OSCR

Huntingtin polyglutamine expansions misdirect axonal transport by perturbing motor and adaptor recruitment.

Code ↔ Paper

7 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 7 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis › Flux analysis ↔ Organelle_motility_TrackMate/TrackMate_to_kymo_and_flux_v4.m, lines 112–195 · score 0.85 · somal side, distal side, trajectory entered, anterograde flux, retrograde flux, box width
  2. [2] § STAR★Methods › Quantification and statistical analysis › Flux analysis ↔ Organelle_motility_KymoButler/TrackMate_to_kymo_and_flux_v4_KB.m, lines 138–223 · score 0.84 · somal side, distal side, trajectory entered, anterograde flux, retrograde flux, box width
  3. [3] § Results › Pathogenic HTT-polyQ increases the fraction of anterograde-directed lysosomes ↔ Organelle_motility_TrackMate/TrackMate_to_kymo_and_flux_v4.m, lines 112–195 · score 0.66 · somal side, soma side, distal side, KymoButler, TrackMate, exited
  4. [4] § Results › Pathogenic HTT-polyQ increases the fraction of anterograde-directed lysosomes ↔ Organelle_motility_KymoButler/TrackMate_to_kymo_and_colour_coded_flux_KB.m, lines 163–247 · score 0.64 · somal side, soma side, distal side, KymoButler, exited, flux
  5. [5] § STAR★Methods › Method details › Endosome immunofluorescence and imaging ↔ Organelle_motility_TrackMate/Plot_xk_yk_per_cell_stationary_track_v2.m, lines 305–383 · score 0.55 · Gaussian mixture, Gaussian fits, density, cell, fraction
  6. [6] § STAR★Methods › Method details › Endosome immunofluorescence and imaging ↔ Organelle_motility_KymoButler/Analyze_processive_motility_em_bstrp_4kchoose.m, lines 951–1035 · score 0.54 · Gaussian mixture, Gaussian fits, density
  7. [7] § STAR★Methods › Quantification and statistical analysis › TrackMate analysis parameters ↔ Organelle_motility_TrackMate/Analyze_Trackmate_motility_v2_em_master.m, lines 79–167 · score 0.50 · Weka Segmentation, TrackMate, mitochondria, trajectories, cargoes

