{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:02:27Z","timestamp":1761490947030,"version":"3.37.3"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T00:00:00Z","timestamp":1581552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) has enabled the simultaneous transcriptomic profiling of individual cells under different biological conditions. scRNA-seq data have two unique challenges that can affect the sensitivity and specificity of single-cell differential expression analysis: a large proportion of expressed genes with zero or low read counts ('dropout' events) and multimodal data distributions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We have developed a zero-inflation-adjusted quantile (ZIAQ) algorithm, which is the first method to account for both dropout rates and complex scRNA-seq data distributions in the same model. ZIAQ demonstrates superior performance over several existing methods on simulated scRNA-seq datasets by finding more differentially expressed genes. When ZIAQ was applied to the comparison of neoplastic and non-neoplastic cells from a human glioblastoma dataset, the ranking of biologically relevant genes and pathways showed clear improvement over existing methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>ZIAQ is implemented in the R language and available at https:\/\/github.com\/gefeizhang\/ZIAQ.<\/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\/btaa098","type":"journal-article","created":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T20:10:54Z","timestamp":1581019854000},"page":"3124-3130","source":"Crossref","is-referenced-by-count":11,"title":["ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data"],"prefix":"10.1093","volume":"36","author":[{"given":"Wenfei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Programming , Sanofi, Framingham, MA 01701, USA"}]},{"given":"Ying","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Columbia University, New York, NY 10032, USA"}]},{"given":"Donghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Programming , Sanofi, Framingham, MA 01701, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7496-0835","authenticated-orcid":false,"given":"Ethan Y","family":"Xu","sequence":"additional","affiliation":[{"name":"Translational Sciences , Sanofi, Framingham, MA 01701, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,2,13]]},"reference":[{"key":"2023013112030659000_btaa098-B1","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1038\/s41388-017-0045-7","article-title":"Epidermal growth factor receptor and EGFRvIII in glioblastoma: signaling pathways and targeted therapies","volume":"37","author":"An","year":"2018","journal-title":"Oncogene"},{"key":"2023013112030659000_btaa098-B2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13059-016-0927-y","article-title":"Design and computational analysis of single-cell RNA-sequencing experiments","volume":"17","author":"Bacher","year":"2016","journal-title":"Genome Biol"},{"key":"2023013112030659000_btaa098-B3","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1038\/s41419-018-0709-4","article-title":"Activation of neutral sphingomyelinase 2 by starvation induces cell-protective autophagy via an increase in Golgi-localized ceramide","volume":"9","author":"Back","year":"2018","journal-title":"Cell Death Dis"},{"key":"2023013112030659000_btaa098-B4","first-page":"215","article-title":"Combining significance levels","author":"Becker","year":"1994","journal-title":"The Handbook of Research Synthesis"},{"key":"2023013112030659000_btaa098-B5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1186\/s13059-016-1033-x","article-title":"Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm","volume":"17","author":"Chu","year":"2016","journal-title":"Genome Biol"},{"key":"2023013112030659000_btaa098-B7","doi-asserted-by":"crossref","first-page":"62","DOI":"10.3389\/fgene.2017.00062","article-title":"Single-cell RNA-sequencing: assessment of differential expression analysis methods","volume":"8","author":"Dal Molin","year":"2017","journal-title":"Front. 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