bio-single-cell

Batch correction on scRNA-seq data uses deep learning models like scVI and scANVI to correct technical variations between experimental runs. This Skill applies these scvi-tools models to integrate datasets and remove batch effects.

Yes, scvi-tools supports analyzing CITE-seq and spatial transcriptomics data. This Skill handles multi-modal analysis with totalVI and MultiVI, and performs spatial transcriptomics deconvolution.

The best way to integrate multiple scRNA-seq datasets is using deep learning-based data integration. This Skill leverages the scvi-tools ecosystem to align datasets and perform comprehensive quality control.

Yes, you need a Python and bash environment with specific bioinformatics packages installed. This Skill requires these dependencies to execute deep learning-based single-cell RNA sequencing analysis and QC.

Yes, single-cell analysis with scvi-tools supports RNA velocity and label transfer. This Skill also handles ATAC-seq analysis and multiome data integration using the scverse deep learning ecosystem.