Single-cell RNA-seq and ATAC-seq integration ============================================ 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