scvi-tools

To correct batch effects in scRNA-seq data, you train a probabilistic model like scVI on raw counts within an AnnData object to learn batch-corrected latent representations. This effectively removes technical variation for seamless cross-dataset integration.

Yes, multimodal single-cell analysis combining RNA and ATAC data is supported through models like MultiVI and TOTALVI. These probabilistic models integrate protein and ATAC modalities with RNA to provide a unified latent representation for comprehensive cellular analysis.

Probabilistic modeling improves differential expression by quantifying uncertainty in gene expression estimates across latent cellular states. This uncertainty-aware approach ensures that downstream interpretations of single-cell data are statistically robust and reproducible.

Yes, you need a Python environment with PyTorch and scvi-tools installed to run the generative models. The workflow also requires Scanpy and AnnData to prepare raw count matrices and perform downstream clustering and visualization tasks.

The best way to learn latent cellular states from raw scRNA-seq counts is applying a deep generative model like scVI. It processes raw counts directly to infer latent representations, normalize expressions, and handle batch effects without requiring manual data normalization.