Paper

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The authors' code

MATLAB · 573 lines · 33 KB · no license · 2 matches

  1. %% Emily Prowse 20230519
  2. %This code turns trackmate trajectories into kymographs and calculates the
  3. %flux of a in either direction within a 2 um box. See the flux section to
  4. %understand exactly how it's calculated.
  6. %Note: if you're planning to use the kymograph function, do not perform
  7. %this for all trajectories-your computer will be mad. change the maximum
  8. %kd to a maximum of 10 and k to a maximum of 5, only run one condition
  9. %(k_choose) at a time. You'll have to comment out the figures at the end
  10. %and the variables that generate them.
  11. clear all;
  12. close all;
  13. clc;
  15. addpath('/Volumes/Emily_htt_2/New_General_codes/');
  16. addpath('/Users/emilyprowse/Documents/McGill/Hendricks_Lab/Analysis/New_General_codes/raacampbell-notBoxPlot-7d90c27/code/+NBP/')
  17. addpath('/Users/emilyprowse/Documents/McGill/Hendricks_Lab/Analysis/violin/');
  19. colour_30Q=[0.5 0.5 0.5];
  20. colour_45Q=[0.75 0 0];
  21. colour_65Q=[0.5 0 0];
  22. colour_81Q=[0.25 0 0];
  24. % colour_30Q=[0.5 0.5 0.5]; %30Q
  25. % colour_45Q=[0.5 0.6 0.6]; %30Q IFg
  26. % colour_65Q=[0.25 0 0]; %81Q
  27. % colour_81Q=[0.25 0.1 0.1]; %81Q IFg
  29. count_stays_in_box=0;
  30. count_ant_exits=0;
  31. count_ret_exits=0;
  32. count_entered_ant=0;
  33. count_entered_ret=0;
  34. count_did_not_enter=0;
  35. count_started_in=0;
  36. count_ent_then_stop=0;
  38. Mot_file='*.mat';
  39. DT=0.12; % single channel exposure time 120ms
  41. % save_dir='/Volumes/Emily_htt_2/Neuron/mito_flux/';
  42. save_dir='/Volumes/Emily_htt_2/Neuron/lyso_flux/';
  43. % save_dir='/Volumes/Emily_htt_2/Neuron/bdnf_flux/';
  45. %% Variables defined
  46. for k_choose = 1:4
  48. if k_choose == 1 % 30Q
  49. cd('/Volumes/Emily_htt_2/Neuron/isoHD30Q/30Q_lyso_mats/');
  50. % cd('/Volumes/Emily_htt_2/Neuron/isoHD30Q/30Q_bdnf_mats/');
  51. % cd('/Volumes/Emily_htt_2/Neuron/isoHD30Q/30Q_mito_mats/');
  52. % cd('/Volumes/Emily_2022/Fake_TM_flux_test_mat/');
  53. elseif k_choose == 2 % 45Q
  54. cd('/Volumes/Emily_htt_2/Neuron/isoHD45Q/45Q_lyso_mats/');
  55. % cd('/Volumes/Emily_htt_2/Neuron/isoHD30Q/30Q_lyso_Ifg_mats/');
  56. % cd('/Volumes/Emily_htt_2/Neuron/isoHD30Q/30Q_bdnf_IFg_mats/');
  57. % cd('/Volumes/Emily_htt_2/Neuron/isoHD45Q/45Q_bdnf_mats/');
  58. % cd('/Volumes/Emily_htt_2/Neuron/isoHD45Q/45Q_mito_mats/');
  59. % cd('/Volumes/Emily_2022/Fake_TM_flux_test_mat/');
  60. elseif k_choose == 3 % 65Q
  61. cd('/Volumes/Emily_htt_2/Neuron/isoHD65Q/65Q_lyso_mats/');
  62. % cd('/Volumes/Emily_htt_2/Neuron/isoHD65Q/65Q_bdnf_mats/');
  63. % cd('/Volumes/Emily_htt_2/Neuron/isoHD65Q/65Q_mito_mats/');
  64. % cd('/Volumes/Emily_htt_2/Neuron/isoHD81Q/81Q_bdnf_mats/');
  65. % cd('/Volumes/Emily_htt_2/Neuron/isoHD81Q/81Q_lyso_mats/');
  66. % cd('/Volumes/Emily_2022/Fake_TM_flux_test_mat/');
  67. elseif k_choose == 4 % 81Q
  68. cd('/Volumes/Emily_htt_2/Neuron/isoHD81Q/81Q_lyso_mats/');
  69. % cd('/Volumes/Emily_htt_2/Neuron/isoHD81Q/81Q_lyso_IFg_mats/');
  70. % cd('/Volumes/Emily_htt_2/Neuron/isoHD81Q/81Q_bdnf_mats/');
  71. % cd('/Volumes/Emily_htt_2/Neuron/isoHD81Q/81Q_bdnf_IFg_mats/');
  72. % cd('/Volumes/Emily_htt_2/Neuron/isoHD81Q/81Q_mito_mats/');
  73. % cd('/Volumes/Emily_2022/Fake_TM_flux_test_mat/');
  74. end
  76. dat=[];
  78. dmot=dir(Mot_file); % Pick the trajectory files in .mat format
  80. count_stays_in_box=0;
  81. count_ant_exits=0;
  82. count_ret_exits=0;
  83. count_entered_ant=0;
  84. count_entered_ret=0;
  85. count_did_not_enter=0;
  86. count_started_in=0;
  87. count_ent_then_stop=0;
  88. n=0;
  90. for k=1:length(dmot) % k is a vector of length dmot
  91. display(k)
  92. load(dmot(k).name);%load everything in dmot
  93. [filepath,name,ext] = fileparts(dmot(k).name);
  94. display(dmot(k).name); % display
  95. cmap = colormap(lines(100000));
  96. Ndat=numel(ab); %comment out for kymobutler
  97. % Ndat=numel(kbpos);
  98. kp=0;
  100. for kd=1:Ndat
  101. kp=kp+1;
  103. x_position=ab(kd).xk;%x position (um) %comment out for kymobutler
  104. y_position=ab(kd).yk;%y position (um) %comment out for kymobutler
  105. t_position=ab(kd).tk;% Time (s) %comment out for kymobutler
  108. origin_x=ab(kd).origin_x; %comment out for kymobutler
  109. origin_y=ab(kd).origin_y; %comment out for kymobutler
  110. axon_length=ab(kd).axon_length; %comment out for kymobutler
  112. %create a 1D position coordinate from the x and y coordinates
  113. X0 = [origin_x origin_y]; %cell center position [x y] %, comment out for kymobutler
  114. r0{kd} = sqrt((x_position-X0(1)).^2 + (y_position-X0(2)).^2); % comment out for kymobutler
  115. %plotting kymograph from trackmate positons
  116. % time{kd}=t_position;k_
  117. % figure(k*10) %Plotting the kymograph
  118. % plot(r0{kd}, time{kd},'-','Color',cmap(kd,:),'linewidth',2)
  119. % xlabel('Position (\mum)'), ylabel('Time (s)'), hold on
  120. % xlim([0,axon_length]);
  121. % set(gca,'Ydir','reverse')
  123. %flux analysis: how many trajectories cross a 2um box in the middle of the axon?
  124. position=r0{kd}; %without kymobutler
  125. % t_position=kbpos(kd).tk;% Time (s)
  126. % position=kbpos(kd).position; %with kymobutler
  127. for it=1:5
  128. ax_seg=(axon_length*it)/5-2; %segment of the axon
  129. box_width=0.5; %We want to look +/- box_width from the middle of the axon
  130. if any(position >= ax_seg-box_width & position <= ax_seg+box_width) %is it in the box?
  131. n=n+1;%count how many fulfill this condition
  132. idx_enters=min(find(position >= ax_seg-box_width & position <= ax_seg+box_width)); %what is the index of the earliest timepoint that the position enters the box
  133. if idx_enters==1
  134. count_started_in(it)=1;
  135. traj_starts_in_box=position(1)>= ax_seg-box_width & position(1) <=ax_seg+box_width; %does it start in the box?
