drug]
  129. list1=list(tempdf['condition'])
  130. length=len(tempdf['condition'])
  131. list2=list(range(1,length+1))
  132. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  133. tempdf.dropna(inplace=True)
  134. DLSs_df[drug]=tempdf #put into dictionary at index
  136. # %%
  137. #combine DLS data
  138. DLS_data=pd.concat(
  139. DLSs_df,
  140. axis=0,
  141. join="outer",
  142. ignore_index=False,
  143. keys=None,
  144. levels=None,
  145. names=None,
  146. verify_integrity=False,
  147. copy=True,
  148. )
  150. # %%
  151. # #import amygdala data
  152. Amygs = ['cTRIO Amygdala cocaine 1 dose','cTRIO Amygdala isoflurane anest','cTRIO Amygdala AFC']
  153. Amygs2= ['cTRIO Amygdala ketamine anesthe']
  154. Amygs_df = {} #empty dictionary
  155. num_drugs=len(Amygs) #get number of drugs/conditions
  156. for drug in range(num_drugs):
  157. #read in the sheet with that drug name
  158. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',
  159. sheet_name=Amygs[drug],skiprows=1)
  160. tempdf=tempdf.transpose()
  161. tempdf.columns = tempdf.iloc[0]
  162. tempdf = tempdf.iloc[1: , :]
  163. #add id column with drug name and condition
  164. tempdf['condition']=Amygs[drug]
  165. list1=list(tempdf['condition'])
  166. length=len(tempdf['condition'])
  167. list2=list(range(1,length+1))
  168. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  169. tempdf.dropna(inplace=True)
  170. Amygs_df[drug]=tempdf #put into dictionary at index
  171. for drug in range(1):
  172. #read in the sheet with that drug name
  173. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',sheet_name=Amygs2[drug])
  174. #add id column with drug name and condition
  175. tempdf['condition']=Amygs2[drug]
  176. list1=list(tempdf['condition'])
  177. length=len(tempdf['condition'])
  178. list2=list(range(1,length+1))
  179. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  180. tempdf.dropna(inplace=True)
  181. Amygs_df[3]=tempdf #put into dictionary at index
  183. # %%
  184. #combine Amyg data
  185. Amyg_data=pd.concat(
  186. Amygs_df,
  187. axis=0,
  188. join="outer",
  189. ignore_index=False,
  190. keys=None,
  191. levels=None,
  192. names=None,
  193. verify_integrity=False,
  194. copy=True,
  195. )
  197. # %%
  198. Amyg_data.dropna(inplace=True,axis=1)
  200. # %%
  201. #import mPFC data
  202. mPFCs = ['cTRIO mPFC isoflurane anesthesi','cTRIO mPFC cocaine 1 dose','cTRIO mPFC AFC']
  203. mPFCs2= ['cTRIO mPFC ketamine anesthesia']
  204. mPFCs_df = {} #empty dictionary
  205. num_drugs=len(mPFCs) #get number of drugs/conditions
  206. for drug in range(num_drugs):
  207. #read in the sheet with that drug name
  208. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',
  209. sheet_name=mPFCs[drug],skiprows=1)
  210. tempdf=tempdf.transpose()
  211. tempdf.columns = tempdf.iloc[0]
  212. tempdf = tempdf.iloc[1: , :]
  213. #add id column with drug name and condition
  214. tempdf['condition']=mPFCs[drug]
  215. list1=list(tempdf['condition'])
  216. length=len(tempdf['condition'])
  217. list2=list(range(1,length+1))
  218. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  219. tempdf.dropna(inplace=True)
  220. mPFCs_df[drug]=tempdf #put into dictionary at index
  221. for drug in range(1):
  222. #read in the sheet with that drug name
  223. tempdf = pd.read_excel(r'data/organized drug and experience data for Bartas et al.xlsx',sheet_name=mPFCs2[drug])
  224. #add id column with drug name and condition
  225. tempdf['condition']=mPFCs2[drug]
  226. list1=list(tempdf['condition'])
  227. length=len(tempdf['condition'])
  228. list2=list(range(1,length+1))
  229. tempdf['condition_num']=[str(s) +'-'+ str(num) for s,num in zip(list1,list2)]
  230. tempdf.dropna(inplace=True)
  231. mPFCs_df[3]=tempdf #put into dictionary at index
  233. # %%
  234. # combine mPFC data
  235. mPFC_data=pd.concat(
  236. mPFCs_df,
  237. axis=0,
  238. join="outer",
