Last updated: 2025-10-15

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In this pages, there are examples on how to plot paths and use Cumulative Speed Allocation (CSA) tool to compare between populations. For more details on CSA, we refer to the paper Cook, Rayens, Do, Payne and McKinley (2025).

We provide plot functions for visualizing trajectories (please click to the function for more details) including:

Plot functions Description
plot_path_inferred() Plot the segmented trajectories after running CPLASS
plot_path_actual_and_inferred() Plot the actual overlap the inferred trajectories (for simulation data where we have the ground truth)
plot_csa() Plot Cummulative Speed Allocations

In this instructions, we continue using the lysosome transport in the Periphery region of cells from Payne’s lab. Please find the details on data collection here Rayens, Cook, McKinley, and Payne.

paths = readRDS("data/21PF/CPLASS_21PF_250_paths_sSIC_with_speed_pen.rds") #load your data here

source("code/plot_functions.R")
source("code/csa_functions.R")
library("tidyverse")
library("ggplot2")
library("patchwork")

plot_path_inferred

Plot a single path

path_index = c(49)
# # If you want to select a certain path, use these lines
path_list = list(paths[[path_index]])

dist_max = c()
t_max = c()
speed_max = c()
motor ="Periphery" #change the name for your data

for (i in 1:length(path_list)){
  path = path_list[[i]]$path_inferred
  segments = path_list[[i]]$segments_inferred
  speed_max = c(speed_max,max(path_list[[i]]$segments_inferred$speeds))
  
  dist_max = c(dist_max,max(c(max(path$x)-min(path$x)),
                            max((path$y)-min(path$y))))
  t_max = c(t_max,max(path$t) - min(path$t))
}
xy_width = 1.2*max(dist_max)
t_lim = c(0,ceiling(max(t_max)))
max_speed = ceiling(max(speed_max))

for (i in 1:length(path_list)){
  if (i %% 20 == 0){
    print(paste("Working on path",i))
  }
  
  dashboard = plot_path_inferred(path_list[[i]],xy_width,t_lim, motor, max_speed, title_ind = path_index, show_time_changes = TRUE)
  
  print(dashboard)
  
}

Version Author Date
380b9e2 Ldo3 2025-10-15

plot_path_actual_and_inferred

This function is useful for evaluation the performance of CPLASS inferred trajectories on the simulation data sets.

Plot a single path

paths = readRDS("data/sim13CKP/Sim13_CKP_25Hz_cplass_with_speed_theory.rds")
path_index = c(15)
# # If you want to select a certain path, use these lines
path_list = list(paths[[path_index]])

dist_max = c()
t_max = c()
speed_max = c()
motor ="Simulation: Base" #change the name for your data

for (i in 1:length(path_list)){
  path = path_list[[i]]$path_inferred
  segments = path_list[[i]]$segments_inferred
  speed_max = c(speed_max,max(path_list[[i]]$segments_inferred$speeds))
  
  dist_max = c(dist_max,max(c(max(path$x)-min(path$x)),
                            max((path$y)-min(path$y))))
  t_max = c(t_max,max(path$t) - min(path$t))
}
xy_width = 1.2*max(dist_max)
t_lim = c(0,ceiling(max(t_max)))
max_speed = ceiling(max(speed_max))

for (i in 1:length(path_list)){
  if (i %% 20 == 0){
    print(paste("Working on path",i))
  }
  
  dashboard = plot_path_actual_and_inferred(path_list[[i]],xy_width,t_lim)
  
  print(dashboard)
  
}
Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_path()`).
Removed 2 rows containing missing values or values outside the scale range
(`geom_path()`).

Version Author Date
380b9e2 Ldo3 2025-10-15

plot_csa

The following is the code to reproduce the Figure 8 in CPLASS paper (will be available soon) where we create CSA plots one for comparing lysosomal transport in perinuclear and periphery regions and one for comparing the sucrose-treated groups restricted to the periphery rgion of the cell. Please see Rayens, Cook, McKinley, and Payne for more details on the data, and Cook, Rayens, Do, Payne , McKinley for the calculation of CSA.

pdf_output = TRUE
folder_name = c("data/21PF/", "data/20PFS_and_PFL/")
real_data_file1 = paste0(folder_name[1],"CPLASS_21PN_250_paths_sSIC_with_speed_pen.rds")
real_data_file2 = paste0(folder_name[1],"CPLASS_21PF_250_paths_sSIC_with_speed_pen.rds")

real_path_list1 = readRDS(real_data_file1)
real_path_list2 = readRDS(real_data_file2)


cohort_list = c("Control Perinuclear", 
                "Control Periphery")
color_list = c("Control Perinuclear" = "#1B9E77", 
               "Control Periphery" ="#377EB8") 

pdf_output = TRUE

##### Create Segment Summary ####
segment_summary = summarize_segments_inferred(real_path_list1,cohort_list[1])
segment_summary = bind_rows(segment_summary, 
summarize_segments_inferred(real_path_list2,cohort_list[2]))

max_speed = min(1.2,max(segment_summary$speeds))