  136. traj_enters_box_ret=0; %position(1) < mid_length-box_width %does it enter from the somal side?
  137. traj_enters_box_ant=0; %position(1) > mid_length+box_width %does it enter from the distal side?
  138. else
  139. traj_enters_box_ret=position(idx_enters-1) < ax_seg-box_width; %does it enter from the somal side?
  140. traj_enters_box_ant=position(idx_enters-1) > ax_seg+box_width; %does it enter from the distal side?
  141. end
  143. if traj_enters_box_ret ==1 %the trajectory actually entered from the retrograde side
  144. count_entered_ret(it)=1;
  145. % traj_exits_box=max(find(position(idx_enters:end)+ box_width> mid_length-box_width | position(idx_enters:end) < mid_length-box_width));
  146. traj_exits_box_ret=position(end) < ax_seg-box_width; %what is the index of the latest timepoint that the position exits the box
  147. traj_exits_box_ant=position(end) > ax_seg+box_width; %what is the index of the latest timepoint that the position exits the box
  148. % traj_stays_in_box=all(position(idx_enters:end)> mid_length-box_width & position(idx_enters:end)<mid_length+box_width);
  149. traj_ent_then_stop=all(position(end)>= ax_seg-box_width & position(end) <=ax_seg+box_width);
  150. if any(traj_exits_box_ret)
  151. count_ret_exits(it)=1;
  152. elseif any(traj_exits_box_ant)
  153. count_ant_exits(it)=1;
  154. elseif any(traj_ent_then_stop)
  155. % count_ent_then_stop=count_ent_then_stop+1; %counting this as a separate case
  156. count_ant_exits(it)=1; %counting the ones that entered from the soma side as anterograde flux
  157. end
  159. elseif traj_enters_box_ant==1 %the trajectory entered the box from the anterograde side
  160. count_entered_ant(it)=1;
  161. % traj_exits_box=max(find(position(idx_enters:end)+ box_width> mid_length-box_width | position(idx_enters:end) < mid_length-box_width));
  162. traj_exits_box_ret=position(end) < ax_seg-box_width; %did it exit on the soma side?
  163. traj_exits_box_ant=position(end) > ax_seg+box_width; %did it exit on the distal side?
  164. % traj_stays_in_box=all(position(idx_enters:end)> mid_length-box_width & position(idx_enters:end)<mid_length+box_width);
  165. traj_ent_then_stop=all(position(end)>= ax_seg-box_width & position(end) <=ax_seg+box_width);
  166. if any(traj_exits_box_ret)
  167. count_ret_exits(it)=1;
  168. elseif any(traj_exits_box_ant)
  169. count_ant_exits(it)=1;
  170. elseif any(traj_ent_then_stop)
  171. % count_ent_then_stop=count_ent_then_stop+1; %counting this as a separate case
  172. count_ret_exits(it)=1; %counting those that enter from the distal side as retrograde flux
  173. end
  174. elseif traj_starts_in_box==1
  175. % count_started_in=count_started_in+1; % was double counted
  176. % traj_exits_box=max(find(position(idx_enters:end)+ box_width> mid_length-box_width | position(idx_enters:end) < mid_length-box_width));
  177. traj_exits_box_ret=position(end) < ax_seg-box_width; %did it exit on the soma side?
  178. traj_exits_box_ant=position(end) > ax_seg+box_width; %did it exit on the distal side?
  179. traj_stays_in_box=all(position(end)>= ax_seg-box_width & position(end) <=ax_seg+box_width);
  180. if any(traj_exits_box_ret)
  181. count_ret_exits(it)=1;
  182. elseif any(traj_exits_box_ant)
  183. count_ant_exits(it)=1;
  184. elseif any(traj_stays_in_box)
  185. count_stays_in_box(it)=1;
  186. end
  188. end
  189. else
  190. count_did_not_enter(it)=1;
  191. clear traj_starts_in_box traj_enters_box_ret traj_enters_box_ant
  192. end
  193. %the below variables are not used, since it is rare to find a
  194. %trajectory in a given box, we add together all the segments in the
  195. %trajectory and reset the counts after each trajectory.
  197. % count_entered_ant_tot=sum(count_entered_ant)
  198. % total_enters_per_seg_calc(it)=(count_entered_ant+count_entered_ret+count_started_in);
  199. % frac_ant_exits_per_seg_calc(it)=count_ant_exits/total_enters_per_seg_calc(it);
  200. % frac_ret_exits_per_seg_calc(it)=count_ret_exits/total_enters_per_seg_calc(it);
  201. % frac_stays_in_per_seg_calc(it)=count_stays_in_box/total_enters_per_seg_calc(it);
  202. % frac_ant_enter_per_seg_calc(it)=count_entered_ant/total_enters_per_seg_calc(it);
  203. % frac_ret_enter_per_seg_calc(it)=count_entered_ret/total_enters_per_seg_calc(it);
  204. % frac_starts_in_per_seg_calc(it)=count_started_in/total_enters_per_seg_calc(it);
  205. end
  207. count_entered_ret_tot=sum(count_entered_ret);
  208. count_entered_ant_tot=sum(count_entered_ant);
  209. count_started_in_tot=sum(count_started_in);
  210. count_ant_exits_tot=sum(count_ant_exits);
  211. count_ret_exits_tot=sum(count_ret_exits);
  212. count_stays_in_box_tot=sum(count_stays_in_box);
  214. % tot_enters_per_traj(kd)=count_entered_ant_tot+count_entered_ret_tot+count_started_in_tot;
  215. % tot_frac_ant_exits_per_traj(kd)=count_ant_exits_tot/tot_enters_per_traj(kd);
  216. % tot_frac_ret_exits_per_traj(kd)=count_ret_exits_tot/tot_enters_per_traj(kd);
  217. % tot_frac_stays_in_per_traj(kd)=count_stays_in_box_tot/tot_enters_per_traj(kd);
  218. % tot_frac_ant_enter_per_traj(kd)=count_entered_ant_tot/tot_enters_per_traj(kd);
  219. % tot_frac_ret_enter_per_traj(kd)=count_entered_ret_tot/tot_enters_per_traj(kd);
  220. % tot_frac_starts_in_per_traj(kd)=count_started_in_tot/tot_enters_per_traj(kd);
  222. tot_ant_exits_per_traj(kd)=count_ant_exits_tot;
  223. tot_ret_exits_per_traj(kd)=count_ret_exits_tot;
  224. tot_stays_in_per_traj(kd)=count_stays_in_box_tot;
  225. tot_ant_enter_per_traj(kd)=count_entered_ant_tot;
  226. tot_ret_enter_per_traj(kd)=count_entered_ret_tot;
  227. tot_starts_in_per_traj(kd)=count_started_in_tot;
  229. count_entered_ant=0; %reset these values so it actually counts each trajectory separately. Updated 20230814.
  230. count_ret_exits=0;
  231. count_ant_exits=0;
  232. count_stays_in_box=0;
  233. count_entered_ret=0;
  234. count_started_in=0;
  235. end
