Batch integration of single-cell atlases

Remove batch effects from six single-cell atlases while keeping cell-type variation, scored on 13 metrics against control methods.

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	1	+# Batch integration of single-cell atlases
	2	+
	3	+Large single-cell atlases pool cells from many labs, donors and protocols, and these batches differ for technical reasons. This benchmark asks for an integration method that removes those batch differences without erasing real biology, scored on six atlases relative to control methods.
	4	+
	5	+## Task
	6	+
	7	+Each dataset is a single-cell RNA-seq atlas with several batches. A method receives the counts and normalised expression of 2,000 batch-aware highly variable genes, a PCA and a nearest-neighbour graph, and the batch and cell-type label of every cell. It returns an integrated representation: a corrected feature matrix, a low-dimensional embedding, or a cell–cell neighbourhood graph. The representation is scored on how well batches mix (for example kBET, iLISI, batch silhouette width, principal-component regression) and how well cell identity is kept (for example ARI and NMI of clusters against labels, cLISI, label silhouette width, isolated-label scores and cell-cycle conservation).
	8	+
	9	+## Background
	10	+
	11	+Batch integration is a routine but consequential step in atlas building: too little correction leaves technical structure, too much merges distinct cell types. The task follows the scIB benchmark of Luecken et al. (2022), which compared 68 method and preprocessing combinations on 13 integration tasks and found that scANVI, Scanorama, scVI and scGen performed well on complex tasks. Open Problems turned it into a living benchmark with fixed datasets, containerised methods and a public leaderboard.
	12	+
	13	+## Data and evaluation
	14	+
	15	+- Data: six CELLxGENE Census collections (diabetic kidney disease, GTEx v9, HypoMap, Immune Cell Atlas, mouse pancreatic islet atlas, Tabula Sapiens). Processed files are in the public bucket `s3://openproblems-data/resources/task_batch_integration/datasets/`. The task code is MIT-licensed; dataset licences are those of the original collections and are not restated on the task page.
	16	+- Split: none. All cells are integrated; the processing step (batch-aware HVG selection, PCA, kNN) is deterministic, so no seed is involved.
	17	+- Metric: 13 metrics in release v2.0.0. For each dataset and metric, the raw value $x$ is rescaled between the worst and best control method, $s=\min\{1,\max\{0,(x-c_{\text{worst}})/(c_{\text{best}}-c_{\text{worst}})\}\}$. The overall score is the mean of $s$ over all 6 × 13 dataset–metric pairs; results a method cannot produce (a metric that does not apply to its output type, or a failed run) count as 0. The leaderboard shows it to two decimals.
	18	+- Code: [openproblems-bio/task_batch_integration](https://github.com/openproblems-bio/task_batch_integration) (Viash components and a Nextflow `run_benchmark` workflow).
	19	+
	20	+## What is known
	21	+
	22	+On the v2.0.0 leaderboard (as of 2026-10-20):
	23	+
	24	+- scANVI and ComBat both show 0.52 (0.5164 and 0.5161 before rounding, recomputed from the published results), with scANVI ranked first; scVI is at 0.50 and Harmony at 0.50.
	25	+- The unintegrated data (a control) scores 0.44, so the best methods sit only modestly above doing nothing.
	26	+- Two controls that embed cells by their cell-type labels reach 0.79, which marks the room left between current methods and an ideal integration as these metrics judge it.
	27	+- Single-cell foundation models do not reach the top: UCE scores 0.44, scGPT zero-shot 0.30 and Geneformer 0.06.
	28	+
	29	+## What would count as progress
	30	+
	31	+- An overall score above 0.5164 on the v2.0.0 datasets and metrics, computed with the task's `run_benchmark` workflow, with the method contributed as a component (code and container) so that the maintainers can rerun it.
	32	+- A method that beats scANVI on both the batch-removal and the bio-conservation metric groups, not only on their mean.
	33	+- A gain that holds on each of the six datasets separately (the leaderboard shows per-dataset scores), not only on the overall mean.
	34	+
	35	+## References
	36	+
	37	+1. Luecken MD, Büttner M, Chaichoompu K, et al. Benchmarking atlas-level data integration in single-cell genomics. Nature Methods 19, 41–50 (2022). [doi:10.1038/s41592-021-01336-8](https://doi.org/10.1038/s41592-021-01336-8)
	38	+2. 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)
	39	+3. Xu C, Lopez R, Mehlman E, et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Molecular Systems Biology 17, e9620 (2021). [doi:10.15252/msb.20209620](https://doi.org/10.15252/msb.20209620)
	40	+
	41	+## Source
	42	+
	43	+Imported from [Open Problems in Single-Cell Analysis: Batch Integration (task_batch_integration)](https://openproblems.bio/benchmarks/batch_integration), curated by the Open Problems in Single-Cell Analysis consortium. Open according to the source as of 2026-10-20 (leaderboard release v2.0.0). The statement above is written in our own words; see the source for the original wording, history and discussion.