# Assumes theta is the same for all frame rates involved
speed_mesh = seq(0,max_speed,length = 200)
ds = speed_mesh[2] - speed_mesh[1]
subsample_size = 200
num_subsamples = 30

csa1 = tibble()

#### Bootstrap CSA for Real Data 1 ####
for (m in 1:num_subsamples){
    subsample = sample(1:length(real_path_list1),subsample_size,replace = TRUE)
    path_subsample = list()
    for (i in 1:length(subsample)){
      path_subsample[[i]] = real_path_list1[[subsample[i]]]
    }
    this_segments_summary = summarize_segments_inferred(path_subsample,"Sample")
    this_csa = compute_csa(this_segments_summary, speed_mesh)$csa
    csa1 = bind_rows(csa1,
                tibble(
                  s = speed_mesh,
                  csa = this_csa,
                  dcsa = c(diff(this_csa)/ds,0),
                  error = NA,
                  error_pct = NA,
                  label = cohort_list[1],
                  Hz = 20,
                  subsample = m
                ))
}


csa2 = tibble()
#### Bootstrap CSA for Real Data 2 ####
for (m in 1:num_subsamples){
    subsample = sample(1:length(real_path_list2),subsample_size,replace = TRUE)
    path_subsample = list()
    for (i in 1:length(subsample)){
      path_subsample[[i]] = real_path_list2[[subsample[i]]]
    }
    this_segments_summary = summarize_segments_inferred(path_subsample,"Sample")
    this_csa = compute_csa(this_segments_summary, speed_mesh)$csa
    csa2 = bind_rows(csa2,
                tibble(
                  s = speed_mesh,
                  csa = this_csa,
                  dcsa = c(diff(this_csa)/ds,0),
                  error = NA,
                  error_pct = NA,
                  label = cohort_list[2],
                  Hz = 20,
                  subsample = m
                ))
}



csa_both = bind_rows(csa1,csa2)

csa_both = csa_both %>% mutate(cohort = paste(label,subsample))

p_csa_compare1 = ggplot(csa_both %>% filter(label == cohort_list[1] | 
                                      label == cohort_list[2]))+
  geom_line(aes(x = s, y = csa, col = label, group = cohort))+
  # ggtitle("Lysosome Periphery Large vs Small")+
  ylab("Cumulative Speed Allocation")+xlab(expression(paste("Speed (",mu,"m/sec)")))+
  scale_color_manual(values = color_list)+
  theme_minimal()+
     theme(
    legend.position = c(0.8, 0.2),  # Adjust position inside the plot
    legend.background = element_rect(fill = "white", color = "black")
  )+labs(color = NULL)
Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
print(p_csa_compare1)

Version Author Date
380b9e2 Ldo3 2025-10-15 
pdf_output = TRUE
real_data_file1 = paste0(folder_name[2],"CPLASS_20PFL_250_paths_sSIC_with_speed_pen.rds")
real_data_file2 = paste0(folder_name[2],"CPLASS_20PFS_250_paths_sSIC_with_speed_pen.rds")

real_path_list1 = readRDS(real_data_file1)
real_path_list2 = readRDS(real_data_file2)

cohort_list = c("Sucrose Large", 
                "Sucrose Small")
color_list = c("Sucrose Large" = "#1B9E77", 
               "Sucrose Small" ="#377EB8") 

pdf_output = TRUE


##### Create Segment Summary ####
segment_summary = summarize_segments_inferred(real_path_list1,cohort_list[1])
segment_summary = bind_rows(segment_summary,
                            summarize_segments_inferred(real_path_list2,cohort_list[2]))


max_speed = min(1.2,max(segment_summary$speeds))

