Differential Expression

Overview

spatialvi-tools inherits scvi-tools’ Bayes-factor differential expression framework and extends it with niche-aware DE (scVIVA) and niche abundance DE (ResolVI).

Standard DE (vanilla / change mode)

All models expose differential_expression(). Two modes are supported:

• vanilla: computes log-fold-change between groups using posterior samples.

• change: computes the posterior probability that the log-fold-change exceeds a threshold \(\delta\) (default 0.25).

de_df = model.differential_expression(
    adata,
    groupby="cell_type",
    group1=["T cells"],
    group2=["B cells"],
    mode="change",
    delta=0.25,
)

Niche DE (scVIVA)

scVIVA’s differential_niche_expression() tests for gene expression differences that are explained by the cellular microenvironment (niche composition) rather than intrinsic cell-type identity. It uses a Gaussian process classifier to attribute expression variance to niche context.

Niche abundance DE (ResolVI)

ResolVI’s differential_niche_abundance() tests for differences in the composition of the spatial neighbourhood between conditions, enabling discovery of altered cell-type co-localisation patterns across disease states.

References

• Boyeau et al. (2019) Deep generative models for detecting differential expression. bioRxiv.