| # Model |
|
|
| ## Architecture |
|
|
| X-Cell is a **set-level diffusion transformer** that predicts perturbed gene expression profiles |
| from control cell populations. Unlike autoregressive single-cell foundation models, X-Cell operates |
| on *sets* of cells and is trained explicitly on interventional data via distribution-matching objectives. |
|
|
| <p align="center"> |
| <img src="figs/x-cell-overview.png" alt="X-Cell Architecture" width="100%"> |
| </p> |
|
|
| ### Key design choices |
|
|
| **Diffusion-based training.** |
| Each training sample has a random fraction (25%, 50%, 75%, or 100%) of control gene expression |
| positions replaced with ground-truth perturbed values. The model learns to predict the full |
| perturbed profile from this partially revealed input. At inference, predictions are iteratively |
| refined across 4 steps (coarse-to-fine). |
|
|
| **Multi-modal biological priors via cross-attention.** |
| At every third self-attention layer, Flamingo-style cross-attention conditions gene representations |
| on six prior knowledge tokens per perturbation: |
|
|
| | Source | Content | Dimension | |
| |--------|---------|-----------| |
| | [ESM-2](https://github.com/facebookresearch/esm) | Protein language model embeddings | 5120 | |
| | [STRING](https://string-db.org) | Protein-protein interaction network | 512 | |
| | [GenePT](https://github.com/yiqunchen/GenePT) | LLM gene representations | 3072 | |
| | [DepMap](https://depmap.org) | Genetic dependency profiles | 1150 | |
| | [JUMP-Cell Painting](https://jump-cellpainting.broadinstitute.org) | Morphological features | 259 | |
| | Gene identity | Stop-gradient gene embedding | — | |
|
|
| **Tied output embeddings.** |
| The output head projects back through the shared gene embedding matrix (PaLM-style 1/√d scaling), |
| acting as an implicit regularizer against conservative collapse. |
|
|
| --- |
|
|
| ## X-Cell Mini |
|
|
| | | X-Cell Mini | |
| |---|---| |
| | **Parameters** | 55M | |
| | **Layers** | 12 | |
| | **Hidden dim** | 512 | |
| | **Attention heads** | 8 | |
| | **FFN** | ReLU, 1× | |
| | **Normalization** | Post-LN (LayerNorm) | |
| | **Cross-attn layers** | 4 | |
| | **Init** | scGPT | |
| | **Training** | Replogle-Nadig | |
| | **Min GPU VRAM** | 8 GB (1 GPU) | |
|
|
| --- |
|
|
| ## Scaling |
|
|
| X-Cell follows power-law scaling consistent with large language models. Train loss scales as |
| L(N) ∝ N⁻⁰·³² (α = 0.32, R² = 0.96) across five model sizes from 83M to 3.1B parameters. |
|
|
| --- |
|
|
| ## Weights |
|
|
| Model weights are hosted on HuggingFace: |
|
|
| ``` |
| Xaira-Therapeutics/X-Cell |
| └── mini/ # X-Cell Mini (55M) |
| ``` |
|
|
| [:hugging: Open on HuggingFace](https://huggingface.co/Xaira-Therapeutics/X-Cell){ .md-button } |
|
|
