Use Cluster Plotter for explorative data analysis

[stefanhahmann]

User Story

From a biological perspective, it would be interesting to perform an explorative study of a developmental biological dataset. The goal is to find out, which features that can already be computed (cf. #10, #11, #12, #13 and will possible to be computed in the future cf. #1, #6 / mastodon-sc/mastodon-pasteur#9, #7, #9, ) are distinctive to separate different types of cells.

The underlying research question would be to find a out if there is a clustering method to reveal biologically relevant clusters (e.g. cell types).

Tasks

• Load data of Averof Lab into Mastodon
• Perform ellipsoid fitting
• Compute all currently possible features
• Export data as CSV
  • Spots
  • (Links)
  • BranchSpots
  • (BranchLinks)
  • Add Links between Spots and BranchSpots (Add computer for BranchSpot id  #37)
• Plugin to export ellipsoids as Label Image (Export Label Image Using Spot Ellipsoids #40)
• Load data into Cluster Plotter
  • Explore Spot features (Plot different features against each other)
  • Explore BranchSpot features (Plot multiple features against each other)
  • Check, if further features can be added using Napari region props
  • Perform u-MAP / dimensionality reduction
    • Try different subsets of features
    • Run on Workstation if performance on local machine is too low
  • Load image data / timelapse into Napari as well to understand potential clusters that can be seen in clusters plotter
    • Test, if HDF5 image data can be imported
    • Perhaps, convert HDF5 image data to TIFF data, if importing is easier then
  • Make screenshots with potentially relevant settings for discussion in next meeting
• Perform tests with data by Mette
• Export data as CSV
  • Spots
  • Links
  • BranchSpots
  • BranchLinks
• Load data into Cluster Plotter
  • Explore Spot features (Plot different features against each other)
  • Explore BranchSpot features (Plot multiple features against each other)
  • Try to find features + clustering settings that correspond to the labels (i.e. cell types) given by Mette

Additional Information

Publicly available data by Averof lab

You can find the zenodo link to the image dataset (+tracks) on this page: https://www.averof-lab.org/pages/2923-resources

Features of interest / features that are assumed to be biologically relevant

• https://docs.google.com/document/d/107yOWeroBS3xZWMTV1Ieuv8u0m1KO-eA3JgH7qhA5_s/edit
• https://docs.google.com/document/d/14U5RnJ59U9dsKmJWv3UL-_2rB9-T7XXnyZWsEf2YN_A/edit (page 3)

Code for reading data exported from Mastodon into Napari Cluster plotter

• https://gitlab.mn.tu-dresden.de/bia-pol/load_tracking_features_into_napari_clusters_plotter

[stefanhahmann]

U-MAP is implemented in a non-parallel way in Napari (making relatively long to process in a single thread):