Summary of Joint Analysis Methods for 10X Spatial Transcriptomics and 10X Single-Cell Data

The joint analysis of spatial transcriptomics data and single-cell data provided by 10X Genomics mainly involves the following mainstream methods:

1. Co-expression Analysis:

Using co-expression network analysis (WGCNA) or other correlation analysis methods to identify co-expressed genes in different cell types or tissue regions.

2. Spatial Mapping and Cell Type Annotation:

Annotating cell types in spatial transcriptomics data using single-cell data. This can be achieved by comparing gene expression patterns in spatial data with known expression patterns of single-cell types.

3. Functional Enrichment Analysis:

Combining single-cell phenotypes and spatial locations, using GO or KEGG for functional annotation to reveal the functional characteristics of cells within different tissue regions.

4.Pseudotime Analysis:

Combining pseudotime trajectory analysis from single-cell data with spatial data to reveal the developmental trajectory of cells in different tissue structures.

5. Cell-Cell Interaction Analysis:

Inferring spatial interactions between cells using spatial data and further analyzing intercellular communication with single-cell data.

6.Visualization:

Using t-SNE, UMAP, or spatial plots for data visualization, combined with cell type identification and spatial information, to display cell heterogeneity within tissue structures.

Joint analysis software usually includes R packages or Python packages, such as Seurat (R), Scanpy (Python), and spatialDE (Python), which can be used for advanced analysis and integration.

BiotechPack, A Biopharmaceutical Characterization and Multi-Omics Mass Spectrometry (MS) Services Provider

Related Services:

Single-Cell Sequencing

Single-Cell Mass Cytometry Analysis