# Assumes theta is the same for all frame rates involved
speed_mesh = seq(0,max_speed,length = 200)
ds = speed_mesh[2] - speed_mesh[1]
subsample_size = 200
num_subsamples = 30

csa1 = tibble()
#### Bootstrap CSA for Real Data 1 ####
for (m in 1:num_subsamples){
    subsample = sample(1:length(real_path_list1),subsample_size,replace = TRUE)
    path_subsample = list()
    for (i in 1:length(subsample)){
      path_subsample[[i]] = real_path_list1[[subsample[i]]]
    }
    this_segments_summary = summarize_segments_inferred(path_subsample,"Sample")
    this_csa = compute_csa(this_segments_summary, speed_mesh)$csa
    csa1 = bind_rows(csa1,
                tibble(
                  s = speed_mesh,
                  csa = this_csa,
                  dcsa = c(diff(this_csa)/ds,0),
                  error = NA,
                  error_pct = NA,
                  label = cohort_list[1],
                  Hz = 20,
                  subsample = m
                ))
}


csa2 = tibble()
#### Bootstrap CSA for Real Data 2 ####
for (m in 1:num_subsamples){
    subsample = sample(1:length(real_path_list2),subsample_size,replace = TRUE)
    path_subsample = list()
    for (i in 1:length(subsample)){
      path_subsample[[i]] = real_path_list2[[subsample[i]]]
    }
    this_segments_summary = summarize_segments_inferred(path_subsample,"Sample")
    this_csa = compute_csa(this_segments_summary, speed_mesh)$csa
    csa2 = bind_rows(csa2,
                tibble(
                  s = speed_mesh,
                  csa = this_csa,
                  dcsa = c(diff(this_csa)/ds,0),
                  error = NA,
                  error_pct = NA,
                  label = cohort_list[2],
                  Hz = 20,
                  subsample = m
                ))
}


csa_both = bind_rows(csa1,csa2)
csa_both = csa_both %>% mutate(cohort = paste(label,subsample))

p_csa_compare2 = ggplot(csa_both %>% filter(label == cohort_list[1] | 
                                      label == cohort_list[2]))+
  geom_line(aes(x = s, y = csa, col = label, group = cohort))+
  # ggtitle("Lysosome Periphery Large vs Small")+
  ylab("Cumulative Speed Allocation")+xlab(expression(paste("Speed (",mu,"m/sec)")))+
  scale_color_manual(values = color_list)+
  theme_minimal()+
     theme(
    legend.position = c(0.85, 0.2),  # Adjust position inside the plot
    legend.background = element_rect(fill = "white", color = "black")
  )+labs(color = NULL)
print(p_csa_compare2)

Version Author Date
380b9e2 Ldo3 2025-10-15 
p_csa_full = (p_csa_compare1 + p_csa_compare2)

if (pdf_output == TRUE){
  outfile = paste0("docs/output/Figure_CSA_compare_PN_PF_PFL_PFS.pdf")
  pdf(outfile,onefile = TRUE,width=9.5,height=3)
}

print(p_csa_full)

if (pdf_output == TRUE){
  dev.off()
}
quartz_off_screen 
                2 

sessionInfo()
R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Ventura 13.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.3.2 lubridate_1.9.4 forcats_1.0.0   stringr_1.5.1  
 [5] dplyr_1.1.4     purrr_1.1.0     readr_2.1.5     tidyr_1.3.1    
 [9] tibble_3.3.0    ggplot2_3.5.2   tidyverse_2.0.0 workflowr_1.7.2

loaded via a namespace (and not attached):
 [1] sass_0.4.10        generics_0.1.4     stringi_1.8.7      hms_1.1.3         
 [5] digest_0.6.37      magrittr_2.0.3     timechange_0.3.0   evaluate_1.0.4    
 [9] grid_4.5.1         RColorBrewer_1.1-3 fastmap_1.2.0      rprojroot_2.1.1   
[13] jsonlite_2.0.0     processx_3.8.6     whisker_0.4.1      ps_1.9.1          
[17] promises_1.3.3     httr_1.4.7         scales_1.4.0       jquerylib_0.1.4   
[21] cli_3.6.5          crayon_1.5.3       rlang_1.1.6        withr_3.0.2       
[25] cachem_1.1.0       yaml_2.3.10        tools_4.5.1        tzdb_0.5.0        
[29] httpuv_1.6.16      vctrs_0.6.5        R6_2.6.1           lifecycle_1.0.4   
[33] git2r_0.36.2       fs_1.6.6           pkgconfig_2.0.3    callr_3.7.6       
[37] pillar_1.11.0      bslib_0.9.0        later_1.4.3        gtable_0.3.6      
[41] glue_1.8.0         Rcpp_1.1.0         xfun_0.53          tidyselect_1.2.1  
[45] rstudioapi_0.17.1  knitr_1.50         farver_2.1.2       htmltools_0.5.8.1 
[49] labeling_0.4.3     rmarkdown_2.29     compiler_4.5.1     getPass_0.2-4