  239. ignore_index=False,
  240. keys=None,
  241. levels=None,
  242. names=None,
  243. verify_integrity=False,
  244. copy=True,
  245. )
  247. # %%
  248. mPFC_data.dropna(inplace=True,axis=1)
  250. # %%
  251. all_data=pd.concat(
  252. all_DA_cells_df,
  253. axis=0,
  254. join="outer",
  255. ignore_index=False,
  256. keys=None,
  257. levels=None,
  258. names=None,
  259. verify_integrity=False,
  260. copy=True,
  261. )
  263. # %%
  264. categories= pd.DataFrame({'group_name':['Psychostimulants','Controls','Controls',
  265. 'Psychostimulants','Other','Other','Controls','MDMA','MDMA','Other',
  266. 'Other','GAD2-Cre','GAD2-Cre','GAD2-Cre'],'condition5':all_DA_cells})
  267. categories2= pd.DataFrame({'group_name':['Cocaine','Controls','Cocaine',
  268. 'FC','Controls'],'condition5':NAcMeds})
  269. categories3= pd.DataFrame({'group_name':['Cocaine','Controls','Cocaine',
  270. 'FC','Controls'],'condition5':NAcLats})
  271. categories4= pd.DataFrame({'group_name':['Cocaine','Controls','AFC',
  272. 'Controls'],'condition5':['cTRIO Amygdala cocaine 1 dose','cTRIO Amygdala isoflurane anest','cTRIO Amygdala AFC','cTRIO Amygdala ketamine anesthe']})
  273. categories5= pd.DataFrame({'group_name':['Controls','Cocaine',
  274. 'AFC','Controls'],'condition5':['cTRIO mPFC isoflurane anesthesi','cTRIO mPFC cocaine 1 dose','cTRIO mPFC AFC','cTRIO mPFC ketamine anesthesia']})
  275. categories6= pd.DataFrame({'group_name':['DAT-Cre','DAT-Cre','GAD2-Cre'],'condition5':['DAT-Cre', 'DAT-Cre 66T','Gad2-Cre']})
  276. categories7= pd.DataFrame({'group_name':['vGluT2-Cre'],'condition5':['vGluT2-Cre']})
  277. categories=pd.concat([categories,categories2,categories3,categories4,categories5,categories6,categories7])
  279. # %%
  280. all_data.replace({'GAD2-Cre isoflurane': 'Gad2-Cre', 'GAD2-Cre isoflurane (lateral)': 'Gad2-Cre'},inplace=True)
  282. # %% [markdown]
  283. # # Define some functions, variables, and other things we will use
  285. # %%
  286. uniq_colors=['mediumorchid','gray','royalblue','darkred','red','hotpink',
  287. 'khaki','palegreen','seagreen','orange','lavender',
  288. 'steelblue','navy','dodgerblue','thistle','violet',
  289. 'deeppink','pink','slateblue','peru','darkorange',
  290. 'magenta','cyan','lightcoral','lawngreen',
  291. 'burlywood'] # to use later
  292. uniq_colors2=['mediumorchid','gray','royalblue','darkred','hotpink',
  293. 'khaki','palegreen','seagreen','orange','lavender',
  294. 'steelblue','navy','dodgerblue','thistle','violet',
  295. 'deeppink','pink','slateblue','peru','darkorange',
  296. 'magenta','cyan','lightcoral','lawngreen',
  297. 'burlywood','red'] # to use later
  299. # %%
  300. #from https://matplotlib.org/devdocs/gallery/statistics/confidence_ellipse.html
  301. def confidence_ellipse(x, y, ax, n_std=set_stdevs, facecolor='none', **kwargs):
  302. """
  303. Create a plot of the covariance confidence ellipse of *x* and *y*.
  305. Parameters
  306. ----------
  307. x, y : array-like, shape (n, )
  308. Input data.
  310. ax : matplotlib.axes.Axes
  311. The axes object to draw the ellipse into.
  313. n_std : float
  314. The number of standard deviations to determine the ellipse's radiuses.
  316. **kwargs
  317. Forwarded to `~matplotlib.patches.Ellipse`
  319. Returns
  320. -------
  321. matplotlib.patches.Ellipse
  322. """
  323. if x.size != y.size:
  324. raise ValueError("x and y must be the same size")
  326. cov = np.cov(x, y)
  327. pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])
  328. # Using a special case to obtain the eigenvalues of this
  329. # two-dimensionl dataset.
  330. ell_radius_x = np.sqrt(1 + pearson)
  331. ell_radius_y = np.sqrt(1 - pearson)
  332. ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2,
  333. facecolor=facecolor, **kwargs)
  335. # Calculating the stdandard deviation of x from
  336. # the squareroot of the variance and multiplying
  337. # with the given number of standard deviations.
