{ "_id": "6ad3408090c8bbdf41a51917", "Package": "ADImpute", "Type": "Package", "Title": "Adaptive Dropout Imputer (ADImpute)", "Version": "1.23.0", "Description": "Single-cell RNA sequencing (scRNA-seq) methods are\ntypically unable to quantify the expression levels of all genes\nin a cell, creating a need for the computational prediction of\nmissing values (‘dropout imputation’). Most existing dropout\nimputation methods are limited in the sense that they\nexclusively use the scRNA-seq dataset at hand and do not\nexploit external gene-gene relationship information. Here we\npropose two novel methods: a gene regulatory network-based\napproach using gene-gene relationships learnt from external\ndata and a baseline approach corresponding to a sample-wide\naverage. ADImpute can implement these novel methods and also\ncombine them with existing imputation methods (currently\nsupported: DrImpute, SAVER). ADImpute can learn the best\nperforming method per gene and combine the results from\ndifferent methods into an ensemble.", "Authors@R": "person(\"Ana Carolina\", \"Leote\", email = \"anacarolinaleote@gmail.com\",\nrole = c(\"cre\",\"aut\"), comment = c(ORCID = \"0000-0003-0879-328X\"))", "License": "GPL-3 + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "biocViews": "GeneExpression, Network, Preprocessing, Sequencing,\nSingleCell, Transcriptomics", "RoxygenNote": "7.1.1", "VignetteBuilder": "knitr", "BugReports": "https://github.com/anacarolinaleote/ADImpute/issues", "Config/pak/sysreqs": "zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:54:17 UTC", "RemoteUrl": "https://github.com/bioc/ADImpute", "RemoteRef": "HEAD", "RemoteSha": "1bb2e1c2a794212deb7080ffe9fcee7b2d5a4808", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-17 09:26:36 UTC", "User": "root" }, "Author": "Ana Carolina Leote [cre, aut] (ORCID:\n)", "Maintainer": 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