## The task

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.

Dataset | Best Method | Paper | Code |
---|---|---|---|

Pancreas (alpha=0.5) | Stereoscope | v0.16.4 | |

Pancreas (alpha=1) | Stereoscope | v0.16.4 | |

Pancreas (alpha=5) | Stereoscope | v0.16.4 |