  338. scale_x = np.sqrt(cov[0, 0]) * n_std
  339. mean_x = np.mean(x)
  341. # calculating the stdandard deviation of y ...
  342. scale_y = np.sqrt(cov[1, 1]) * n_std
  343. mean_y = np.mean(y)
  345. transf = transforms.Affine2D() \
  346. .rotate_deg(45) \
  347. .scale(scale_x, scale_y) \
  348. .translate(mean_x, mean_y)
  350. ellipse.set_transform(transf + ax.transData)
  351. return ax.add_patch(ellipse)
  353. # %%
  354. def signif(x, p): #get p significant digits of all of a numpy array (why does base numpy not have this...)
  355. x = np.asarray(x)
  356. x_positive = np.where(np.isfinite(x) & (x != 0), np.abs(x), 10**(p-1))
  357. mags = 10 ** (p - 1 - np.floor(np.log10(x_positive)))
  358. return np.round(x * mags) / mags
  360. # %%
  361. def plot_components(components_df): #plot feature importance of the PCA components 1-5
  362. #code adapted from
  363. #https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html
  364. fig, ax = plt.subplots(figsize=(6, 13))
  365. comptemp0=components_df.transpose()
  366. comptemp1=comptemp0.to_numpy()
  367. comptemp=signif(comptemp1,3)
  368. im = ax.imshow(comptemp)
  369. pca5=['PC 1', 'PC 2', 'PC 3', 'PC 4', 'PC 5']
  370. regions=components_df.columns
  371. # We want to show all ticks...
  372. ax.set_xticks(np.arange(len(pca5)))
  373. ax.set_yticks(np.arange(len(regions)))
  374. # ... and label them with the respective list entries
  375. ax.set_xticklabels(pca5)
  376. ax.set_yticklabels(regions)
  378. # Rotate the tick labels and set their alignment.
  379. plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
  380. rotation_mode="anchor")
  381. fig.colorbar(im)
  383. # Loop over data dimensions and create text annotations.
  384. for i in range(len(regions)):
  385. for j in range(len(pca5)):
  386. text = ax.text(j, i, comptemp[i, j],
  387. ha="center", va="center", color="w")
  389. ax.set_title("Components and Feature Importance")
  390. fig.tight_layout()
  391. plt.show()
  393. # %%
  394. def make_pca_plots_label(pc1,pc2,pc3,group,labels): #makes plots of pc1v2, 1v3, and 2v3
  395. align_embed= pd.DataFrame({'pc1': pc1.values,'pc2': pc2.values,'pc3': pc3.values},index=labels)
  396. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  397. for i,r in enumerate(np.unique(align_embed.index.values)):
  398. drug1=align_embed[align_embed.index == r]
  399. ax.scatter(drug1['pc1'],drug1['pc2'],c=uniq_colors[i],label=r)
  400. ax.set_xlabel('PC 1')
  401. ax.set_ylabel('PC 2')
  402. for i, txt in enumerate(group):
  403. ax.annotate(txt, (pc1[i], pc2[i]))
  404. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  405. plt.show()
  406. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  407. for i,r in enumerate(np.unique(align_embed.index.values)):
  408. drug1=align_embed[align_embed.index == r]
  409. ax.scatter(drug1['pc1'],drug1['pc3'],c=uniq_colors[i],label=r)
  410. ax.set_xlabel('PC 1')
  411. ax.set_ylabel('PC 3')
  412. for i, txt in enumerate(group):
  413. ax.annotate(txt, (pc1[i], pc3[i]))
  414. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  415. plt.show()
  416. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  417. for i,r in enumerate(np.unique(align_embed.index.values)):
  418. drug1=align_embed[align_embed.index == r]
  419. ax.scatter(drug1['pc2'],drug1['pc3'],c=uniq_colors[i],label=r)
  420. ax.set_xlabel('PC 2')
  421. ax.set_ylabel('PC 3')
  422. for i, txt in enumerate(group):
  423. ax.annotate(txt, (pc2[i], pc3[i]))
  424. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  425. plt.show()
  427. # %%
  428. def make_pca_plots_ellipse(pc1,pc2,pc3,group,labels,grouped_elipse='no'): #makes plots of pc1v2, 1v3, and 2v3
  429. align_embed= pd.DataFrame({'pc1': pc1.values,'pc2': pc2.values,'pc3': pc3.values},index=labels)
  430. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  431. if grouped_elipse=='no':
