Plastid and peroxisome movement tracks in the root cells of Arabidopsis thaliana
Data files
Sep 20, 2024 version files 2.34 MB
-
peroxisome_movement_tracks_3D_2023.csv
132.47 KB
-
peroxisome_movement_tracks_pca_2D_2023.csv
240.57 KB
-
plastid_movement_tracks_3D_2019.csv
961.88 KB
-
plastid_movement_tracks_pca_2D_2019.csv
996.66 KB
-
README.md
4.75 KB
Nov 04, 2024 version files 2.34 MB
-
peroxisome_movement_tracks_3D_2023.csv
132.47 KB
-
peroxisome_movement_tracks_pca_2D_2023.csv
240.57 KB
-
plastid_movement_tracks_3D_2019.csv
961.88 KB
-
plastid_movement_tracks_pca_2D_2019.csv
996.66 KB
-
README.md
6.12 KB
Abstract
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.
README: 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.