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It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote single-cell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483\u2013486, 2017), as the best method for classifying single-cell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised <jats:italic>adapted single-cell consensus clustering (adaSC3)<\/jats:italic> suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of <jats:italic>adaSC<\/jats:italic>3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of <jats:italic>adaSC<\/jats:italic>3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of <jats:italic>adaSC<\/jats:italic>3.<\/jats:p>","DOI":"10.1007\/s11634-020-00428-1","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T10:06:07Z","timestamp":1608026767000},"page":"885-896","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adapted single-cell consensus clustering (adaSC3)"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8354-6660","authenticated-orcid":false,"given":"Cornelia","family":"Fuetterer","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1854-6226","authenticated-orcid":false,"given":"Thomas","family":"Augustin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3565-8315","authenticated-orcid":false,"given":"Christiane","family":"Fuchs","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,15]]},"reference":[{"issue":"8","key":"428_CR1","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1093\/bioinformatics\/btv715","volume":"32","author":"P Angerer","year":"2016","unstructured":"Angerer P, Haghverdi L, B\u00fcttner M, Theis FJ, Marr C, Buettner F (2016) Destiny: diffusion maps for large-scale single-cell data in R. 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