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1-Gene-Expression-Processing.ipynb		1-Gene-Expression-Processing.ipynb
1.1-Gene-Expression-Processing-with-Pearson-Residuals.ipynb		1.1-Gene-Expression-Processing-with-Pearson-Residuals.ipynb
2-Chromatin-Accessibility-Processing.ipynb		2-Chromatin-Accessibility-Processing.ipynb
3-Multimodal-Omics-Data-Integration.ipynb		3-Multimodal-Omics-Data-Integration.ipynb
README.md		README.md

README.md

Single-cell RNA-seq and ATAC-seq integration

Information on joint gene expression and open chromatin profiling can be found here.

Data processing and analysis workflow is split into multiple chapters.

Gene Expression Processing. This notebook largely follows this scanpy tutorial on processing and clustering PBMCs.
Peaks Processing. While following the flow of the previous chapter, it introduces ATAC-related functionality for data processing and visualisation.
Multimodal Omics Data Integration. This notebook demonstrates how multiple modalities can be combined in a single Python workflow and how multi-omics methods such as multi-omics factor analysis (MOFA) can be applied for data analysis and interpretation.

There are also notebooks showcasing specific details and alternative processing steps.

Gene Expression Processing using Pearson residuals shows how normalization strategy presented by Lause et al. can be used when working with RNA counts.