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Leveraging single-cell reference atlases to analyze new data has brought about a paradigm shift in single-cell data science akin to the first reference genome in genomics. However, methods for performing this mapping require computational expertise and, oftentimes, considerable compute power, limiting access for researchers who may benefit the most. Here ArchMap, a no-code query-to-reference mapping tool, removes this barrier by providing all-in-one automated mapping, cell-type annotation and collaborative features to analyze single-cell datasets from a wide range of integrated, often published, reference atlases and allows the extension of atlases with the growing Human Cell Atlas and related efforts. This paves the way for a democratization of reference mapping capabilities.
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Lotfollahi et al. (2026) studied this question.
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