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. 2023 Aug 23;39(9):btad521. doi: 10.1093/bioinformatics/btad521

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© The Author(s) 2023. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 2. — The algorithm design of CellAnn. (A) The CellAnn algorithm consists of three modules: (1) Module 1: CellAnn calculates the correlations between query clusters and clusters in reference and background. The cutoff is estimated based on the correlation between query clusters and background references. (2) Module 2: CellAnn calculates the correlations and estimates correlation cutoffs between the query data and sub-clusters in reference datasets. (3) Module 3: CellAnn performs the Wilcoxon rank-sum test to determine cell types further if a query cluster is similar to multiple sub-clusters in the reference.