  237. tot_enters_per_cell{k}=sum(tot_ant_enter_per_traj)+sum(tot_ret_enter_per_traj)+sum(tot_starts_in_per_traj);
  238. tot_ant_exits_per_cell{k}=sum(tot_ant_exits_per_traj);
  239. tot_ret_exits_per_cell{k}=sum(tot_ret_exits_per_traj);
  240. tot_stays_in_per_cell{k}=sum(tot_stays_in_per_traj);
  241. tot_ant_enter_per_cell{k}=sum(tot_ant_enter_per_traj);
  242. tot_ret_enter_per_cell{k}=sum(tot_ret_enter_per_traj);
  243. tot_starts_in_per_cell{k}=sum(tot_starts_in_per_traj);
  245. frac_ant_exits_per_cell{k}=sum(tot_ant_exits_per_traj)./tot_enters_per_cell{k};
  246. frac_ret_exits_per_cell{k}=sum(tot_ret_exits_per_traj)./tot_enters_per_cell{k};
  247. frac_stays_in_per_cell{k}=sum(tot_stays_in_per_traj)./tot_enters_per_cell{k};
  248. frac_ant_enter_per_cell{k}=sum(tot_ant_enter_per_traj)./tot_enters_per_cell{k};
  249. frac_ret_enter_per_cell{k}=sum(tot_ret_enter_per_traj)./tot_enters_per_cell{k};
  250. frac_starts_in_per_cell{k}=sum(tot_starts_in_per_traj)./tot_enters_per_cell{k};
  253. clear axon_length x_position X0 y_position t_position_actual t_position
  255. tot_ant_exits_per_traj=[];
  256. tot_ret_exits_per_traj=[];
  257. tot_stays_in_per_traj=[];
  258. tot_ant_enter_per_traj=[];
  259. tot_ret_enter_per_traj=[];
  260. tot_starts_in_per_traj=[];
  262. end
  264. tot_enters_per_cell_mat=cell2mat(tot_enters_per_cell);
  265. frac_ant_enter_per_cell_mat=cell2mat(frac_ant_enter_per_cell);
  266. frac_ret_enter_per_cell_mat=cell2mat(frac_ret_enter_per_cell);
  267. frac_starts_in_per_cell_mat=cell2mat(frac_starts_in_per_cell);
  268. frac_ant_exits_per_cell_mat=cell2mat(frac_ant_exits_per_cell);
  269. frac_ret_exits_per_cell_mat=cell2mat(frac_ret_exits_per_cell);
  270. frac_stays_in_per_cell_mat=cell2mat(frac_stays_in_per_cell);
  272. tot_enters_per_cell_value_mat=cell2mat(tot_enters_per_cell);
  274. tot_enters_per_cell_all_conds{k_choose}=tot_enters_per_cell_mat;
  275. frac_ant_enter_per_cell_all_conds{k_choose}=frac_ant_enter_per_cell_mat;
  276. frac_ret_enter_per_cell_all_conds{k_choose}=frac_ret_enter_per_cell_mat;
  277. frac_starts_in_per_cell_all_conds{k_choose}=frac_starts_in_per_cell_mat;
  278. frac_ant_exits_per_cell_all_conds{k_choose}=frac_ant_exits_per_cell_mat;
  279. frac_ret_exits_per_cell_all_conds{k_choose}=frac_ret_exits_per_cell_mat;
  280. frac_stays_in_per_cell_all_conds{k_choose}=frac_stays_in_per_cell_mat;
  282. total_enters_per_cell_value_all_conds{k_choose}=tot_enters_per_cell_value_mat;
  284. mean_total_enters_per_cell_all_conds{k_choose}=mean(tot_enters_per_cell_mat,"omitnan");
  285. mean_frac_ant_enter_per_cell_all_conds{k_choose}=mean(frac_ant_enter_per_cell_mat,"omitnan");
  286. mean_frac_ret_enter_per_cell_all_conds{k_choose}=mean(frac_ret_enter_per_cell_mat,"omitnan");
  287. mean_frac_starts_in_per_cell_all_conds{k_choose}=mean(frac_starts_in_per_cell_mat,"omitnan");
  288. mean_frac_ant_exits_per_cell_all_conds{k_choose}=mean(frac_ant_exits_per_cell_mat,"omitnan");
  289. mean_frac_ret_exits_per_cell_all_conds{k_choose}=mean(frac_ret_exits_per_cell_mat,"omitnan");
  290. mean_frac_stays_in_per_cell_all_conds{k_choose}=mean(frac_stays_in_per_cell_mat,"omitnan");
  292. avg_total_entered(k_choose)=mean(tot_enters_per_cell_mat,"omitnan");
  293. avg_entered_ant(k_choose)=mean(frac_ant_enter_per_cell_mat,"omitnan");
  294. avg_entered_ret(k_choose)=mean(frac_ret_enter_per_cell_mat,"omitnan");
  295. avg_starts_in(k_choose)=mean(frac_starts_in_per_cell_mat,"omitnan");
  296. avg_exits_ant(k_choose)=mean(frac_ant_exits_per_cell_mat,"omitnan");
  297. avg_exits_ret(k_choose)=mean(frac_ret_exits_per_cell_mat,"omitnan");
  298. avg_stays_in(k_choose)=mean(frac_stays_in_per_cell_mat,"omitnan");
  300. sem_total_entered(k_choose)=std(tot_enters_per_cell_mat,"omitnan")/(sqrt(length(tot_enters_per_cell_mat)));
  301. sem_entered_ant(k_choose)=std(frac_ant_enter_per_cell_mat,"omitnan")/(sqrt(length(frac_ant_enter_per_cell_mat)));
  302. sem_entered_ret(k_choose)=std(frac_ret_enter_per_cell_mat,"omitnan")/(sqrt(length(frac_ret_enter_per_cell_mat)));
  303. sem_starts_in(k_choose)=std(frac_starts_in_per_cell_mat,"omitnan")/(sqrt(length(frac_starts_in_per_cell_mat)));
  304. sem_exits_ant(k_choose)=std(frac_ant_exits_per_cell_mat,"omitnan")/(sqrt(length(frac_ant_exits_per_cell_mat)));
  305. sem_exits_ret(k_choose)=std(frac_ret_exits_per_cell_mat,"omitnan")/(sqrt(length(frac_ret_exits_per_cell_mat)));
  306. sem_stays_in(k_choose)=std(frac_stays_in_per_cell_mat,"omitnan")/(sqrt(length(frac_stays_in_per_cell_mat)));
  308. save([save_dir, 'total_enters_1um'],'tot_enters_per_cell_all_conds');
  309. save([save_dir, 'frac_ant_ent_1um'],'frac_ant_enter_per_cell_all_conds');
  310. save([save_dir, 'frac_ret_ent_1um'],'frac_ret_enter_per_cell_all_conds');
  311. save([save_dir, 'start_in_1um'],'frac_starts_in_per_cell_all_conds');
  312. save([save_dir, 'frac_ant_ex_1um'],'frac_ant_exits_per_cell_all_conds');
  313. save([save_dir, 'frac_ret_ex_1um'],'frac_ret_exits_per_cell_all_conds');
  314. save([save_dir, 'frac_stay_in_1um'],'frac_stays_in_per_cell_all_conds');
  316. tot_enters_per_cell=[];
  317. tot_ant_exits_per_cell=[];
  318. tot_ret_exits_per_cell=[];
  319. tot_stays_in_per_cell=[];
  320. tot_ant_enter_per_cell=[];
  321. tot_ret_enter_per_cell=[];
  322. tot_starts_in_per_cell=[];
  324. frac_ant_exits_per_cell=[];
  325. frac_ret_exits_per_cell=[];
  326. frac_stays_in_per_cell=[];
  327. frac_ant_enter_per_cell=[];
  328. frac_ret_enter_per_cell=[];
  329. frac_starts_in_per_cell=[];
  331. end
  333. figure('Name','ant_ret_stat_exit_bar','NumberTitle','off'), hold on, %4 conditions
  334. exit_fractions=[avg_exits_ant(1) avg_exits_ant(2) avg_exits_ant(3) avg_exits_ant(4); avg_exits_ret(1) avg_exits_ret(2) avg_exits_ret(3) avg_exits_ret(4); avg_stays_in(1) avg_stays_in(2) avg_stays_in(3) avg_stays_in(4)];
