{ "_id": "6a201303b401979e734497fd", "Package": "BUSseq", "Type": "Package", "Title": "Batch Effect Correction with Unknow Subtypes for scRNA-seq data", "Version": "1.19.0", "Date": "2025-10-14", "Description": "BUSseq R package fits an interpretable Bayesian\nhierarchical model---the Batch Effects Correction with Unknown\nSubtypes for scRNA seq Data (BUSseq)---to correct batch effects\nin the presence of unknown cell types. BUSseq is able to\nsimultaneously correct batch effects, clusters cell types, and\ntakes care of the count data nature, the overdispersion, the\ndropout events, and the cell-specific sequencing depth of\nscRNA-seq data. After correcting the batch effects with BUSseq,\nthe corrected value can be used for downstream analysis as if\nall cells were sequenced in a single batch. BUSseq can\nintegrate read count matrices obtained from different scRNA-seq\nplatforms and allow cell types to be measured in some but not\nall of the batches as long as the experimental design fulfills\nthe conditions listed in our manuscript.", "Authors@R": "c(person(given = \"Fangda\", family = \"Song\", role = c(\"aut\",\"cre\"), email = \"sfd1994895@gmail.com\", comment = c(ORCID = \"0000-0001-6007-3517\")), person(\"Ga Ming\", \"Chan\", role = c(\"aut\"), email = \"middletotheleft@gmail.com\"), person(\"Yingying\", \"Wei\", role = c(\"aut\"), email = \"ywei@cuhk.edu.hk\", comment = c(ORCID = \"0000-0003-3826-336X\")))", "License": "Artistic-2.0", "LazyData": "true", "Keywords": "Single-cell RNA-seq experiments; Batch effects; Model-based\nclustering; Integrative analysis", "VignetteBuilder": "knitr", "biocViews": "ExperimentalDesign, GeneExpression, StatisticalMethod,\nBayesian, Clustering, FeatureExtraction, BatchEffect,\nSingleCell, Sequencing", "URL": 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