  432. for i,r in enumerate(np.unique(align_embed.index.values)):
  433. drug1=align_embed[align_embed.index == r]
  434. ax.scatter(drug1['pc1'],drug1['pc2'],c=uniq_colors[i],label=r)
  435. confidence_ellipse(drug1['pc1'],drug1['pc2'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0)
  436. ax.set_xlabel('PC 1')
  437. ax.set_ylabel('PC 2')
  438. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  439. plt.show()
  440. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  441. for i,r in enumerate(np.unique(align_embed.index.values)):
  442. drug1=align_embed[align_embed.index == r]
  443. ax.scatter(drug1['pc1'],drug1['pc3'],c=uniq_colors[i],label=r)
  444. confidence_ellipse(drug1['pc1'],drug1['pc3'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0)
  445. ax.set_xlabel('PC 1')
  446. ax.set_ylabel('PC 3')
  447. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  448. plt.show()
  449. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  450. for i,r in enumerate(np.unique(align_embed.index.values)):
  451. drug1=align_embed[align_embed.index == r]
  452. ax.scatter(drug1['pc2'],drug1['pc3'],c=uniq_colors[i],label=r)
  453. confidence_ellipse(drug1['pc2'],drug1['pc3'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0)
  454. ax.set_xlabel('PC 2')
  455. ax.set_ylabel('PC 3')
  456. else:
  457. align_embed['condition5']=labels #categories
  458. category_df=align_embed.merge(categories,on='condition5',copy=True).dropna()
  459. for i,r in enumerate(np.unique(align_embed.index.values)):
  460. drug1=align_embed[align_embed.index == r]
  461. ax.scatter(drug1['pc1'],drug1['pc2'],c=uniq_colors[i],label=r)
  462. ax.set_xlabel('PC 1')
  463. ax.set_ylabel('PC 2')
  464. for i,r in enumerate(np.unique(category_df['group_name'])):
  465. drug1=category_df[category_df['group_name']== r]
  466. confidence_ellipse(drug1['pc1'],drug1['pc2'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0,label=r)
  467. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  468. plt.show()
  469. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  470. for i,r in enumerate(np.unique(align_embed.index.values)):
  471. drug1=align_embed[align_embed.index == r]
  472. ax.scatter(drug1['pc1'],drug1['pc3'],c=uniq_colors[i],label=r)
  473. ax.set_xlabel('PC 1')
  474. ax.set_ylabel('PC 3')
  475. for i,r in enumerate(np.unique(category_df['group_name'])):
  476. drug1=category_df[category_df['group_name']== r]
  477. confidence_ellipse(drug1['pc1'],drug1['pc3'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0,label=r)
  478. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  479. plt.show()
  480. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  481. for i,r in enumerate(np.unique(align_embed.index.values)):
  482. drug1=align_embed[align_embed.index == r]
  483. ax.scatter(drug1['pc2'],drug1['pc3'],c=uniq_colors[i],label=r)
  484. ax.set_xlabel('PC 2')
  485. ax.set_ylabel('PC 3')
  486. for i,r in enumerate(np.unique(category_df['group_name'])):
  487. drug1=category_df[category_df['group_name']== r]
  488. confidence_ellipse(drug1['pc2'],drug1['pc3'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0,label=r)
  489. ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5)) #make legend not on the plot
  490. plt.show()
  492. # %%
  493. def get_feats_and_labels(dataframe): #gets features and conditions (drugs) and brain regions
  494. labels=dataframe['condition'].values
  495. condition_nums=dataframe['condition_num'].values
  496. features=dataframe.iloc[: , 2:24]
  497. regions=features.columns
  498. return features, labels, condition_nums, regions
  500. # %%
  501. gad2only2=all_data[(all_data["condition"] == 'Gad2-Cre')]
  502. features4, labels4, condition_nums4, regions4 = get_feats_and_labels(gad2only2)
  504. # %%
  505. #modularized pieter's code
  506. def umap_n_times(X_scaled,labels,N = 20,n_neighbors=15,metric='euclidean'):
  507. umap_distances = np.zeros((len(labels),len(labels),N,))
