Last updated: 2025-10-16

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Knit directory: CPLASS/

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In this page, we provided example on running CPLASS for a single path or a collection of paths.

1. Loading the library and source codes

Firstly, let’s load all needed library and source codes of CPLASS

library("Rlab")
library("matrixcalc")
library("pracma")
library("limSolve")
library("tidyverse")
library("ggplot2")
library("patchwork")
library("foreach")
library("doParallel")
library("glmnet")
library("earth")
library(ggthemes)
library(readr)
source("code/CPLASS.R")
source("code/plot_functions.R")
source("code/csa_functions.R")
source("code/PENALTY.R")

2. Running CPLASS

CPLASS requires trajectory data that include observed time points and 2-D spatial coordinates (x- and y-positions). Each observation corresponds to the position of a tracked particle at a given time.

Example Dataset

The dataset provided here contains lysosome trajectories collected in the peripheral regions of cells by Payne’s Lab. These data represent the movement of intracellular cargos (lysosomes) driven by molecular motors, which undergo intermittent transitions between transport states.

Lysosome trajectory data columns
Column Description
t Observed time (in seconds or frame number)
x X-position of the particle
y Y-position of the particle 
data = read_csv("data/Real_21_Periphery.csv") #load your data here

In the following, let’s run CPLASS for a single path and plot the trajectory.

CPLASS

See CPLASS for full details

# Step 1: what does your path look like?
path = data %>% filter(index_path == 1)
t = path$t
x = path$x
y = path$y

# Step 2: running CPLASS
cplass = CPLASS(t,x,y,iter_max = 2000) #notice that I am using the default but you can change any argument to adjust the MCMC steps, burn-in steps, speed limit threshold, turn-on or turn off the speed penalty, and so on

# Step 3: save the output as .rds file
saveRDS(cplass,file="output/Example_path_1_Periphery.rds")

Visualize the results

i=1
motor = "Perinuclear Lysosome" #enter the name you want to display in the figure
speed_max = c()
speed_max = c(speed_max,max(cplass$segments_inferred$speeds))
max_speed = ceiling(max(speed_max))
plot_path_inferred(cplass,motor = motor,max_speed = max_speed,t_lim = c(0,30))

Version Author Date
380b9e2 Ldo3 2025-10-15

The black line denotes the trajectory in the x-y coordinate plane and the time series for each coordinate. The blue line in each figure represents the inferred anchor position. Each green panel denotes a Motile segment, and each pink panel denotes a Stationary segment. Here, we used a threshold of \(0.1\mu\)m/s to label a segment as stationary/motile.

CPLASS_paths

# We will run on the 21PF data set with 3 paths.
subdata = data%>%filter(index_path%in%c(1,2,3)) #contains 3 paths
cplass_path = list()
for (i in 1:3)
{
     path = subdata %>% filter(index_path == i)
     t = path$t
     x = path$x
     y = path$y
     time_rate = min(diff(t))
     cplass_path[[i]] = CPLASS(t,x,y,iter_max = 2000)
}
saveRDS(cplass_path,file="output/Example_3_paths_Periphery.rds")

Visualization

Since we have a lot of paths here, it is better to save the plots into a pdf file to see them all together. The follow code will help us to do so. The output looks like this Real21PF_gallery.

path_list = cplass_path


file_list = c("21PF")
filestub = paste0("output/Real",file_list[1])
outfile = paste0(filestub,"_gallery.pdf")

 
motor = "Perinuclear Lysosomes" #change the name to the name of your data

pdf_output = TRUE

if (pdf_output == TRUE){
  pdf(outfile,onefile = TRUE,width=7,height=5)
}

dist_max = c()
t_max = c()
speed_max = c()

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)
  print(dashboard)

  
}

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

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] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] kableExtra_1.4.0  knitr_1.50        igraph_2.1.4      here_1.0.1       
 [5] ggthemes_5.1.0    earth_5.3.4       plotmo_3.6.4      plotrix_3.8-4    
 [9] Formula_1.2-5     glmnet_4.1-10     Matrix_1.7-3      doParallel_1.0.17
[13] iterators_1.0.14  foreach_1.5.2     patchwork_1.3.2   lubridate_1.9.4  
[17] forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4       purrr_1.1.0      
[21] readr_2.1.5       tidyr_1.3.1       tibble_3.3.0      ggplot2_3.5.2    
[25] tidyverse_2.0.0   limSolve_2.0.1    pracma_2.4.4      matrixcalc_1.0-6 
[29] Rlab_4.0          workflowr_1.7.2  

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   viridisLite_0.4.2  farver_2.1.2       fastmap_1.2.0     
 [5] promises_1.3.3     digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] survival_3.8-3     processx_3.8.6     magrittr_2.0.3     compiler_4.5.1    
[13] rlang_1.1.6        sass_0.4.10        tools_4.5.1        yaml_2.3.10       
[17] labeling_0.4.3     bit_4.6.0          xml2_1.4.0         RColorBrewer_1.1-3
[21] withr_3.0.2        grid_4.5.1         git2r_0.36.2       scales_1.4.0      
[25] MASS_7.3-65        cli_3.6.5          crayon_1.5.3       rmarkdown_2.29    
[29] generics_0.1.4     rstudioapi_0.17.1  httr_1.4.7         tzdb_0.5.0        
[33] cachem_1.1.0       splines_4.5.1      vctrs_0.6.5        jsonlite_2.0.0    
[37] callr_3.7.6        hms_1.1.3          bit64_4.6.0-1      systemfonts_1.2.3 
[41] jquerylib_0.1.4    glue_1.8.0         codetools_0.2-20   ps_1.9.1          
[45] stringi_1.8.7      gtable_0.3.6       shape_1.4.6.1      later_1.4.3       
[49] quadprog_1.5-8     pillar_1.11.0      htmltools_0.5.8.1  R6_2.6.1          
[53] textshaping_1.0.1  rprojroot_2.1.1    vroom_1.6.5        lpSolve_5.6.23    
[57] evaluate_1.0.4     lattice_0.22-7     httpuv_1.6.16      bslib_0.9.0       
[61] Rcpp_1.1.0         svglite_2.2.1      whisker_0.4.1      xfun_0.53         
[65] fs_1.6.6           getPass_0.2-4      pkgconfig_2.0.3