| --- |
| license: cc-by-4.0 |
| tags: |
| - single-cell |
| - scRNA-seq |
| - batch-correction |
| - scvi |
| library_name: pytorch |
| --- |
| |
| # BA-scVI trained on scMARK v2 |
|
|
| A Batch-Adversarial scVI model trained on the [scMARK v2](https://zenodo.org/records/7795653) |
| benchmark (11 cancer scRNA-seq studies, 109,543 cells, 23 standardised cell types). |
|
|
| ## Result |
|
|
| Scored with the K-Neighbors Intersection (KNI) metric from |
| [Diaz-Mejia et al., LMRL @ ICLR 2025](https://arxiv.org/abs/2503.20730) |
| (k=50, tau=0.8k, batch = `study_name`, label = `standard_true_celltype`), |
| over all 109,543 cells: |
|
|
| | model | KNI | cross-study acc | batch diversity | |
| |---|---|---|---| |
| | **this model** | **0.7153** | 0.7182 | 0.995 | |
| | BA-scVI (published) | 0.7110 | 0.712 | 0.999 | |
| | PCA baseline | 0.470 | - | - | |
|
|
| The published 0.7110 was independently reproduced from the authors' released |
| embedding using both their `calc_kni_score` and our implementation (0.7110 / |
| 0.7114), so the two numbers are directly comparable. |
|
|
| ## Architecture |
|
|
| scVI-family VAE with ZINB likelihood and an adversarial discriminator on the |
| latent. Batch identity is injected into the **decoder only** (the encoder never |
| sees it), matching the configuration used for the published BA-scVI results. |
|
|
| - 10-d latent, 512 hidden units, 4 layers, dropout 0.1 |
| - ZINB reconstruction, masked by each study's measured gene panel |
| - discriminator weight 100, confusion objective, normalised per batch level |
| - batch levels: 1 modality / 11 studies / 354 samples |
| - input: 35,804 genes (see `gene_list.txt`; order matters) |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| ck = torch.load("bascvi_scmark_epoch63.ckpt", map_location="cpu", weights_only=False) |
| gene_list = ck["hyper_parameters"]["gene_list"] # 35,804 HGNC symbols, input order |
| ``` |
|
|
| Counts must be raw integers in `gene_list` order; the model applies |
| `log(1 + 1e4 * x / x.sum())` internally. Embed via |
| `forward(batch, encode=True, predict_mode=True)["qz_m"]`, which zeroes the batch |
| vector — the latent is what you score. |
|
|
| ## Training data |
|
|
| Trained **only** on scMARK v2 (CC-BY 4.0). No other expression data was used. |
| The 35,804-gene input vocabulary is a superset reference gene list; genes a |
| study did not measure are masked out of the reconstruction loss as structural |
| zeros rather than treated as biological zeros. |
|
|
| ## Caveats |
|
|
| - KNI is computed over all cells, including those the model trained on — this is |
| the published benchmark's own methodology, and applies equally to the 0.7110 |
| reference. |
| - KNI rewards low-dimensional embeddings through its batch-diversity gate; all |
| numbers above are at 10-d. |
|
|
