LARIS: ligand-receptor interactions in spatial data
LARIS — Ligand And Receptor Interaction in Spatial transcriptomics — is a separate package with its own repository and documentation. It is described here because it answers the same question as SCALAR under a different constraint, and choosing between them is the first decision, not an implementation detail.
- Repository: https://github.com/genecell/LARIS
- Documentation: https://genecell.github.io/LARIS/
- Install:
pip install laris
What changes when cells have coordinates
SCALAR asks whether a ligand is specific to the sender and the receptor specific to the receiver. It cannot ask whether those two cells were ever near each other, because a dissociated dataset does not know. Every ordered pair of cell types is scored as though contact were possible.
In spatial data that assumption is testable, and it is usually false. Two cell types that both express a complementary pair may sit in different layers and never touch. LARIS uses the coordinates: interaction strength is computed per cell against its spatial neighbours, so a pair only scores where the two partners are actually adjacent.
That shifts what the output is. SCALAR returns one score per (LR pair, sender, receiver). LARIS returns both: a score for each individual cell, and the sender-receiver cell-type summary computed from it. So it is not a trade — the cell-type view SCALAR gives you is still there, with the per-cell resolution underneath it, which is what lets the same result be mapped onto the tissue or tested for spatial variability.
What it produces
- Per-cell interaction strength for each ligand-receptor pair.
- Spatially variable LR pairs — pairs whose interaction is patterned across the tissue rather than uniform.
- Sender-receiver scores at cell-type level, the summary that is directly comparable to a SCALAR result.
- Spatial neighbourhood context — which cell-type compositions a given interaction occurs in.
Which one to use
| SCALAR | LARIS | |
|---|---|---|
| data | dissociated scRNA-seq / snRNA-seq | spatial (MERFISH, Xenium, Visium, …) |
| unit of the answer | cell-type pair | individual cell and cell-type pair |
| what constrains a hit | specificity in both partners | specificity and physical proximity |
| the output can be | ranked, filtered, plotted | all of that, plus mapped onto tissue |
They share the interaction database. The CellChatDB tables that
piaso.data.load_lr_database fetches are the same input either method takes, so
a pair list curated for one transfers to the other.
The natural pairing is both: SCALAR on a dissociated reference to find which interactions exist in the tissue at all, LARIS on the spatial sample to ask where they happen.