DOI: 10.5061/dryad.7wm37pvzp
title: Data from: Cellects, a software to quantify cell expansion and motion

description: Automated quantification offers unique opportunities to study biological
 phenomena, increasing reproducibility, replicability, accuracy, and
 throughput, while reducing observer biases. We present Cellects, a tool to
 quantify growth and motion in 2D. This software operates with image
 sequences containing specimens growing and moving on an immobile flat
 surface. Its user-friendly interface makes it easy to adjust the
 quantification parameters to cover a wide range of species and conditions,
 and includes tools to validate the results and correct mistakes if
 necessary. The software provides the region covered by the specimens at
 each point of time, as well as many geometrical descriptors that
 characterize it. We validated Cellects with Physarum polycephalum, which
 is particularly difficult to detect because of its complex shape and
 internal heterogeneity. This validation covered five different conditions
 with different background and lighting, and found Cellects to be highly
 accurate in all cases. Cellects’ main strengths are its broad scope of
 action, automated computation of a variety of geometrical descriptors,
 easy installation and user-friendly interface.
description: Methodological information related to the analysis is available
 in the Cellects GitHub repository: https://github.com/Aurele-B/Cellects
description: # Data from: Cellects, a software to quantify cell expansion and motion
 This README contains the information to install the Cellects software, to
 use it, and to reproduce the results of the paper describing it. ##
 Installation and documentation Find Cellects'
 [documentation](https://aurele-b.github.io/Cellects/) and
 [readme](https://github.com/Aurele-B/Cellects) on github. ## Content This
 repository contains one zip file allowing to reproduce the results of the
 paper describing Cellects: * Cellects_data.zip ### Files structure ```
 Cellects_data.zip |- cellular_dynamics_and_morphologies_across_systems/ |
 |- figure1.py | |- fig1/ | |- Cobo2018/ | |- Guiet2022/ | |- physarum/ |-
 ground_truth_accuracy/ | |- figure2_ground_truth_tests.py | |- fig2/ |-
 a_posteriori_accuracy/ | |- CellectsPaper | | |- src/ | | | |- cellects/ |
 | | |- config | | | |- core | | | |- image_analysis | | | |-
 paper_illustrations | | | |- utils | | | |- __main__.py | |-
 paper_figs_jpg/ | |- paper_figs_tif/ | |- quail_development/ | |-
 slime_choice_on_blackpaper/ | |- slime_circular_on_leds/ | |-
 slime_circular_on_whitepaper/ | |- slime_lengthen_on_leds/ | |-
 slime_square_on_leds/ | |- Validation_images/ | | |-
 slime_choice_on_blackpaper/ | | |- slime_circular_on_leds/ | | |-
 slime_circular_on_whitepaper/ | | |- slime_lengthen_on_leds/ | | |-
 slime_square_on_leds/ ``` ### Reproducing Figure 1 * Cellects version:
 v1.0.1 * Subfolder location:
 cellular_dynamics_and_morphologies_across_systems * Additional
 information: each subfolder contains a.md file to describe what to do for
 each data set. * Python file to run: Figure1.py ### Reproducing the ground
 truth analysis of Figure 2 * Cellects version: v1.0.1 * Subfolder
 location: ground_truth_accuracy * Python file to run:
 Figure2_ground_truth_tests.py ### Reproducing the a posteriori analysis of
 Figure 2 * Cellects version: not versioned, saved in the CellectsPaper
 folder * Subfolder location: a_posteriori_accuracy * Additional
 information: README.md in the CellectsPaper folder (specific installation)
 * Python file to run: Cellects paper.py ### Detailed files content ####
 Files content of the cellular dynamics and morphologies across systems
 Cobo2018 contains: * replicate_fungi_analysis.md: a file explaining where
 to download the 'Fungus' data and how to analyze it using
 Cellects. * cellects_settings.json: a parameter file allowing users to run
 the analysis without having to change any parameter of Cellects' GUI.
