Extended Data Fig. 1: Comparative performance of the cosine similarity approach implemented in scMORE versus six traditional methods on single-cell data. | Nature Aging

Extended Data Fig. 1: Comparative performance of the cosine similarity approach implemented in scMORE versus six traditional methods on single-cell data.

From: Integrating polygenic signals and single-cell multiomics identifies cell-type-specific regulomes critical for immune- and aging-related diseases

Extended Data Fig. 1

a. Logistic regression; b. Averaged gene expression; c. Wilcoxon test; d. Wilcoxon test with tie correction (TIE); e. Student’s t-test (standard); f. Cosine similarity (scMORE); g. Student’s t-test with overestimated variance. Note: Dot plots display the top three cell type-specific genes identified by each method: logistic regression, average gene expression, Wilcoxon test, Wilcoxon test with TIE, Student’s t-test, Student’s t-test with overestimated variance, and cosine similarity metric in scMORE. Dot color indicates the mean expression of each marker gene in a given cell type, while dot size reflects the fraction of cells expressing that gene within the cell type. Data are derived from a single-cell multiomic dataset comprising 2,392 brain cells spanning 16 cell types (Flash-Frozen Human Healthy Brain Tissue (3k)), and was obtained from the 10x Genomics website (https://www.10xgenomics.com/datasets/frozen-human-healthy-brain-tissue-3-k-1-standard-2-0-0).

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