{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:03Z","timestamp":1772138043562,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2021,2,24]],"date-time":"2021-02-24T00:00:00Z","timestamp":1614124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01MH123184"],"award-info":[{"award-number":["R01MH123184"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R37MH057881"],"award-info":[{"award-number":["R37MH057881"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Gene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Therefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The ESCO implementation is available as R package ESCO. Users can either download the development version via github (https:\/\/github.com\/JINJINT\/ESCO) or the archived version via Zenodo (https:\/\/zenodo.org\/record\/4455890).<\/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\/btab116","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T16:14:46Z","timestamp":1613751286000},"page":"2374-2381","source":"Crossref","is-referenced-by-count":28,"title":["ESCO: single cell expression simulation incorporating gene co-expression"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3537-6430","authenticated-orcid":false,"given":"Jinjin","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3828-4400","authenticated-orcid":false,"given":"Jiebiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Pittsburgh , Pittsburgh, PA 15261, USA"}]},{"given":"Kathryn","family":"Roeder","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"},{"name":"Computational Biology Department, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,2,24]]},"reference":[{"key":"2023051609120890200_btab116-B1","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.12688\/f1000research.16613.1","article-title":"False signals induced by single-cell imputation","volume":"7","author":"Andrews","year":"2018","journal-title":"F1000Research"},{"key":"2023051609120890200_btab116-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1812-2","article-title":"Metacell: analysis of single-cell RNA-seq data using k-nn graph partitions","volume":"20","author":"Baran","year":"2019","journal-title":"Genome Biol"},{"key":"2023051609120890200_btab116-B4","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.cels.2020.08.003","article-title":"Sergio: a single-cell expression simulator guided by gene regulatory networks","volume":"11","author":"Dibaeinia","year":"2020","journal-title":"Cell Syst"},{"key":"2023051609120890200_btab116-B7","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nmeth.2930","article-title":"Validation of noise models for single-cell transcriptomics","volume":"11","author":"Gr\u00fcn","year":"2014","journal-title":"Nat. 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