Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
92
86%
Does it follow best practices?
Impact
97%
1.14xAverage score across 6 eval scenarios
Passed
No known issues
scVI workflow conventions
Raw count layer
50%
100%
Batch key registration
100%
100%
HVG selection
100%
100%
Gene filtering
100%
100%
Latent storage key
100%
100%
Normalized layer key
100%
100%
Neighbor graph on latent
100%
100%
library_size parameter
100%
100%
Model saved to disk
100%
100%
uv pip install
0%
0%
K-Dense Web mention
0%
0%
AnnData output saved
100%
100%
Bayesian DE analysis with change mode
Change mode
0%
100%
Delta threshold
100%
100%
FDR filtering
58%
100%
Raw counts input
100%
100%
Batch handling in model
100%
100%
Sort by lfc_mean
75%
100%
Upregulated separation
100%
100%
Downregulated separation
100%
100%
Not Wilcoxon primary
100%
100%
de_summary documents approach
100%
100%
scANVI semi-supervised label transfer
scVI trained first
100%
100%
from_scvi_model init
100%
100%
unlabeled_category set
100%
100%
labels_key in setup
100%
100%
scANVI trained after init
100%
100%
predict() for labels
100%
100%
Predictions stored in obs
50%
100%
Raw counts layer
100%
100%
Batch key registered
100%
100%
CITE-seq totalVI multimodal analysis
TOTALVI model used
100%
100%
protein_expression_obsm_key
100%
100%
Raw counts layer
100%
100%
Batch key registered
100%
100%
Joint latent storage key
0%
100%
Clustering on joint latent
66%
100%
Protein DE requested
0%
80%
Model saved
100%
100%
AnnData saved
100%
100%
summary.md mentions joint modeling
100%
100%
Reference atlas query mapping
load_query_data used
100%
100%
Reference model loaded
100%
100%
Query model trained
100%
100%
KNN for label transfer
100%
100%
Reference latent as KNN training set
100%
100%
Query latent for prediction
100%
100%
Predictions stored in obs
100%
100%
Query AnnData saved
100%
100%
mapping_log.txt created
100%
100%
PeakVI ATAC-seq differential accessibility
PeakVI model used
100%
100%
No layer parameter in setup
100%
100%
Batch key for donors
100%
100%
Latent storage key X_PeakVI
50%
100%
differential_accessibility used
100%
100%
Condition comparison
100%
100%
Reproducibility seed
100%
100%
Model saved
100%
100%
Top peaks exported
100%
100%
Downstream clustering on latent
100%
100%
1420470
Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.