  335. exit_err_bars=[sem_exits_ant(1) sem_exits_ant(2) sem_exits_ant(3) sem_exits_ant(4); sem_exits_ret(1) sem_exits_ret(2) sem_exits_ret(3) sem_exits_ret(4); sem_stays_in(1) sem_stays_in(2) sem_stays_in(3) sem_stays_in(4)];
  336. b1=bar(exit_fractions,'grouped');
  337. b1(1).FaceColor=colour_30Q;
  338. b1(2).FaceColor=colour_45Q;
  339. b1(3).FaceColor=colour_65Q;
  340. b1(4).FaceColor=colour_81Q;
  341. b1(1).FaceAlpha=0.5;
  342. b1(2).FaceAlpha=0.5;
  343. b1(3).FaceAlpha=0.5;
  344. b1(4).FaceAlpha=0.5;
  345. hold on
  346. scatter(0.7273-0.02+0.05*rand(numel(frac_ant_exits_per_cell_all_conds{1}),1),frac_ant_exits_per_cell_all_conds{1},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_30Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  347. scatter(0.9091-0.02+0.05*rand(numel(frac_ant_exits_per_cell_all_conds{2}),1),frac_ant_exits_per_cell_all_conds{2},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_45Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  348. scatter(1.0909-0.02+0.05*rand(numel(frac_ant_exits_per_cell_all_conds{3}),1),frac_ant_exits_per_cell_all_conds{3},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_65Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  349. scatter(1.2727-0.02+0.05*rand(numel(frac_ant_exits_per_cell_all_conds{4}),1),frac_ant_exits_per_cell_all_conds{4},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_81Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  350. scatter(1.7273-0.02+0.05*rand(numel(frac_ret_exits_per_cell_all_conds{1}),1),frac_ret_exits_per_cell_all_conds{1},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_30Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  351. scatter(1.9091-0.02+0.05*rand(numel(frac_ret_exits_per_cell_all_conds{2}),1),frac_ret_exits_per_cell_all_conds{2},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_45Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  352. scatter(2.0909-0.02+0.05*rand(numel(frac_ret_exits_per_cell_all_conds{3}),1),frac_ret_exits_per_cell_all_conds{3},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_65Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  353. scatter(2.2727-0.02+0.05*rand(numel(frac_ret_exits_per_cell_all_conds{4}),1),frac_ret_exits_per_cell_all_conds{4},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_81Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  354. scatter(2.7273-0.02+0.05*rand(numel(frac_stays_in_per_cell_all_conds{1}),1),frac_stays_in_per_cell_all_conds{1},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_30Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  355. scatter(2.9091-0.02+0.05*rand(numel(frac_stays_in_per_cell_all_conds{2}),1),frac_stays_in_per_cell_all_conds{2},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_45Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  356. scatter(3.0909-0.02+0.05*rand(numel(frac_stays_in_per_cell_all_conds{3}),1),frac_stays_in_per_cell_all_conds{3},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_65Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  357. scatter(3.2727-0.02+0.05*rand(numel(frac_stays_in_per_cell_all_conds{4}),1),frac_stays_in_per_cell_all_conds{4},25,'MarkerFaceColor','none','MarkerEdgeColor',colour_81Q,'MarkerEdgeAlpha',0.5,'LineWidth',2);
  358. hold on
  359. [ngroups,nbars]= size(exit_fractions);
  360. groupwidth= min(0.8, nbars/(nbars + 1.5));
  361. % Based on barweb.m by Bolu Ajiboye from MATLAB File Exchange
  362. for i = 1:nbars
  363. % Calculate center of each bar
  364. x = (1:ngroups) - groupwidth/2 + (2*i-1) * groupwidth / (2*nbars);
  365. errorbar(x, exit_fractions (:,i), exit_err_bars (:,i), 'k', 'linestyle', 'none','LineWidth',2);
  366. end
  367. xticks([1 2 3]);
  368. xticklabels({'Anterograde flux', 'Retrograde flux','Stationary'});
  369. ylabel('Fraction of Cargoes that enter the box');
  370. publication_fig(0,0,1);
  372. figure('Name','ant_ret_stat_entry_bar','NumberTitle','off'), hold on, %4 conditions
  373. enter_fractions=[avg_entered_ant(1) avg_entered_ant(2) avg_entered_ant(3) avg_entered_ant(4); avg_entered_ret(1) avg_entered_ret(2) avg_entered_ret(3) avg_entered_ret(4); avg_starts_in(1) avg_starts_in(2) avg_starts_in(3) avg_starts_in(4)];
  374. enter_err_bars=[sem_entered_ant(1) sem_entered_ant(2) sem_entered_ant(3) sem_entered_ant(4); sem_entered_ret(1) sem_entered_ret(2) sem_entered_ret(3) sem_entered_ret(4); sem_starts_in(1) sem_starts_in(2) sem_starts_in(3) sem_starts_in(4)];
  375. b2=bar(enter_fractions,'grouped');
  376. b2(1).FaceColor=colour_30Q;
  377. b2(2).FaceColor=colour_45Q;
  378. b2(3).FaceColor=colour_65Q;
  379. b2(4).FaceColor=colour_81Q;
  380. b2(1).FaceAlpha=0.5;
  381. b2(2).FaceAlpha=0.5;
  382. b2(3).FaceAlpha=0.5;
  383. b2(4).FaceAlpha=0.5;
  384. hold on
  385. [ngroups,nbars]= size(enter_fractions);
  386. groupwidth= min(0.8, nbars/(nbars + 1.5));
  387. % Based on barweb.m by Bolu Ajiboye from MATLAB File Exchange
  388. for i = 1:nbars
  389. % Calculate center of each bar
  390. x = (1:ngroups) - groupwidth/2 + (2*i-1) * groupwidth / (2*nbars);
  391. errorbar(x, enter_fractions (:,i), enter_err_bars (:,i), 'k', 'linestyle', 'none','LineWidth',2);
  392. end
  393. xticks([1 2 3]);
  394. xticklabels({'Anterograde flux', 'Retrograde flux','Stationary'});
  395. ylabel('Fraction of Cargoes that enter the box');
  396. publication_fig(0,0,1);
  397. xticks([1 2 3]);
  398. xticklabels({'Somal Entry', 'Distal Entry','Started In'});
  399. ylabel('Fraction of Cargoes that enter the box');
  400. publication_fig(0,0,1);
  402. figure('Name','ant_vs_ret_entry','NumberTitle','off'), hold on, %4 conditions
  403. ant_vs_ret_fractions=[avg_entered_ant(1)/avg_entered_ret(1) avg_entered_ant(2)/avg_entered_ret(2) avg_entered_ant(3)/avg_entered_ret(3) avg_entered_ant(4)/avg_entered_ret(4)];
  404. b3=bar(ant_vs_ret_fractions);
  405. xticklabels({'30Q','45Q', '65Q', '81Q'});
  406. % xticklabels({'30Q','30Q IFγ', '81Q', '81Q IFγ'});
  407. ylabel('Anterograde/Retrograde Flux Entry');
  408. publication_fig(0,0,1);
