Spatial Data Alignment

Spatial Data Alignment is a computational method that integrates spatially resolved molecular datasets by establishing precise positional correspondences across multiple tissue sections or modalities, enabling joint analysis of spatial single-cell omics data through geometric transformation and feature matching.

44

Publications

2.2K

Citations

284

Authors

116

Institutions

Publications per year

2017–2026

27

Authors

284

  1. IV
    Isaac Virshup

    Helmholtz Zentrum München, Germany

    2 publications · 218 citations

    2

    h-index

  2. JM
    Josh Moore

    University of Dundee, United Kingdom

    2 publications · 218 citations

    2

    h-index

  3. LF
    Lucas Fidon

    University College London, United Kingdom

    2 publications · 33 citations

    2

    h-index

  4. GP
    Giovanni Palla

    Helmholtz Zentrum München, Germany

    2 publications · 218 citations

    2

    h-index

  5. FJ
    Fabian J. Theis

    Helmholtz Zentrum München, Germany

    2 publications · 218 citations

    2

    h-index

1–5 of 284

Venues

  1. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 67 citations

    3

    h-index

  2. Nature Communications

    2 publications · 176 citations

    2

    h-index

  3. Nature Methods

    2 publications · 274 citations

    2

    h-index

  4. Open Research Europe

    2 publications · 33 citations

    2

    h-index

  5. IEEE Transactions on Medical Imaging

    2 publications · 58 citations

    2

    h-index

1–5