MANTA
MANTA

MANTA


Multiscale Analysis for N-dimensional Transcriptome Alignment

Meeting 13/02 Talking Points
  • Weighting Scheme
  • Matching problem
  • Plane Matching → Voxelization → Ransac → Umeya/Kabsch → …
     

    Methods
    Methods
     

    TODO → 18/02/2025
    Fix degeneracy in rigid alignment
    • Probably due to sparse voxels and spurious couplings?
    3D test cases for rigid alignment
    3D tilt
    3D widespread rotation
    Full 3D mouse brain (even/odd)
    Download STARmap Data and perform equal registration tasks as with MERFISH
    Define axis → Currently model allows to “slice” through planes
    • See meeting 13/02
    Start working on non-rigid -dimensional registration
    Do spatial embeddings improve features?
    • Test Novae embedding
    • Start working on region-based non-rigid alignment. ⚠️ region-based rigid alignment is probably overkill!
    Thesis Parts
    • Existing registration methods
    • Introduction
    • Benchmark
    TODO → 13/02/2025
    Expand 2D-based RANSAC + Kabsch-Umeyama to 3D
    • Does rotation matrix in 3D hold up? ⇒ YES
    • Do we need to resort to quaternions? ⇒ YES
     
    Proper error computation
    Fix iterative refinement
    Optimalization
    GIF of alignment
    Voxel-match plotting
    Determinism in algorithm
    Restart (→ prevent local minima in parameter space)
    Start working on/discussing the non-rigid alignment
    TODO → 06/02/2025
    Find voxel correlation algorithm
    • Find where voxels should be rigidly aligned to. Some ideas;
      • Best correlator (can not handle sparse voxelization!)
      • Weighted average of k-best correlators
    • Move voxel rigidly towards the found position
    • Define maximal neighbourhood depending on voxelization scale (probably one-neighbours ⇒ 27 voxels to check)
    TODO → 30/01/2025
    Voxelization
    • New AnnData object
    • Keep center transform in metadata
    • Assign cells to voxels (keep in metadata)
    • Find good resolution range ⇒ not powers of two, voxels too big in this case!
    Mouse Brain Segmentation
    • Segment Mouse Brain into 2 datasets (→ Make 3D reconstruction of 200µm slices, 68 slices per dataset)
    • Try interpolation in z-axis
      • Linear interpolation
      • SpatialZ
    Manuscript
    • Start creating custom Legrand-inspired template
    • Make sure to have
      • list of tables
      • list of figures
      • list of abbreviations
      • custom TOC
      • custom headers (chapter-wise) → full header?
      • custom headers (section-wise) → number on side
      • custom part indicator (no TOC, overdone!)
      • use CMU-bright
      • proper BibTex
    • Port existing manuscript over