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
Differential expression
Visualization
There will be a focus not only on how to run the software, but also the motivation behind each of these steps.
The colab notebook can be found here. The full notebook can also be viewed in a web browser here.
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.
Conversion of data objects with Zellkonverter