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README.md

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+---
+license: cc-by-nc-sa-4.0
+language:
+  - en
+thumbnail: x-cell-overview.png
+tags:
+  - biology
+  - single-cell
+  - perturbation-prediction
+  - diffusion-model
+  - genomics
+  - CRISPRi
+datasets:
+  - Xaira-Therapeutics/X-Atlas-Pisces
+pipeline_tag: other
+---
+
+# X-Cell
+
+**A diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.**
+
+> **Status: Model weights and inference code coming soon.**
+> The Python API, model weights, and tutorials are under active development.
+> Watch the [GitHub repository](https://github.com/Xaira-Therapeutics/x-cell) for release updates.
+
+<p align="center">
+  <img src="x-cell-overview.png" alt="X-Cell Architecture" width="100%">
+</p>
+
+## Model Description
+
+X-Cell predicts genome-scale transcriptional responses to genetic perturbations across diverse cellular contexts. Trained on **X-Atlas/Pisces** (25.6M perturbed single cells, 7 CRISPRi Perturb-seq screens), X-Cell integrates multi-modal biological priors through cross-attention and generalizes zero-shot to unseen cell types and perturbations.
+
+### Key Results
+
+- **5x higher Pearson delta** than the next-best method on held-out iPSC perturbations
+- **Zero-shot T-cell inactivation** — predicts CD3 complex inactivators and novel regulators (LRBA, APPL2)
+- **LLM-class scaling laws** — train loss scales as L(N) ~ N^-0.32 (R^2 = 0.96)
+- **Zero-shot cell type generalization** to melanocyte progenitors and primary human CD4+ T cells
+
+## Model
+
+| Model | Parameters | Description |
+|-------|-----------|-------------|
+| **X-Cell Mini** | 55M | Fast inference; initialized from scGPT |
+
+## Architecture
+
+X-Cell is a **set-level diffusion transformer** that operates on sets of cells (not individual cells) and refines predictions iteratively via a masked diffusion process. Key components:
+
+- **Diffusion-based training** with 4-step coarse-to-fine refinement at inference
+- **Multi-modal biological priors** via Flamingo-style cross-attention (ESM-2, STRING, GenePT, DepMap, JUMP-Cell Painting, scGPT)
+- **Tied output embeddings** with PaLM-style 1/sqrt(d) scaling
+
+## Intended Use
+
+X-Cell is designed for predicting transcriptional responses to CRISPRi gene knockdowns. It is intended for research use in computational biology and genomics.
+
+## Training Data
+
+Trained on X-Atlas/Pisces — the largest CRISPRi Perturb-seq compendium to date:
+
+| Screen | Context | Perturbations | Cells |
+|--------|---------|--------------|-------|
+| HCT116 | Colorectal cancer | 18,924 | 3.4M |
+| HEK293T | Kidney epithelial | 18,312 | 4.5M |
+| HepG2 | Hepatocellular carcinoma | 9,735 | 2.6M |
+| iPSC | Induced pluripotent stem cells | 10,095 | 4.2M |
+| Jurkat Resting | T lymphoblastic leukemia | 10,872 | 2.8M |
+| Jurkat Active | CD3/CD28-stimulated T cells | 10,878 | 2.8M |
+| iPSC Multi-Diff | Multi-lineage differentiation | 12,175 | 5.1M |
+
+Dataset: [Xaira-Therapeutics/X-Atlas-Pisces](https://huggingface.co/datasets/Xaira-Therapeutics/X-Atlas-Pisces)
+
+## Usage (Coming Soon)
+
+```python
+from xcell import XCell
+
+model = XCell.from_pretrained("Xaira-Therapeutics/X-Cell", variant="mini")
+predictions = model.predict("control_cells.h5ad", perturbation="BRCA1")
+```
+
+Full documentation: [Xaira-Therapeutics.github.io/x-cell](https://xaira-therapeutics.github.io/x-cell)
+
+## Citation
+
+```bibtex
+@article{xcell2026,
+  title   = {X-Cell: Scaling Causal Perturbation Prediction Across Diverse
+             Cellular Contexts via Diffusion Language Models},
+  year    = {2026},
+}
+```
+
+## License
+
+This model is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.