DOI: 10.5061/dryad.5hqbzkhfd
title: Plastid and peroxisome movement tracks in the root cells of Arabidopsis thaliana

description: In movement analysis, correlated random walk (CRW) models often use
 so-called turning angles, which are measured relative to the previous
 movement direction. To segregate between different movement modes, hidden
 Markov models (HMMs) describe movements as piecewise stationary CRWs in
 which the distributions of turning angles and step sizes depend on the
 underlying state. This typically allows for the segregation of movement
 modes that show different movement speeds.  We show that in some
 cases, it may be interesting to investigate absolute angles, i.e., biased
 random walks (BRWs) instead of turning angles. In particular, while
 discrimination between states in the turning angle setting can only rely
 on movement speed, models with absolute angles can be used to discriminate
 between sections of different movement directions. A preprocessing
 algorithm is provided that enables the analysis of absolute angles in the
 existing R package moveHMM. In a data set of movements of cell
 organelles, models using not the turning angle but the absolute angle
 could capture interesting additional properties. Goodness of fit was
 increased for HMMs with absolute angles, and HMMs with absolute angles
 tended to choose a higher number of states, suggesting the existence and
 relevance of prominent directional changes in the present data
 set. These results suggest that models with absolute angles can
 provide important information in the analysis of movement patterns if the
 existence and frequency of directional changes is of biological
 importance.
description: # Plastid and peroxisome movement tracks in the root cell of Arabidopsis
 thaliana for analysing movement modes The analysis and description of
 movement processes of cell organelles in plants can yield important
 insights into their functional role within the cell's metabolism. For
 example, it is of interest to analyse different modes of movement such as
 cytoplasmic streaming or active transport along structures of the
 cytoskeleton. We therefore recorded the three dimensional movement
 processes of two types of cell organelles, plastids and peroxisomes, in
 the root cells of the plant Arabidopsis thaliana. ## Description of the
 data and file structure The data set consists of a description of data
 acquisition and post-processing in
 Plastid_peroxisome_tracks_details_recording_post_processing.pdf, four csv
 files, containing the movement tracks of the plastids and peroxisomes, and
 example code for application of the PCA to the organelle movement tracks
 in pca_of_movement_tracks.Rmd. ####
 Plastid\_peroxisome\_tracks\_details\_recording\_post\_processing.pdf This
 file contains a description of data acquisition, detection and tracking,
 post-processing and principal component analysis leading to the movement
 tracks of the cell organelles plastid and peroxisome as in the four csv
 data tables. #### plastid\_movement\_tracks\_3D\_2019.csv This is a csv
 table containing the movement tracks of the plastids over time in three
 dimensions. Each row describes the position of one plastid at one time
 point. The columns of the table are: * TRACK_ID: The ID of the movement
 track of one plastid. All rows with the same track id belong to the same
 plastid. * POSITION_X, POSITION_Y, POSITION_Z: The x,y,z coordinates of
 the plastid measured in micrometers relative to the boundary of the
 microscopy area. * POSITION_T: The time point at which the position was
 measured. Time in discrete steps of 4.3 seconds. Time is the same for all
 plastids, i.e. time point 1 of all plastids refers to the same time. *
 MEAN_INTENSITY, MEDIAN_INTENSITY, MIN_INTENSITY, MAX_INTENSITY,
 TOTAL_INTENSITY, STANDARD_DEVIATION: The mean, median, minimum, maximum
 and total intensity of the pixels within the detected spot, as well as the
 standard deviation of the intensity of these pixels. * CONTRAST: The
 contrast of the spot compared to the local backround. * SNR: The
 signal-to-noise ratio of the detected spot compared to the local
 backround. * ESTIMATED_DIAMETER: The estimated diameter of the detected
 spot. #### peroxisome\_movement\_tracks\_3D\_2023.csv This is a csv table
 containing the movement tracks of the peroxisomes over time in three
 dimensions. Each row describes the position of one peroxisome at one time
 point. The columns of the table are: * TRACK_ID: The ID of the movement
 track of one peroxisome. All rows with the same track id belong to the
 same peroxisome. * POSITION_X,POSITION_Y,POSITION_Z: The x,y,z coordinates
 of the peroxisome measured in micrometers relative to the boundary of the
 microscopy area. * POSITION_T: The time point at which the position was
 measured. Time in discrete steps of 4.3 seconds. Time is the same for all
 peroxisome, i.e. time point 1 of all peroxisome refers to the same time.
 #### plastid\_movement\_tracks\_pca\_2D\_2019.csv This is a csv table
 containing the movement tracks of the plastids from
 plastid_movement_tracks_3D_2019.csv projected onto the first and second
 principal component. The columns of the table are: * TRACK_ID: The ID of
 the movement track of one plastid. All rows with the same track id belong
 to the same plastid. * PC1, PC2: The coordinates of the spots in the first
 and second principal component. * POSITION_T: The time point at which the
 position was measured. Same as before. * VARPC1, VARPC2: The percentage of
 variance that can be explained in the first and second principal component
 respectively. #### peroxisome\_movement\_tracks\_pca\_2D\_2023.csv This is
 a csv table containing the movement tracks of the peroxisomes from
 peroxisome_movement_tracks_3D_2023.csv projected onto the first and second
 principal component. Columns as in
 plastid_movement_tracks_pca_2D_2019.csv. ## Code/Software ####
 pca\_of\_movement\_tracks.Rmd This is a R markdown file containing code to
 visualize the tracks from the four csv tables, as well as an example to
 show how the PCA was applied to the 3D movement tracks. The file was
 written in R Studio (version 2024.04.2 Build 764) with the R version 4.3.0
 (2023-04-21 ucrt -- "Already Tomorrow"). Additional R packages
 needed for running this code are: plotly (version 4.10.4), MASS (version
 7.3.58.4), plot3D (version 1.4.1) and colorspace (version 2.1.0), where
 the version refers to the version used to create the code. ####
 bivariate\_change\_point\_detection\_linear\_walk.R This R code file
 contains functions and examples for the bivariate change point detection
 in movement direction and speed of 2D cell organelle movement as in
 plastid_movement_tracks_pca_2D_2019.csv and
 peroxisome_movement_tracks_pca_2D_2023.csv. The change point test and
 algorithm are based on the Linear Walk model, a model for 2D movement
 presented in Plomer, S., Ernst, T., Gebhardt, P., Schleiff, E., Neininger,
 R., and Schneider, G.(2024a). Bivariate change point detection in movement
 direction and speed. arXiv preprint arXiv:2402.02489. The file was written
 in R Studio (version 2023.06.0 Build 421) with the R version 4.4.0
 (2024-04-24 ucrt -- "Puppy Cup"). Changes made since previously
 published version: The file bivariate_change_point_detection_linear_walk.R
 (software) was added. This R code file contains functions and examples for
 the bivariate change point detection in the movement direction and speed
 of 2D cell organelle movement as provided in the files
 peroxisome_movement_tracks_pca_2D_2023.csv and
 plastid_movement_tracks_pca_2D_2019.csv, based on the Linear Walk model as
 presented in the paper Plomer, S., Ernst, T., Gebhardt, P., Schleiff, E.,
 Neininger, R., and Schneider, G. (2024a). Bivariate change point detection
 in movement direction and speed. arXiv preprint arXiv:2402.02489.