Sergei Rybakov

Pertpy: an end-to-end framework for perturbation analysis

Pertpy provides a modular framework for analyzing large-scale perturbation single-cell experiments, offering tools for metadata annotation and perturbation distance calculations.

anndata: Annotated data

The paper introduces anndata, a Python package for handling annotated data matrices, providing features like sparse data support and a PyTorch interface.

Biologically informed deep learning to infer gene program activity in single cells

The paper introduces expiMap, a deep learning model that enables interpretable reference mapping of single-cell transcriptome data by incorporating biologically meaningful gene programs.

Squidpy: a scalable framework for spatial omics analysis

Squidpy extends Scanpy/AnnData with spatial neighbor graphs and a lazy-loading image container, enabling spatial statistics, ligand-receptor tests, image features, and ~10-fold faster neighborhood enrichment than Giotto.

Learning interpretable latent autoencoder representations with annotations of feature sets

The paper introduces an interpretable autoencoder framework for single-cell RNA-seq data using annotated feature sets, showing improved scalability over factor models.