bio-scvi-tools

What problem does it solve?

This Skill addresses the complexity of analyzing single-cell omics data by providing advanced probabilistic models for tasks like batch correction, integration, and differential expression analysis.

Core Features & Use Cases

• Probabilistic Modeling: Utilizes deep generative models (VAEs) for robust analysis.
• Data Modalities: Supports RNA-seq, ATAC-seq, CITE-seq, and spatial transcriptomics.
• Use Case: You have multiple batches of single-cell RNA-seq data and need to integrate them, correct for batch effects, and perform differential gene expression analysis between cell types. This Skill provides the tools to achieve this with uncertainty quantification.

Quick Start

Use the bio-scvi-tools skill to analyze single-cell RNA-seq data by setting up AnnData, training an SCVI model, and extracting the latent representation.