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Multi-target integration and annotation of single-cell RNA-sequencing data

Cells are the building blocks of human tissues and organs, and the distributions of different cell-types change due to environmental or disease conditions and treatments. Single-cell RNA sequencing is used to study heterogeneity of cells in biological samples. To date, computational approaches aided in the discovery of dominant and rare cell-types and facilitated the construction of cell atlases. Integration of new data with the existing reference atlases is an emerging computational problem, and this paper proposes to frame it as a multi-target prediction task, solvable using supervised machine learning. We systematically and rigorously test 63 different predictors on synthetic benchmarks with different properties. The best performing predictor has high Cohen's Kappa scores and low mean absolute errors in single-batch and multi-batch integration experiments.