 * cellects_data.h5: a data file allowing users to run the video tracking
 without having to run the image analysis using Cellects' GUI.
 Guiet2018 contains: * replicate_hela_analysis.md: a file explaining where
 to download the 'HeLa' data and how to analyze it using
 Cellects. It also contains a small script to convert the 5 channels .tif
 file into an image stack (usable by Cellects). * cellects_settings.json: a
 parameter file allowing users to run the analysis without having to change
 any parameter of Cellects' GUI. * cellects_data.h5: a data file
 allowing users to run the video tracking without having to run the image
 analysis using Cellects' GUI. physarum contains: * physarum_rgb.h5:
 The first and 500th rgb image of the *P.polycephalum* example. *
 coord_physarum_t500_y1475_x1477.h5: The coordinates of the pixels covered
 by the cell at these frames. * coord_physarum_network_t500_y1475_x1477.h5:
 The coordinates of the pixels covered by the network at these frames. *
 coord_physarum_pseudopods_t500_y1475_x1477.h5: The coordinates of the
 pixels covered by the pseudopods at these frames. *
 edges_physarum_t720_y1475_x1477.csv: The identities of the two vertices
 linking each edge of the graph at all frames. *
 vertices_physarum_t720_y1475_x1477.csv: The identities and coordinates of
 all vertices of the graph at all frames. * replicate_physarum_analysis.md:
 a file explaining how to reproduce the analysis. fig1 contains: * All
 panels of the Figure 1 of the paper. figure 1.py contains: * The complete
 script to reproduce all panels of the Figure 1 of the paper. #### Files
 content of the ground truth accuracy fig2 contains: * All panels of the
 ground truth comparison part of the Figure 2 of the paper.
 Figure2_ground_truth_tests.py contains: * The complete script to reproduce
 all panels of the ground truth comparison part of the Figure 1 of the
 paper. #### Files content of the a posteriori analysis ##### Raw data
 files and outputs from Cellects Raw data and outputs from the Cellects
 software can be found in six folders (named quail_development,
 slime_choice_on_blackpaper, slime_circular_on_leds,
 slime_circular_on_whitepaper, slime_lengthen_on_leds,
 slime_square_on_leds) in the Cellects_data.zip archive. In these six
 folders, raw data are image sequences (in tif for quail_development and
 jpg for all others). The outputs from Cellects include: * Visualization of
 the efficiency of the analysis: * jpg files contain screenshots of the
 analysis of all arenas (and their identification number). * mp4 files
 contain videos of the analysis of each arena (named according to their
 identification number). * Data tables: * one_row_per_arena.csv *
 Description: contains the kind of variables that have only one value for
 the whole video of an arena * Variables (unit): * arena (1):
 identification number of the arena * first_move (frame): time at which
 Cellects detects a first movement of a specimen in the arena *
 iso_digi_transi (min): when the specimen displays an isotropic growth, if
 it switches into a digitated growth, save the time at which it happens. NA
 if one of these conditions is not met. * is_growth_isotropic (bool): 1 if
 the specimen displays an isotropic growth, 0 otherwise * final_area
 (pixels): the area covered by the specimens at the last frame. *
 one_row_per_frame.csv * Description: contains the kind of variables that
 have one value per frame for each arena * Variables (unit): * arena (1):
 identification number of the arena * time (min): for each arena, one row
 corresponds to one frame of the video * area (pixels): area covered by the
 specimen * perimeter (pixels): the total perimeter of the specimens at the
 last frame. * circularity (1): 4*pi*area/(perimeter^2) * rectangularity
 (1): area/(R1*R2) with R1 and R2 the lengths of the sides of the smallest
 rectangle containing the specimens * total_hole_area (pixels): summed area
 of all holes * solidity (1): area/convex_hull_area * convexity (1):
 convex_hull_perimeter/perimeter * eccentricity (1): sqrt(1 -
