Single-cell RNA-seq and ATAC-seq integration
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):
gene expression processing notebooks — largely follows this scanpy tutorial on processing and clustering PBMCs;
peaks processing notebook introduces ATAC-related functionality for data processing and visualisation,
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):
processing individual modalities and multi-omics integration demonstrates how individual modalities can be processed and integrated to prepare the ground for downstream analysis,
celltype annotation offers a closer look at how cell type labels can be assigned with a few complementary approaches.