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Recently, various computational methods have been developed to analyze the scRNAseq data, such as clustering and visualization. However, current visualization methods, including t-SNE and UMAP, are challenged by the limited accuracy of rendering the geometric relationship of populations with distinct functional states. Most visualization methods are unsupervised, leaving out information from the clustering results or given labels. This leads to the inaccurate depiction of the distances between the bona fide functional states. In particular, UMAP and t-SNE are not optimal to preserve the global geometric structure. They may result in a contradiction that clusters with near distance in the embedded dimensions are in fact further away in the original dimensions. Besides, UMAP and t-SNE cannot track the variance of clusters. Through the embedding of t-SNE and UMAP, the variance of a cluster is not only associated with the true variance but also is proportional to the sample size.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present supCPM, a robust supervised visualization method, which separates different clusters, preserves the global structure and tracks the cluster variance. Compared with six visualization methods using synthetic and real datasets, supCPM shows improved performance than other methods in preserving the global geometric structure and data variance. Overall, supCPM provides an enhanced visualization pipeline to assist the interpretation of functional transition and accurately depict population segregation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The R package and source code are available at https:\/\/zenodo.org\/record\/5975977#.YgqR1PXMJjM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac131","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T15:12:06Z","timestamp":1646320326000},"page":"2496-2503","source":"Crossref","is-referenced-by-count":15,"title":["Supervised capacity preserving mapping: a clustering guided visualization method for scRNA-seq data"],"prefix":"10.1093","volume":"38","author":[{"given":"Zhiqian","family":"Zhai","sequence":"first","affiliation":[{"name":"Department of Computational Mathematics, Science and Engineering, Michigan State University , East Lansing, MI 48824, USA"},{"name":"Department of Statistics, University of California Los Angeles , Los Angeles, CA 90095, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9868-9824","authenticated-orcid":false,"given":"Yu L","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry , Ann Arbor, MI 48109, USA"},{"name":"University of Michigan Rogel Cancer Center , Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5084-977X","authenticated-orcid":false,"given":"Rongrong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics, Science and Engineering, Michigan State University , East Lansing, MI 48824, USA"},{"name":"Department of Mathematics, Michigan State University , East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1049-2219","authenticated-orcid":false,"given":"Yuying","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics, Science and Engineering, Michigan State University , East Lansing, MI 48824, USA"},{"name":"Department of Statistics and Probability, Michigan State University , East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"2023041402552610900_","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1016\/j.patcog.2010.12.015","article-title":"Supervised principal component analysis: visualization, classification and regression on subspaces and submanifolds","volume":"44","author":"Barshan","year":"2011","journal-title":"Pattern Recognit"},{"key":"2023041402552610900_","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using UMAP","volume":"37","author":"Becht","year":"2019","journal-title":"Nat. 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