A flourishing body of computational tools have made it easier to robustly analyze single-cell -omics datasets in a scalable and reproducible way. Here we will dive into conducting an analysis of a single-cell RNA-sequencing dataset with Scanpy and scvi-tools, two popular Python libraries for general purpose analysis tasks.
This tutorial will cover the following items:
Overview of the AnnData format, which powers Python-based single-cell libraries
Data preprocessing and quality control
Dimensionality reduction and dataset integration
There will be a focus not only on how to run the software, but also the motivation behind each of these steps.
Andrews, T. S., & Hemberg, M. (2018). False signals induced by single-cell imputation. F1000Research, 7.
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018). Deep generative modeling for single-cell transcriptomics. Nature methods, 15(12), 1053-1058.
Luecken, M. D., & Theis, F. J. (2019). Current best practices in single‐cell RNA‐seq analysis: a tutorial. Molecular systems biology, 15(6), e8746.
Wolf, F. A., Angerer, P., & Theis, F. J. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 19(1), 15.