  508. for n in range(0,N):
  509. embedding=umap.UMAP(n_neighbors=n_neighbors,metric=metric).fit_transform(X_scaled)
  510. for r1 in range(0,len(labels)):
  511. for r2 in range(0,len(labels)):
  512. d = np.sum((embedding[r1]-embedding[r2])**2)**(0.5)
  513. umap_distances[r1,r2,n]=d
  514. max_distance = np.max(umap_distances[:,:,n])
  515. umap_distances[:,:,n] = umap_distances[:,:,n]/max_distance
  516. umap_distances_means = np.mean(umap_distances,axis=2)
  517. return umap_distances_means
  519. # %%
  520. #modularized pieter's code
  521. def umap_dist_heatmap(t1,indexed='no'):
  522. if indexed=='yes':
  523. labs=labels.values
  524. uniq=list(range(len(labels)))
  525. uniq_labels = [str(i) +'_'+ str(j) for i, j in zip(labs, uniq)]
  526. else:
  527. labs=labels.values
  528. uniq=[x[1] for x in labels.index.values]
  529. uniq_labels = [str(i) +'_'+ str(j) for i, j in zip(labs, uniq)]
  530. fig,ax =plt.subplots(figsize=(10,4))
  531. plt.pcolor(t1, cmap="RdYlBu_r")
  533. plt.yticks(np.arange(0.5, len(uniq_labels), 1),uniq_labels)
  534. plt.xticks(np.arange(0.5, len(uniq_labels), 1),uniq_labels)
  535. plt.colorbar()
  536. plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
  537. rotation_mode="anchor")
  538. plt.show()
  540. # %%
  541. #modularized pieter's code
  542. def pca_distance(pc1,pc2,pc3,labels):
  543. pca_distances = np.zeros((len(labels),len(labels)))
  544. for r1 in range(0,len(labels)):
  545. for r2 in range(0,len(labels)):
  546. d = ((pc1[r1]-pc1[r2])**2 +(pc2[r1]-pc2[r2])**2+(pc3[r1]-pc3[r2])**2)**(0.5)
  547. pca_distances[r1,r2]=d #euclidean distance
  548. max_distance = np.max(pca_distances[:,:])
  549. pca_distances[:,:] = pca_distances[:,:]/max_distance #normalized
  550. return pca_distances
  552. # %%
  553. def distance_to_other_points(to_heatmap,labels,condition_nums,umap_or_pca):
  554. means_df2 = pd.DataFrame(to_heatmap,index=labels,columns=condition_nums)
  555. labels2=[str(s) +'_average' for s in labels]
  556. means_df2['condition2']=labels2
  557. means_df2=means_df2.groupby(['condition2']).mean().transpose()
  558. cm = sns.clustermap(means_df2,yticklabels=True,xticklabels=True)
  559. cm.fig.suptitle("point distance - average by condition 1 axis"+umap_or_pca)
  560. cm.fig.set_size_inches(18, 18)
  561. plt.show()
  562. means_df2['condition3']=labels2
  563. means_df2=means_df2.groupby(['condition3']).mean().transpose()
  564. cm = sns.clustermap(means_df2,yticklabels=True,xticklabels=True)
  565. cm.fig.suptitle("point distance - average by condition both axis"+umap_or_pca)
  566. cm.fig.set_size_inches(18, 18)
  567. plt.show()
  569. # %%
  570. def umap_and_heatmap(X_scaled,labels,condition_nums,grouped_elipse='no'):
  571. neighbors=int(len(labels)/3)
  572. metrics=set_metric
  573. reducer = umap.UMAP(metric=metrics,n_neighbors=neighbors)
  574. embedding = reducer.fit_transform(X_scaled)
  576. align_embed= pd.DataFrame(embedding,index=labels,columns=['umap1','umap2'])
  577. # fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  578. # for i,r in enumerate(np.unique(align_embed.index.values)):
  579. # drug1=align_embed[align_embed.index == r]
  580. # ax.scatter(drug1['umap1'],drug1['umap2'],c=uniq_colors[i],label=r)
  581. # for i, txt in enumerate(condition_nums):
  582. # ax.annotate(txt, (embedding[i,0], embedding[i,1]))
  583. # ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5))
  584. # ax.set_xlabel('UMAP 1')
  585. # ax.set_ylabel('UMAP 2')
  586. # plt.show()
  588. fig, ax = plt.subplots(figsize=(12, 10)) #init fig
  589. if grouped_elipse=='no':
  590. for i,r in enumerate(np.unique(align_embed.index.values)):
  591. drug1=align_embed[align_embed.index == r]
  592. ax.scatter(drug1['umap1'],drug1['umap2'],c=uniq_colors[i],label=r)
  593. confidence_ellipse(drug1['umap1'],drug1['umap2'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0)
  594. else:
  595. align_embed['condition5']=labels #categories
  596. category_df=align_embed.merge(categories,on='condition5',copy=True).dropna()
  597. for i,r in enumerate(np.unique(align_embed.index.values)):
  598. drug1=align_embed[align_embed.index == r]
  599. ax.scatter(drug1['umap1'],drug1['umap2'],c=uniq_colors[i],label=r)
  600. for i,r in enumerate(np.unique(category_df['group_name'])):
  601. drug1=category_df[category_df['group_name']== r]
  602. confidence_ellipse(drug1['umap1'],drug1['umap2'], ax, alpha=0.5, n_std=set_stdevs, facecolor=uniq_colors2[i], edgecolor=uniq_colors2[i], zorder=0,label=r)
  603. leg2 = ax.legend(loc="right",bbox_to_anchor=(1.6, 0.5))
  604. ax.set_xlabel('UMAP 1')
  605. ax.set_ylabel('UMAP 2')
  606. plt.show()
  608. #distance heatmap
  609. to_heatmap=umap_n_times(X_scaled,labels,metric=metrics,n_neighbors=neighbors)
  610. means_df = pd.DataFrame(to_heatmap,index=condition_nums,columns=condition_nums)
  611. cm = sns.clustermap(means_df,yticklabels=True,xticklabels=True)
  612. cm.fig.suptitle("average umap relative distance")
  613. cm.fig.set_size_inches(18, 18)
  614. plt.show()
  615. distance_to_other_points(to_heatmap,labels,condition_nums,' - UMAP') # umap
  617. # %%
  618. def get_feats_and_labels(dataframe):
  619. labels=dataframe['Experimental condition']
  620. condition_nums=dataframe['condition_nums']
  621. features=dataframe.iloc[: , 1:23]
  622. regions=features.columns
  623. return features, labels, condition_nums, regions
  625. # %% [markdown]
  626. # # Get features from DAT-cre and 66T data
  628. # %% [markdown]
  629. # # DAT-cre and 66T and Gad2Cre (no drugs)
  631. # %%
  632. features2, labels2, condition_nums2, regions2 = get_feats_and_labels(DATcre66T_gad2)
  633. features3, labels3, condition_nums3, regions3 = get_feats_and_labels(VGlut2_cre)
  634. labels=list(labels2)+list(labels4)+list(labels3)
  635. condition_nums=list(condition_nums2)+list(condition_nums4)+list(condition_nums3)
  636. #scale X
  638. X2=features2.to_numpy().astype(float)
  639. X3=features3.to_numpy().astype(float)
  640. X4=features4.to_numpy().astype(float)
  641. #features4, labels4, condition_nums4, regions4
  642. X=np.vstack((X2, X4))
  643. X=np.vstack((X, X3))
  644. #get rid of NAs before scaling
  646. X_scaled=zscore(X,axis=1)
  647. X_scaled=zscore(X_scaled,axis=0)
  649. # %%
  650. #can change n_components to more if you want to visualize in 3D
  651. pca = PCA(n_components=5)
  652. #PCA using the scaled drug data array made earlier
  653. principalComponents = pca.fit_transform(X_scaled)
  654. #make into dataframe if you want to view with labels
  655. principalDf = pd.DataFrame(data = principalComponents
  656. , columns = ['principal component 1', 'principal component 2', 'principal component 3', 'principal component 4', 'principal component 5'])
  657. region_components=pd.DataFrame(data = pca.components_ , columns = regions2)
  658. #view df
  659. print('Explained variance for each PC: 1, 2, 3')
  660. print(pca.explained_variance_)
  661. principalDf
  663. # %% [markdown]
  664. # ### PCA Plots
  666. # %%
  667. pc1=principalDf['principal component 1']
  668. pc2=principalDf['principal component 2']
  669. pc3=principalDf['principal component 3']
  670. #make_pca_plots_label(pc1,pc2,pc3,condition_nums,labels)
  671. make_pca_plots_ellipse(pc1,pc2,pc3,condition_nums,labels,grouped_elipse='yes')
  672. plot_components(region_components)
  674. # %%
  675. pca_dist=pca_distance(pc1,pc2,pc3,labels)
  676. heatmapdf = pd.DataFrame(pca_dist,index=condition_nums,columns=condition_nums)
  677. cm = sns.clustermap(heatmapdf,yticklabels=True)
  678. cm.fig.suptitle("PCA relative distance")
  679. cm.fig.set_size_inches(18, 18)
  680. plt.show()
  681. distance_to_other_points(pca_dist,labels,condition_nums,' - PCA') # pca
  683. # %% [markdown]
  684. # ### UMAP
  686. # %%
  687. umap_and_heatmap(X_scaled,labels,condition_nums,grouped_elipse='yea')