  410. figure('Name','ant_vs_ret_exit','NumberTitle','off'), hold on, %4 conditions
  411. ant_vs_ret_fractions=[avg_exits_ant(1)/avg_exits_ret(1) avg_exits_ant(2)/avg_exits_ret(2) avg_exits_ant(3)/avg_exits_ret(3) avg_exits_ant(4)/avg_exits_ret(4)];
  412. b3=bar(ant_vs_ret_fractions);
  413. xticklabels({'30Q','45Q', '65Q', '81Q'});
  414. % xticklabels({'30Q','30Q IFγ', '81Q', '81Q IFγ'});
  415. ylabel('Anterograde/Retrograde Flux Exit');
  416. publication_fig(0,0,1);
  418. figure('Name','total_entered','NumberTitle','off'), hold on, %4 conditions
  419. scatter(1-0.125+0.25*rand(numel(total_enters_per_cell_value_all_conds{1}),1),total_enters_per_cell_value_all_conds{1},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2), hold on;
  420. scatter(2-0.125+0.25*rand(numel(total_enters_per_cell_value_all_conds{2}),1),total_enters_per_cell_value_all_conds{2},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  421. scatter(3-0.125+0.25*rand(numel(total_enters_per_cell_value_all_conds{3}),1),total_enters_per_cell_value_all_conds{3},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  422. scatter(4-0.125+0.25*rand(numel(total_enters_per_cell_value_all_conds{4}),1),total_enters_per_cell_value_all_conds{4},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  423. plot(1,mean(total_enters_per_cell_value_all_conds{1},"omitnan"),'k_','MarkerSize',30);
  424. plot(2,mean(total_enters_per_cell_value_all_conds{2},"omitnan"),'k_','MarkerSize',30);
  425. plot(3,mean(total_enters_per_cell_value_all_conds{3},"omitnan"),'k_','MarkerSize',30);
  426. plot(4,mean(total_enters_per_cell_value_all_conds{4},"omitnan"),'k_','MarkerSize',30);
  427. xlim([0 5]);
  428. xticks([1 2 3 4]);
  429. publication_fig(0,0,1);
  430. hFig=findall(0,'type','figure');
  431. hLeg=findobj(hFig(1,1),'type','legend');
  432. set(hLeg,'visible','off');
  433. xticklabels({'30Q','45Q', '65Q', '81Q'});
  434. % xticklabels({'30Q','30Q IFγ', '81Q', '81Q IFγ'});
  435. ylabel('Total Entered per cell');
  436. publication_fig(0,0,1);
  438. exit_all{1}=frac_ant_exits_per_cell_all_conds{1};
  439. exit_all{2}=frac_ant_exits_per_cell_all_conds{2};
  440. exit_all{3}=frac_ant_exits_per_cell_all_conds{3};
  441. exit_all{4}=frac_ant_exits_per_cell_all_conds{4};
  442. exit_all{5}=frac_ret_exits_per_cell_all_conds{1};
  443. exit_all{6}=frac_ret_exits_per_cell_all_conds{2};
  444. exit_all{7}=frac_ret_exits_per_cell_all_conds{3};
  445. exit_all{8}=frac_ret_exits_per_cell_all_conds{4};
  446. exit_all{9}=frac_stays_in_per_cell_all_conds{1};
  447. exit_all{10}=frac_stays_in_per_cell_all_conds{2};
  448. exit_all{11}=frac_stays_in_per_cell_all_conds{3};
  449. exit_all{12}=frac_stays_in_per_cell_all_conds{4};
  451. figure('Name','ant_ret_stat_exit_scatter','NumberTitle','off'), hold on,
  452. % violin(exit_all,'facecolor',[colour_30Q;colour_45Q;colour_65Q;colour_81Q;colour_30Q;colour_45Q;colour_65Q;colour_81Q;colour_30Q;colour_45Q;colour_65Q;colour_81Q],'facealpha',0.3,'edgecolor','k','mc','k','medc','k--');
  453. scatter(1-0.125+0.25*rand(numel(exit_all{1}),1),exit_all{1},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2);
  454. scatter(2-0.125+0.25*rand(numel(exit_all{2}),1),exit_all{2},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  455. scatter(3-0.125+0.25*rand(numel(exit_all{3}),1),exit_all{3},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  456. scatter(4-0.125+0.25*rand(numel(exit_all{4}),1),exit_all{4},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  457. scatter(6-0.125+0.25*rand(numel(exit_all{5}),1),exit_all{5},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2);
  458. scatter(7-0.125+0.25*rand(numel(exit_all{6}),1),exit_all{6},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  459. scatter(8-0.125+0.25*rand(numel(exit_all{7}),1),exit_all{7},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  460. scatter(9-0.125+0.25*rand(numel(exit_all{8}),1),exit_all{8},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  461. scatter(11-0.125+0.25*rand(numel(exit_all{9}),1),exit_all{9},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2);
  462. scatter(12-0.125+0.25*rand(numel(exit_all{10}),1),exit_all{10},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  463. scatter(13-0.125+0.25*rand(numel(exit_all{11}),1),exit_all{11},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  464. scatter(14-0.125+0.25*rand(numel(exit_all{12}),1),exit_all{12},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  465. plot(1,mean(exit_all{1},"omitnan"),'k_','MarkerSize',20);
  466. plot(2,mean(exit_all{2},"omitnan"),'k_','MarkerSize',20);
  467. plot(3,mean(exit_all{3},"omitnan"),'k_','MarkerSize',20);
  468. plot(4,mean(exit_all{4},"omitnan"),'k_','MarkerSize',20);
  469. plot(6,mean(exit_all{5},"omitnan"),'k_','MarkerSize',20);
  470. plot(7,mean(exit_all{6},"omitnan"),'k_','MarkerSize',20);
  471. plot(8,mean(exit_all{7},"omitnan"),'k_','MarkerSize',20);
  472. plot(9,mean(exit_all{8},"omitnan"),'k_','MarkerSize',20);
  473. plot(11,mean(exit_all{9},"omitnan"),'k_','MarkerSize',20);
  474. plot(12,mean(exit_all{10},"omitnan"),'k_','MarkerSize',20);
  475. plot(13,mean(exit_all{11},"omitnan"),'k_','MarkerSize',20);
  476. plot(14,mean(exit_all{12},"omitnan"),'k_','MarkerSize',20);
  477. xlim([0 15]);
  478. ylabel('Fraction that enter the box');
  479. publication_fig(0,0,1);
  480. hFig=findall(0,'type','figure');
  481. hLeg=findobj(hFig(1,1),'type','legend');
  482. set(hLeg,'visible','off');
  485. enter_all{1}=frac_ant_enter_per_cell_all_conds{1};
  486. enter_all{2}=frac_ant_enter_per_cell_all_conds{2};
  487. enter_all{3}=frac_ant_enter_per_cell_all_conds{3};
  488. enter_all{4}=frac_ant_enter_per_cell_all_conds{4};
  489. enter_all{5}=frac_ret_enter_per_cell_all_conds{1};
  490. enter_all{6}=frac_ret_enter_per_cell_all_conds{2};
  491. enter_all{7}=frac_ret_enter_per_cell_all_conds{3};
  492. enter_all{8}=frac_ret_enter_per_cell_all_conds{4};
