1. scRNA-seq data preparation

Overview

Before starting a CellOracle analysis, the scRNA-seq data must be preprocessed. Please prepare the scRNA-seq data as an anndata object using Scanpy.

Note

Scanpy is a python toolkit for scRNA-seq data analysis. If you are new to Scanpy, please read the documentation to learn it in advance.

• scanpy documentation: https://scanpy.readthedocs.io/en/stable/

• anndata documentation: https://anndata.readthedocs.io/en/latest/

Warning

In this section, we will introduce an example of how to prepare the input data for CellOracle analysis. This is NOT the CellOracle analysis itself. We do NOT use CellOracle in this scRNA-seq data preprocessing steps.

A. scRNA-seq data preprocessing with Scanpy

Please download the notebook from here . Or please click below to view the content.

• Overview
  • Notebook file
  • Steps
  • Caution
  • Acknowledgement
• 0. Import libraries
• 1. Load data
• 2. Filtering
• 3. Normalization
• 4. Identification of highly variable genes
• 5. Log transformation
• 6. PCA and neighbor calculations
• 7. Cell clustering
• 8. Dimensionality reduction using PAGA and force-directed graphs
• 9. Check data
• 10. [Optional step] Cluster annotation
• 11. [Optional step] Subset cells
• 12. Save processed data

B. scRNA-seq data preprocessing with Seurat

R notebook … comming in the future update.

Note

If you used Seurat for preprocessing, you will need to convert your Seurat object into an anndata object. CellOracle has a python API and command-line API to help users with this data conversion. Please go to the documentation of CellOracle’s API documentation for more information.