 (minor_axis_length/major_axis_length)) * euler number (1): connected shape
 number - holes number * standard_deviation_x (pixels): standard deviation
 on the x-axis * standard_deviation_y (pixels): standard deviation on the
 y-axis * skewness_x (1): skewness on the x-axis * skewness_y (1): skewness
 on the y-axis * kurtosis_x (1): kurtosis on the x-axis * kurtosis_y (1):
 kurtosis on the y-axis * major_axis_len (pixels): sqrt(6 * (C2x + C2y +
 sqrt((2*C11)² + (C2X - C2y)²))) With C2x and C2y the second orders central
 moments with regard to x and y. And with C11 the first order central
 moment with regard to both x and y. * minor_axis_len (pixels): sqrt(6 *
 (C2x + C2y - sqrt((2*C11)² + (C2X - C2y)²))) * axes_orientation (radians):
 0.5 * arctan((2*C11)/(C2x - C2y)) * one_row_per_frame_arena1.csv *
 Description: contains the kind of variables that have one value per frame
 for each arena. Only exists when there can be more than one specimen per
 arena. * Variables (unit): * arena (1): identification number of the arena
 * time (min): for each arena, one row corresponds to one frame of the
 video * area (pixels): followed by a number identifying one specimen in
 the arena * newly_explored_area (pixels): area covered by the specimens
 for the first time at this frame * one_row_per_oscillating_cluster.csv *
 Description: contains a quantification of the clusters oscillating
 synchronously in the specimen * Variables (unit): * arena (1):
 identification number of the arena * mean_pixel_period (frame): the
 average period of the pixels of the current oscillating cluster * phase
 (frame): the average phase of the pixels of the current oscillating
 cluster * cluster_size (pixels): the size of the current oscillating
 cluster * edge_distance (pixels): the minimal distance between the current
 oscillating cluster and the edge of the specimen * software_settings.csv *
 Description: A list of all the settings to use when analyzing the current
 folder with Cellects. * Software and analysis related files: * infos.JPG
 files contain the treatment applied to each arena * .pkl files contain
 parameters and matrices allowing to rerun the analysis more quickly #####
 A posteriori validation of the Cellects software The validation folder, in
 the Cellects_data.zip archive, contains folders named after the five
 tested experimental conditions: * slime_choice_on_blackpaper *
 slime_circular_on_leds * slime_circular_on_whitepaper *
 slime_lengthen_on_leds * slime_square_on_leds Each of these folders
 contains: * The result of automatic segmentation for each of the sixty
 randomly selected arenas (Last_image_arena_**x**.tif) * The manual
 correction of these segmentations (Aremask**x**.tif) * .pkl files allowing
 to rerun the analysis more quickly Note: files named
 Last_image_arena_**x**bis.tif or Aremask**x**bis.tif correspond to the
 second analysis made on these arenas in Appendix 2. organized data tables,
 data visualizations, and statistical output ##### Figures of the paper
 presenting the a posteriori validation The folders paper_figs_jpg and
 paper_figs_tif, in the Cellects_data.zip archive, are made to be empty.
 Running the CellectsPaper python project will fill them with the images
 and plots used in the figures of the related article. All files created in
 this way are compatible with the CC0 license waiver. ##### Scripts to
 reproduce the figures of the a posteriori validation CellectsPaper
 contains a version of Cellects without GUI and the code to reproduce the
 figures of the a posteriori validation using the Cellects_data folder. ##
 Location of the data Boussard, Aurèle; Arrufat, Patrick; Dussutour,
 Audrey; Pérez-Escudero, Alfonso (2023). Cellects, a software to quantify
 cell expansion and motion [Dataset]. Dryad.
 [https://doi.org/10.5061/dryad.7wm37pvzp](https://doi.org/10.5061/dryad.7wm37pvzp) An up-to-date version of the Cellects software, along with installation procedure and user manual, can be found at [https://github.com/Aurele-B/Cellects](https://github.com/Aurele-B/Cellects)