{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:56Z","timestamp":1772138036575,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T00:00:00Z","timestamp":1585008000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German research foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004807","name":"DFG","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004807","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate School of Quantitative Biosciences Munich","award":["GSC 1006"],"award-info":[{"award-number":["GSC 1006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Dimensionality reduction is a key step in the analysis of single-cell RNA-sequencing data. It produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single-cell data. However, with no linear relation between gene and embedding coordinate, there is no way to extract the identity of genes driving any cell\u2019s position in the low-dimensional embedding, making it difficult to characterize the underlying biological processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this article, we introduce the concepts of local and global gene relevance to compute an equivalent of principal component analysis loadings for non-linear low-dimensional embeddings. Global gene relevance identifies drivers of the overall embedding, while local gene relevance identifies those of a defined sub-region. We apply our method to single-cell RNA-seq datasets from different experimental protocols and to different low-dimensional embedding techniques. This shows our method\u2019s versatility to identify key genes for a variety of biological processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>To ensure reproducibility and ease of use, our method is released as part of destiny 3.0, a popular R package for building diffusion maps from single-cell transcriptomic data. It is readily available through Bioconductor.<\/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\/btaa198","type":"journal-article","created":{"date-parts":[[2020,3,20]],"date-time":"2020-03-20T16:11:08Z","timestamp":1584720668000},"page":"4291-4295","source":"Crossref","is-referenced-by-count":10,"title":["Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0369-2888","authenticated-orcid":false,"given":"Philipp","family":"Angerer","sequence":"first","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health , Neuherberg 85764, Germany"},{"name":"TUM School of Life Sciences Weihenstephan, Technical University of Munich , Freising 85354, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1293-7656","authenticated-orcid":false,"given":"David S","family":"Fischer","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health , Neuherberg 85764, Germany"},{"name":"TUM School of Life Sciences Weihenstephan, Technical University of Munich , Freising 85354, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabian J","family":"Theis","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health , Neuherberg 85764, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Scialdone","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health , Neuherberg 85764, Germany"},{"name":"Institute of Epigenetics and Stem Cells, Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health , Neuherberg 85764, Germany"},{"name":"Institute of Functional Epigenetics, Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health , M\u00fcnchen 81377, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2154-4552","authenticated-orcid":false,"given":"Carsten","family":"Marr","sequence":"additional","affiliation":[{"name":"Institute of Computational Biology, Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health , Neuherberg 85764, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,3,24]]},"reference":[{"key":"2023062312041225400_btaa198-B1","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1093\/bioinformatics\/btv715","article-title":"Destiny: diffusion maps for large-scale single-cell data in R","volume":"32","author":"Angerer","year":"2016","journal-title":"Bioinformatics"},{"key":"2023062312041225400_btaa198-B2","author":"Angerer","year":"2017"},{"key":"2023062312041225400_btaa198-B3","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Syst"},{"key":"2023062312041225400_btaa198-B4","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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