Single-cell RNA-seq and ATAC-seq integration
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Information on joint gene expression and open chromatin profiling `can be found here `_.
Different datasets are covered:
- 10k peripheral blood mononuclear cells (PBMCs) (data by 10x Genomics can be found `here `_):
1. `gene expression processing notebooks `_ — largely follows `this scanpy tutorial `_ on processing and clustering PBMCs;
2. `peaks processing notebook `_ introduces ATAC-related functionality for data processing and visualisation,
3. `multimodal omics data integration 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.
- 3k cells from the frozen human healthy brain tissue (data by 10x Genomics can be found `here `_):
4. `processing individual modalities and multi-omics integration `_ demonstrates how individual modalities can be processed and integrated to prepare the ground for downstream analysis,
4. `celltype annotation `_ offers a closer look at how cell type labels can be assigned with a few complementary approaches.
.. toctree::
:hidden:
:maxdepth: 3
pbmc10k/1-Gene-Expression-Processing.ipynb
pbmc10k/2-Chromatin-Accessibility-Processing.ipynb
pbmc10k/3-Multimodal-Omics-Data-Integration.ipynb
brain3k/1-Processing-and-Integration.ipynb
brain3k/2-Celltype-Annotation.ipynb