2-DAT-Cre vs GAD2-Cre and vGluT2-Cre-euclidean.ipynb at commit 4868c4c, under MIT · at the source

Overview

Authors: Katrina Bartas1, Pieter Derdeyn1, Guilian Tian2, Jose J Vasquez2, Ghalia Azouz2, Cindy M Yamamoto2, May Hui2, Kevin T Beier2,3,4,5
ORCID iDs: Pieter Derdeyn, Jose J Vasquez, May Hui, Kevin T Beier
  1. Program in Mathematical, Computational, and Systems Biology, University of California, Irvine Irvine United States
  2. Department of Physiology and Biophysics, University of California, Irvine Irvine United States
  3. Department of Biomedical Engineering, University of California, Irvine Irvine United States
  4. Department of Neurobiology and Behavior, University of California, Irvine Irvine United States
  5. Department of Pharmaceutical Sciences, University of California, Irvine Irvine United States
Institutions: University of California, Irvine (United States)
Journal: eLife, volume 13, article RP93664
Dates: published online 15 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.93664 · PMID 42138352 · PMCID PMC13179063 · OpenAlex W4394581287
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity
Keywords: addictive drugs, input mapping, rabies virus, ketamine/xylazine, cellular activity, connectomics, Mouse
MeSH: Dopaminergic Neurons*, Mesencephalon*, Rabies virus*, Animals, Male, Mice, Mice, Inbred C57BL (* major topic)
Journal subjects: Neuroscience
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (R00 DA041445, DP2 AG067666, R01 DA054374, R01 DA056599, R01 NS130044, T32 GM008620, F30 DA056215, T32 GM136624); Tobacco-Related Disease Research Program (T31KT1437, T31P1426); American Parkinson Disease Association (APDA-5589562); Alzheimer's Association (AARG-NTF-20-685694); New Vision Research (CCAD2020-002); BrightFocus Foundation (A2022031S); Brain and Behavior Research Foundation (NARSAD 26845); National Science Foundation (GRFP DGE-1839285)
Citations: cited by 1 paper (Europe PMC); 88 references in the paper
Research resources: pAAV2-8 RRID:Addgene_112864, RRID:Addgene_113694, pAAV2-5 RRID:Addgene_232922, HEK293T RRID:CVCL_0063, C57BL/6J RRID:IMSR_JAX:000664, DAT-Cre RRID:IMSR_JAX:006660, vGluT2-Cre RRID:IMSR_JAX:016963, GAD2-Cre RRID:IMSR_JAX:028867

