Cellina#

Cellina is a dual-encoder variational autoencoder for predicting how a cell’s gene expression changes under altered spatial contexts — a class of queries we call tissue graph counterfactuals.

In tissues, a cell’s transcriptional state is shaped by its local neighborhood: the composition of nearby cells and the signals they emit. Existing perturbation methods typically treat cells as independent and apply perturbations uniformly. Cellina addresses this gap by explicitly separating a cell’s intrinsic state (z, encoding cell identity) from its spatial context (s, encoding microenvironmental influence), then uses s as a conditioning input to render counterfactual predictions under two types of intervention:

Edge perturbation — rewire a cell’s neighborhood (replace neighbors with those from a different domain)
Node perturbation — modify the expression of existing neighbors (e.g. pathway activation or knockout)

Getting started#

Follow the worked tutorials for end-to-end examples on colorectal cancer tissue: Cellina and Cellina-GAT (or run them locally from docs/tutorial.ipynb and docs/tutorial_gat.ipynb).

How it works#

Generative model. Cellina is a VAE with two latent variables. An MLP encoder \(\text{Enc}_z\) maps raw counts to \(z \sim q(z \mid x)\); a spatial encoder maps the cell’s neighborhood to \(s \sim q(s \mid \mathcal{N}(v))\). A shared decoder reconstructs counts from \([z;\, s]\) under a Negative Binomial likelihood. Both latents have standard normal priors.

Supervised disentanglement. Optimizing the ELBO alone does not prevent \(z\) from absorbing spatially-driven variation. Cellina adds auxiliary objectives:

A cell-type classifier on \(z\) anchors it to transcriptional identity.
An adversarial discriminator is trained to predict spatial domain from \(z\); the encoder is then trained to fool it, routing microenvironmental variation to \(s\) by elimination.
A graph-supervised contrastive loss \(s\) (CellinaGCN only, optional), as a biologically grounded inductive bias that promotes similarity within local neighbourhoods. Enabled by setting link_prediction_weight > 0.

Training alternates between a discriminator step (encoder frozen) and a VAE step (discriminator frozen), following a standard adversarial schedule.

Two variants differ in how the spatial encoder is implemented:

Code class	Paper name	Spatial encoder
`Cellina`	Cellina	Degree-normalized weighted pseudobulk aggregation of neighbor expression → MLP
`CellinaGCN`	Cellina-GAT	Multi-layer GATv2 on the local subgraph; self-loops excluded so \(v\)’s own expression is captured by \(z\) alone; modified contrastive loss on \(s\)

The two variants perform on par. Cellina decouples neighborhood construction from training and scales similarly to non-spatial baselines; CellinaGCN learns attention over each subgraph at additional cost per step.

Tissue graph counterfactuals#

Cellina supports two post-training interventions on the spatial graph \(\mathcal{G}\):

Edge perturbation replaces a cell’s spatial neighbourhood with donors sampled from a target tissue domain, while keeping the cell’s own expression fixed:

\[\mathcal{N}(v) := \mathcal{N}'\]

Node perturbation modifies the feature vectors of \(v\)’s neighbours while preserving graph topology. For a target gene set \(\mathcal{S}\) and a gene-specific transformation \(T_g\):

\[\begin{split}x_{u,g}^{\mathrm{cf}} = \begin{cases} T_g(x_{u,g}) & g \in \mathcal{S} \\ x_{u,g} & g \notin \mathcal{S} \end{cases}\end{split}\]

\(T_g\) can encode any intervention (additive shift, knockout, overexpression, or learned counterfactual values).

See the Cellina and Cellina-GAT tutorials for full worked examples.

Release notes#

See the changelog.

Installation#

Cellina ships two conda environments: environment.yml for the full GPU/CUDA setup, and env_minimal.yml for a lightweight CPU-only install. Create one with conda env create, then follow the tutorials above.

Citation#

Citation coming soon.

Built on scvi-tools.

Contact#

If you found a bug, please use the issue tracker.

Cellina

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