  493. enter_all{9}=frac_stays_in_per_cell_all_conds{1};
  494. enter_all{10}=frac_stays_in_per_cell_all_conds{2};
  495. enter_all{11}=frac_stays_in_per_cell_all_conds{3};
  496. enter_all{12}=frac_stays_in_per_cell_all_conds{4};
  498. figure('Name','ant_ret_stat_entry_scatter','NumberTitle','off'), hold on,
  499. % violin(enter_all,'facecolor',[colour_30Q;colour_45Q;colour_65Q;colour_81Q;colour_30Q;colour_45Q;colour_65Q;colour_81Q;colour_30Q;colour_45Q;colour_65Q;colour_81Q],'facealpha',0.3,'edgecolor','k','mc','k','medc','k--');
  500. scatter(1-0.125+0.25*rand(numel(enter_all{1}),1),enter_all{1},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2);
  501. scatter(2-0.125+0.25*rand(numel(enter_all{2}),1),enter_all{2},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  502. scatter(3-0.125+0.25*rand(numel(enter_all{3}),1),enter_all{3},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  503. scatter(4-0.125+0.25*rand(numel(enter_all{4}),1),enter_all{4},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  504. scatter(6-0.125+0.25*rand(numel(enter_all{5}),1),enter_all{5},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2);
  505. scatter(7-0.125+0.25*rand(numel(enter_all{6}),1),enter_all{6},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  506. scatter(8-0.125+0.25*rand(numel(enter_all{7}),1),enter_all{7},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  507. scatter(9-0.125+0.25*rand(numel(enter_all{8}),1),enter_all{8},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  508. scatter(11-0.125+0.25*rand(numel(enter_all{9}),1),enter_all{9},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2);
  509. scatter(12-0.125+0.25*rand(numel(enter_all{10}),1),enter_all{10},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  510. scatter(13-0.125+0.25*rand(numel(enter_all{11}),1),enter_all{11},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  511. scatter(14-0.125+0.25*rand(numel(enter_all{12}),1),enter_all{12},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  512. plot(1,mean(enter_all{1},"omitnan"),'k_','MarkerSize',20);
  513. plot(2,mean(enter_all{2},"omitnan"),'k_','MarkerSize',20);
  514. plot(3,mean(enter_all{3},"omitnan"),'k_','MarkerSize',20);
  515. plot(4,mean(enter_all{4},"omitnan"),'k_','MarkerSize',20);
  516. plot(6,mean(enter_all{5},"omitnan"),'k_','MarkerSize',20);
  517. plot(7,mean(enter_all{6},"omitnan"),'k_','MarkerSize',20);
  518. plot(8,mean(enter_all{7},"omitnan"),'k_','MarkerSize',20);
  519. plot(9,mean(enter_all{8},"omitnan"),'k_','MarkerSize',20);
  520. plot(11,mean(enter_all{9},"omitnan"),'k_','MarkerSize',20);
  521. plot(12,mean(enter_all{10},"omitnan"),'k_','MarkerSize',20);
  522. plot(13,mean(enter_all{11},"omitnan"),'k_','MarkerSize',20);
  523. plot(14,mean(enter_all{12},"omitnan"),'k_','MarkerSize',20);
  524. xlim([0 15]);
  525. ylabel('Fraction that enter the box');
  526. publication_fig(0,0,1);
  527. hFig=findall(0,'type','figure');
  528. hLeg=findobj(hFig(1,1),'type','legend');
  529. set(hLeg,'visible','off');
  531. ant_vs_ret_all{1}=frac_ant_exits_per_cell_all_conds{1}./frac_ret_exits_per_cell_all_conds{1};
  532. ant_vs_ret_all{2}=frac_ant_exits_per_cell_all_conds{2}./frac_ret_exits_per_cell_all_conds{2};
  533. ant_vs_ret_all{3}=frac_ant_exits_per_cell_all_conds{3}./frac_ret_exits_per_cell_all_conds{3};
  534. ant_vs_ret_all{4}=frac_ant_exits_per_cell_all_conds{4}./frac_ret_exits_per_cell_all_conds{4};
  536. figure('Name','ant_vs_ret_scatter','NumberTitle','off'), hold on,
  537. scatter(1-0.125+0.25*rand(numel(ant_vs_ret_all{1}),1),ant_vs_ret_all{1},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_30Q,'LineWidth',2), hold on;
  538. scatter(2-0.125+0.25*rand(numel(ant_vs_ret_all{2}),1),ant_vs_ret_all{2},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_45Q,'LineWidth',2);
  539. scatter(3-0.125+0.25*rand(numel(ant_vs_ret_all{3}),1),ant_vs_ret_all{3},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_65Q,'LineWidth',2);
  540. scatter(4-0.125+0.25*rand(numel(ant_vs_ret_all{4}),1),ant_vs_ret_all{4},25,'MarkerFaceColor','none','MarkerEdgeAlpha',0.3,'MarkerEdgeColor',colour_81Q,'LineWidth',2);
  541. plot(1,mean(ant_vs_ret_all{1}(~isinf(ant_vs_ret_all{1})), "omitnan"),'k_','MarkerSize',30);
  542. plot(2,mean(ant_vs_ret_all{2}(~isinf(ant_vs_ret_all{2})), "omitnan"),'k_','MarkerSize',30);
  543. plot(3,mean(ant_vs_ret_all{3}(~isinf(ant_vs_ret_all{3})), "omitnan"),'k_','MarkerSize',30);
  544. plot(4,mean(ant_vs_ret_all{4}(~isinf(ant_vs_ret_all{4})), "omitnan"),'k_','MarkerSize',30);
  545. ylabel('Fraction anterograde/retrograde');
  546. xlim([0 5]);
  547. xticks([1 2 3 4]);
  548. xticklabels({'30Q','45Q', '65Q', '81Q'});
  549. % xticklabels({'30Q','30Q IFγ', '81Q', '81Q IFγ'});
  550. publication_fig(0,0,1);
  551. hFig=findall(0,'type','figure');
  552. hLeg=findobj(hFig(1,1),'type','legend');
  553. set(hLeg,'visible','off');
  555. fileprefix='20230904_isoHD_lyso_flux_1um_box';
  556. %
  557. % tempdir_1 = '/Users/emilyprowse/Documents/McGill/Hendricks_Lab/Analysis/Motility_Stats/'; % Your destination folder
  558. % FolderName_1 = tempdir_1; % Your destination folder
  559. %Saving stats to a table
  560. % stats_table=table(titles, rg_stats, alpha_stats,dirbias_stats, proc_stats, diff_stats, stat_stats);
  561. % writetable(stats_table,fullfile(FolderName_1, [fileprefix,'.csv']),'WriteRowNames',true);
  563. tempdir = '/Users/emilyprowse/Documents/McGill/Hendricks_Lab/Analysis/isoHD_Neuron_motility/'; % Your destination folder
  564. FolderName = tempdir; % Your destination folder
  565. FigList = findobj(allchild(0), 'flat', 'Type', 'figure');
  566. for iFig = 1:length(FigList)
  567. FigHandle = FigList(iFig);
  568. FigName = get(FigHandle, 'Name');
  569. savefig(FigHandle, fullfile(FolderName, [append(fileprefix,FigName), '.fig']));
  570. saveas(FigHandle, fullfile(FolderName, [append(fileprefix,FigName), '.png']));
  571. end

