Rybakov, S. | Search bioRxiv

bioRxiv2021

Squidpy: a scalable framework for spatial single cell analysis

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides both infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.

bioinformatics↗

bioRxiv2020

Learning interpretable latent autoencoder representations with annotations of feature sets

Existing methods for learning latent representations for single-cell RNA-seq data are based on autoencoders and factor models. However, representations learned by autoencoders are hard to interpret and representations learned by factor models have limited flexibility. Here, we introduce a framework for learning interpretable autoencoders based on regularized linear decoders. It decomposes variation into interpretable components using prior knowledge in the form of annotated feature sets obtained from public databases. Through this, it provides an alternative to enrichment techniques and factor models for the task of explaining observed variation with biological knowledge. Benchmarking our model on two single-cell RNA-seq datasets, we demonstrate how our model outperforms an existing factor model regarding scalability while maintaining interpretability.

bioinformatics↗