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Spatial extension

Document generated: 2024-09-14 14:01:52 UTC+0000

Source: vignettes/scdrake_spatial.Rmd
scdrake_spatial.Rmd

scdrake now offer spatial extension for the first stage (01_input_qc) and the second stage (02_norm_clustering) of the single-sample pipeline. The spatial possibility is aimed at Visium technology, respectively on spot-based technologies. Scdrake provides comparable results with Seurat, Giotto (R), as well as scanpy (Python), and correspond to Best Practices for Spatial Transcriptomics. For now, we discourage usage of scdrake for other technologies than Visium. For futher analyses of the spatial dataset we recommend CARD for deconvolution and CellChat2 or IGAN for cell-cell interaction.

This vignette should serve as a supplement to other vignettes, as vignette("stage_input_qc") and vignette("stage_norm_clustering")).

Spatial exsention functions

Spatial visualization

For (01_input_qc) and (02_norm_clustering) of the single-sample pipeline we now offer visualization of tissue, as pseudo tissue spot visualization. Spatial extension will add spot coordinates (array_col and array_row) from SpaceRanger tissue_possitions.csv file, and will filter away all spots, that are by SpaceRanger labeled as not in tissue. Visualization function are implemented from the Giotto package. Visualization is automatically used for quality control and dimension reduction results.

Selection of spatially variable genes

For spatial analyses in stage 02_norm_clustering vignette("stage_norm_clustering") when spatial option is enabled, spatially variable genes (SVGs) are automaticaly calculated together with HVGs. That is done using Seurat::SVFInfo, selection method MoransI. A straightforward union of SVGs and HVGs is taking to further processing, see https://www.biorxiv.org/content/10.1101/2021.08.27.457741v1 for more details.

Marker-based annotation

Marker-based annotation was implemented for both single-cell and spatial datasets. In summary, expression profiles and statistical metrics are computed for each cell/spot, the result is visualized using a heatmap and dimension reduction plot. For spatial datasets is enabled to visualized results in tissue coordinates, both enrichment plots for each annotation label (individual enrichment plots) and for overall results for each spot. Marker-based annotation is implemented from the Giotto package, the function is based on Kim SY et al.