Spatial decomposition (also often referred to as Spatial deconvolution) is applicable to spatial transcriptomics data where the transcription profile of each capture location (spot, voxel, bead, etc.) do not share a bijective relationship with the cells in the tissue, i.e., multiple cells may contribute to the same capture location. The task of spatial decomposition then refers to estimating the composition of cell types/states that are present at each capture location. The cell type/states estimates are presented as proportion values, representing the proportion of the cells at each capture location that belong to a given cell type.

We distinguish between reference-based decomposition and de novo decomposition, where the former leverage external data (e.g., scRNA-seq or scNuc-seq) to guide the inference process, while the latter only work with the spatial data. We require that all datasets have an associated reference single cell data set, but methods are free to ignore this information.

## Metrics

### R2

R2 pronounced as “R squared”, also known as the “coefficient of determination”. R2 reports the fraction of the true proportion values’ (adata.obsm["proportions_true"]) variance that can be explained by the predicted proportion values (adata.obsm["proportion_pred"]). The best score, and upper bound, is 1.0. There is no fixed lower bound for the metric. The uniform/non-weighted average across all cell types/states is used to summarize performance. See the sklearn documentation for details on the implementation and the wikipedia site for more general information regarding the metric.

DatasetBest MethodPaperCode
Pancreas (alpha=0.5)Stereoscope

v0.16.4

Pancreas (alpha=1)Stereoscope

v0.16.4

Pancreas (alpha=5)Stereoscope

v0.16.4