tlsR: Detection and Spatial Analysis of Tertiary Lymphoid Structures

Fast, reproducible detection and quantitative analysis of tertiary lymphoid structures (TLS) in multiplexed tissue imaging data.

Typical workflow

1. Load or prepare a named list of data frames (ldata), one per tissue sample. Each data frame must contain columns x, y (spatial coordinates in microns), and phenotype (character: "B cell" / "T cell" / other).

2. Run detect_TLS to label B+T co-localised regions.

3. (Optional) Run scan_clustering to identify windows of significant immune clustering via local Ripley's L.

4. Run calc_icat to score the internal linearity/organisation of each detected TLS.

5. Run detect_tic to identify T-cell clusters outside TLS.

6. Use summarize_TLS to obtain a tidy summary table.

7. Use plot_TLS to produce publication-ready spatial plots.

References

Amiryousefi et al. (2025) doi:10.1101/2025.09.21.677465

See also

Useful links:

• https://github.com/AmiryousefiLab/tlsR

Author

Maintainer: Ali Amiryousefi ali_amiryousefi@hms.harvard.edu (ORCID)

Authors:

• Jeremiah Wala jeremiah_wala@dfci.harvard.edu (ORCID)

Other contributors:

• Peter Sorger (ORCID) [contributor]