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bioc
BUSseq:Batch Effect Correction with Unknow Subtypes for scRNA-seq data
BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript.
Maintained by Fangda Song. Last updated 5 months ago.
experimentaldesigngeneexpressionstatisticalmethodbayesianclusteringfeatureextractionbatcheffectsinglecellsequencingcppopenmp
4.48 score 30 scriptsbioc
BUScorrect:Batch Effects Correction with Unknown Subtypes
High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity.
Maintained by Xiangyu Luo. Last updated 5 months ago.
geneexpressionstatisticalmethodbayesianclusteringfeatureextractionbatcheffect
4.00 score 2 scripts