Background: Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal.Results: We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression.Conclusion: Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.

Tissue macrophages have a split personality: Resident tissue macrophages (RTMs) reside in various tissue-specific niches during development. They evince microenvironment-directed phenotypes that support host defense and tissue homeostasis. Chakarov et al. used single-cell RNA sequencing and fate-mapping of murine lung RTMs to interrogate RTM-subset heterogeneity, interrelationships, and ontogeny (see the Perspective by Mildner and Yona). In addition to alveolar macrophages, they identified two different interstitial macrophage populations. One population mostly abutted nerve fibers; the other population preferentially localized near blood vessels and appeared to support vessel integrity and inhibit inflammatory cell infiltration into tissues. Science , this issue p. eaau0964 ; see also p. 1154

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.

Tracing development of the dendritic cell lineage: Dendritic cells (DCs) are important components of the immune system that form from the bone marrow into two major cell lineages: plasmacytoid DCs and conventional DCs. Seeet al.applied single-cell RNA sequencing and cytometry by time-of-flight to characterize the developmental pathways of these cells. They identified blood DC precursors that shared surface markers with plasmacytoid DCs but that were functionally distinct. This unsuspected level of complexity in pre-DC populations reveals additional cell types and refines understanding of known cell types.Science, this issue p.eaag3009