Cell-type decomposition of spatial transcriptomics spots

Estimate the cell-type proportions in each spot of simulated spatial transcriptomics data, given a single-cell reference.

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1	# Cell-type decomposition of spatial transcriptomics spots
2
3	In spot-based spatial transcriptomics, each capture spot records a mixture of several cells. This benchmark asks a method to recover the proportion of each cell type in every spot, given a single-cell reference, on data where the true proportions are known.
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5	## Task
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7	A method receives a spatial dataset (one expression profile per spot) and a single-cell RNA-seq reference with cell-type labels. It must return, for every spot, a vector of cell-type proportions that sums to one. Methods may also ignore the reference and decompose the spots de novo. The predicted proportions are compared with the true ones.
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9	## Background
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11	Spatial decomposition (or deconvolution) is a common step in analysing spot-based data such as Visium, because a spot can contain many cells. Methods range from non-negative least squares to probabilistic models such as Cell2location, RCTD, Stereoscope and DestVI. Real tissue offers no exact ground truth, so the benchmark builds spots by pooling single cells whose identities are known. The task was set up by Alma Andersson, Giovanni Palla, Vitalii Kleshchevnikov, Hirak Sarkar and Scott Gigante within Open Problems.
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13	## Data and evaluation
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15	- Data: seven datasets in release v1.0.0. Six are pseudo-spots built from human pancreas (inDrop) and Tabula Muris Senis lung single-cell data: each spot pools 10–30 cells whose type proportions are drawn from a Dirichlet distribution with $\alpha = 0.5$, 1 or 5, so smaller $\alpha$ gives purer spots. The seventh is a simulation from the DestVI paper. Data are downloaded and built by the dataset loaders in the task code.
16	- Split: there is no train/test split; the single-cell reference is supplied with each spatial dataset. Pseudo-spots are generated with seed 42.
17	- Metric: $R^2$ between true and predicted proportions, averaged uniformly over cell types. It is rescaled per dataset between the random-proportions control (0) and the true-proportions control (1), clipped to $[0, 1]$, and the overall score is the mean over the seven datasets, with failed runs counted as 0. The leaderboard shows it to two decimals; methods with several parameter sets are listed by their best and worst set.
18	- Code: release v1.0.0 lives in the legacy [openproblems repository](https://github.com/openproblems-bio/openproblems/tree/v1.0.0/openproblems/tasks/spatial_decomposition); the task now continues in [openproblems-bio/task_spatial_decomposition](https://github.com/openproblems-bio/task_spatial_decomposition).
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20	## What is known
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22	On the v1.0.0 leaderboard (as of 2026-10-20):
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24	- Cell2location leads at 0.85 (0.8538 before rounding, recomputed from the published results) with its best parameter set (0.84 with its worst).
25	- DestVI follows at 0.75, non-negative least squares at 0.67, RCTD at 0.57 and Stereoscope at 0.56; Tangram scores 0.
26	- A pre-release (v1.1.0-rc1) re-runs the task on ten datasets in the new framework, and there the ranking changes: Stereoscope leads at 0.45 and Cell2location scores 0.33. Scores between releases are not comparable.
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28	## What would count as progress
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30	- An overall score above 0.8538 on the v1.0.0 datasets, computed with the release's evaluation code, with code that reproduces it.
31	- A method that also leads on the newer ten-dataset benchmark in task_spatial_decomposition, where no method yet reaches 0.5.
32	- Better raw $R^2$ on the near-uniform mixtures ($\alpha = 5$), where the leading methods have their lowest raw scores; for Cell2location it stays below 0.3 on the lung data with every parameter set.
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34	## References
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36	1. Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology 40, 661–671 (2022). [doi:10.1038/s41587-021-01139-4](https://doi.org/10.1038/s41587-021-01139-4)
37	2. Lopez R, Li B, Keren-Shaul H, et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology 40, 1360–1369 (2022). [doi:10.1038/s41587-022-01272-8](https://doi.org/10.1038/s41587-022-01272-8)
38	3. Luecken MD, Gigante S, Burkhardt DB, Cannoodt R, et al. Defining and benchmarking open problems in single-cell analysis. Nature Biotechnology 43, 1035–1040 (2025). [doi:10.1038/s41587-025-02694-w](https://doi.org/10.1038/s41587-025-02694-w)
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40	## Source
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42	Imported from [Open Problems in Single-Cell Analysis: Spatial Decomposition (spatial_decomposition)](https://openproblems.bio/benchmarks/spatial_decomposition), curated by the Open Problems in Single-Cell Analysis consortium. Open according to the source as of 2026-10-20 (leaderboard release v1.0.0). The statement above is written in our own words; see the source for the original wording, history and discussion.