Abstract

Addictive drugs cause long-lasting changes in connectivity from inputs onto ventral tegmental area dopamine cells (VTADA) that contribute to drug-induced behavioral adaptations. However, it is not known which inputs are altered. Here, we used a rabies virus (RABV)-based mapping strategy to quantify RABV-labeled inputs to VTA cells after a single exposure to one of a variety of misused drugs – cocaine, amphetamine, methamphetamine, morphine, and nicotine – and compared the relative global input labeling across conditions. We observed that all tested addictive drugs elicited similar input changes onto VTADA cells, in particular onto DA cells projecting to the lateral shell of the nucleus accumbens and amygdala. In addition, repeated administration of ketamine/xylazine to induce anesthesia induces a change in inputs to VTADA cells that is similar to but different from those elicited by a single exposure to addictive drugs, suggesting that caution should be taken when using ketamine/xylazine-based anesthesia in rodents when assessing motivated behaviors. Furthermore, comparison of viral tracing data to an atlas of gene expression in the adult mouse brain showed that the basal expression patterns of several gene classes, especially calcium channels, were highly correlated with the extent of both addictive drug- or ketamine/xylazine-induced changes in RABV-labeled inputs to VTADA cells. Reducing expression levels of the voltage-gated calcium channel Cacna1e in cells in the nucleus accumbens lateral shell reduced RABV-mediated input labeling of these cells into VTADA cells. These results directly link genes controlling cellular excitability and the extent of input labeling by RABV.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

ejcorn/mouse_abi_tool

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 5efe587ddbe223518503b28065435da95bfaf7ff, 16 July 2021
Languages: R (11)
Size: 19 files, 11 scripts
Software Heritage: archived
Found in: the text, “Gene expression data access and processing”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/ejcorn/mouse_abi_tool

ktbartas/Bartas_et_al_eLife_2024

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 4868c4c1df5cea4574c924df6042999362407284, 2 May 2026
Languages: Jupyter (14)
Size: 22 files, 14 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 14 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (8 files), NumPy (8 files), pandas (8 files), SciPy (7 files), seaborn (7 files), scikit-learn (3 files), UMAP (3 files), AllenSDK (1 file), Pillow (1 file), statsmodels (1 file), tifffile (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/ktbartas/Bartas_et_al_eLife_2024

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 19 scripts, each with its path and the digest of its content;
  • 11 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

The current manuscript is largely a computational study, so no data have been generated for most of this manuscript, except for Figure 10; raw data are available for Figure 10 on Dryad at https://doi.org/10.5061/dryad.gxd25481q. Code used for this work is available on GitHub (https://github.com/ktbartas/Bartas_et_al_eLife_2024 copy archived at Bartas and Derdeyn, 2026).

The following dataset was generated:

BartasK DerdeynP BeierKT 2026Drug-induced changes in connectivity to midbrain dopamine cells revealed by rabies monosynaptic tracingDryad Digital Repository10.5061/dryad.gxd25481qPMC1317906342138352

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 7 keywords, 7 MeSH terms, 8 funders, 84 references, 8 RRIDs.

Cite

This paper

Bartas, K., Derdeyn, P., Tian, G., Vasquez, J. J., Azouz, G., Yamamoto, C. M., Hui, M., & Beier, K. T. (2026). Drug-induced changes in connectivity to midbrain dopamine cells revealed by rabies monosynaptic tracing. eLife, 13, RP93664. https://doi.org/10.7554/elife.93664

BibTeX

@article{bartas2026drug,
author = {Bartas, Katrina and Derdeyn, Pieter and Tian, Guilian and Vasquez, Jose J and Azouz, Ghalia and Yamamoto, Cindy M and Hui, May and Beier, Kevin T},
title = {{Drug-induced changes in connectivity to midbrain dopamine cells revealed by rabies monosynaptic tracing}},
journal = {eLife},
year = {2026},
month = may,
volume = {13},
pages = {RP93664},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.93664},
url = {https://doi.org/10.7554/elife.93664},
pmid = {42138352},
pmcid = {PMC13179063}
}

RIS

TY - JOUR
AU - Bartas, Katrina
AU - Derdeyn, Pieter
AU - Tian, Guilian
AU - Vasquez, Jose J
AU - Azouz, Ghalia
AU - Yamamoto, Cindy M
AU - Hui, May
AU - Beier, Kevin T
TI - Drug-induced changes in connectivity to midbrain dopamine cells revealed by rabies monosynaptic tracing
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/05/15
VL - 13
SP - RP93664
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.93664
UR - https://doi.org/10.7554/elife.93664
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13179063",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.93664",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
15
]
]
}
}

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