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 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
(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
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.