TrackMate_to_kymo_and_flux_v4.m at commit 6f57e0a, no license · at the source

Overview

Authors: Emily NP Prowse1, Brooke A Turkalj1, Muriel Sébastien1,2, Lale Gursu1, Daniel Beaudet1, Jia Feng3, Chengqian Zhou1, Heidi M McBride4, Gary J Brouhard2,5, Mahmoud A Pouladi3, Adam G Hendricks1,5
ORCID iDs: Muriel Sébastien, Jia Feng, Adam G Hendricks
  1. Department of Bioengineering, McGill University, Montréal, QC, Canada
  2. Department of Biology, McGill University, Montréal, QC, Canada
  3. Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
  4. Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
  5. Centre de Recherche en Biologie Structurale, McGill University, Montréal, QC, Canada
Institutions: McGill University (Canada); University of British Columbia (Canada); Montreal Neurological Institute and Hospital (Canada)
Journal: iScience, volume 29, issue 6, article 115748
Dates: received 10 April 2024; accepted 13 April 2026; published online 17 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.115748 · PMID 42181265 · PMCID PMC13194641 · OpenAlex W4394785919
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.04.12.589210
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Statistics, Evoked potentials, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Molecular network, Molecular neuroscience, Cellular neuroscience
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Quebec Health Research Fund
Citations: not cited yet (Europe PMC); 73 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

hendricks-lab/emily-codes

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 6f57e0abe77a1ab2e09da46bb5c6023c589c09db, 30 April 2024
Languages: MATLAB (40)
Size: 47 files, 40 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (17 files), Optimization Toolbox (5 files), Signal Processing Toolbox (3 files), Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
41 files
At the source: github.com/hendricks-lab/emily-codes/releases

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;
  • 40 scripts, each with its path and the digest of its content;
  • 7 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

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: hendricks-lab/emily-codes
  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.isci.2026.115748.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 3 funders, 72 references.

Cite

This paper

Prowse, E. N., Turkalj, B. A., Sébastien, M., Gursu, L., Beaudet, D., Feng, J., Zhou, C., McBride, H. M., Brouhard, G. J., Pouladi, M. A., & Hendricks, A. G. (2026). Huntingtin polyglutamine expansions misdirect axonal transport by perturbing motor and adaptor recruitment. iScience, 29(6), 115748. https://doi.org/10.1016/j.isci.2026.115748

BibTeX

@article{prowse2026huntingtin,
author = {Prowse, Emily NP and Turkalj, Brooke A and Sébastien, Muriel and Gursu, Lale and Beaudet, Daniel and Feng, Jia and Zhou, Chengqian and McBride, Heidi M and Brouhard, Gary J and Pouladi, Mahmoud A and Hendricks, Adam G},
title = {{Huntingtin polyglutamine expansions misdirect axonal transport by perturbing motor and adaptor recruitment}},
journal = {iScience},
year = {2026},
month = apr,
volume = {29},
number = {6},
pages = {115748},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115748},
url = {https://doi.org/10.1016/j.isci.2026.115748},
pmid = {42181265},
pmcid = {PMC13194641}
}

RIS

TY - JOUR
AU - Prowse, Emily NP
AU - Turkalj, Brooke A
AU - Sébastien, Muriel
AU - Gursu, Lale
AU - Beaudet, Daniel
AU - Feng, Jia
AU - Zhou, Chengqian
AU - McBride, Heidi M
AU - Brouhard, Gary J
AU - Pouladi, Mahmoud A
AU - Hendricks, Adam G
TI - Huntingtin polyglutamine expansions misdirect axonal transport by perturbing motor and adaptor recruitment
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/04/17
VL - 29
IS - 6
SP - 115748
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115748
UR - https://doi.org/10.1016/j.isci.2026.115748
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.115748",
"type": "article-journal",
"title": "Huntingtin polyglutamine expansions misdirect axonal transport by perturbing motor and adaptor recruitment",
"container-title": "iScience",
"author": [
{
"family": "Prowse",
"given": "Emily NP"
},
{
"family": "Turkalj",
"given": "Brooke A"
},
{
"family": "Sébastien",
"given": "Muriel"
},
{
"family": "Gursu",
"given": "Lale"
},
{
"family": "Beaudet",
"given": "Daniel"
},
{
"family": "Feng",
"given": "Jia"
},
{
"family": "Zhou",
"given": "Chengqian"
},
{
"family": "McBride",
"given": "Heidi M"
},
{
"family": "Brouhard",
"given": "Gary J"
},
{
"family": "Pouladi",
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},
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"given": "Adam G"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "6",
"page": "115748",
"DOI": "10.1016/j.isci.2026.115748",
"PMID": "42181265",
"PMCID": "PMC13194641",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.115748",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
17
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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