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Filtering genes is suggested to improve the performance of DE methods, but the influence of filtering genes has not been demonstrated. Furthermore, the optimal methods for different scRNA-seq datasets are divergent, and different datasets should benefit from data-specific DE gene detection strategies. However, existing tools did not take gene filtering into consideration. There is a lack of metrics for evaluating the optimal method on experimental datasets. Based on two new metrics, we propose single-cell Consensus Optimization of Differentially Expressed gene detection, an R package to automatically optimize DE gene detection for each experimental scRNA-seq dataset.<\/jats:p>","DOI":"10.1093\/bib\/bbac180","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T11:10:04Z","timestamp":1650625804000},"source":"Crossref","is-referenced-by-count":17,"title":["scCODE: an R package for data-specific differentially expressed gene detection on single-cell RNA-sequencing data"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1549-0340","authenticated-orcid":false,"given":"Jiawei","family":"Zou","sequence":"first","affiliation":[{"name":"School of Life Sciences and Biotechnology, Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University , Shanghai, China"},{"name":"Institute of Clinical Science, Zhongshan Hospital, Fudan University , Shanghai, 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