Package: BUSseq 1.11.0

Fangda Song

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.

Authors:Fangda Song [aut, cre], Ga Ming Chan [aut], Yingying Wei [aut]

BUSseq_1.11.0.tar.gz
BUSseq_1.11.0.zip(r-4.5)BUSseq_1.11.0.zip(r-4.4)BUSseq_1.11.0.zip(r-4.3)
BUSseq_1.11.0.tgz(r-4.4-arm64)BUSseq_1.11.0.tgz(r-4.4-x86_64)BUSseq_1.11.0.tgz(r-4.3-arm64)BUSseq_1.11.0.tgz(r-4.3-x86_64)
BUSseq_1.11.0.tar.gz(r-4.5-noble)BUSseq_1.11.0.tar.gz(r-4.4-noble)
BUSseq.pdf |BUSseq.html✨
BUSseq/json (API)
NEWS

# Install 'BUSseq' in R:
install.packages('BUSseq', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/songfd2018/busseq/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On BioConductor:BUSseq-1.11.0(bioc 3.20)BUSseq-1.10.0(bioc 3.19)

bioconductor-package

15 exports 0.49 score 34 dependencies

Last updated 2 months agofrom:976e904875

Exports:baseline_expression_valuesBIC_BUSseqBUSseq_MCMCcell_effect_valuescelltype_effectscelltype_mean_expressioncelltypescorrected_read_countsdropout_coefficient_valuesheatmap_data_BUSseqimputed_read_countsintrinsic_genes_BUSseqlocation_batch_effectsoverdispersionsraw_read_counts

Dependencies:abindaskpassBiobaseBiocGenericsbitopscaToolscrayoncurlDelayedArrayGenomeInfoDbGenomeInfoDbDataGenomicRangesgplotsgtoolshttrIRangesjsonliteKernSmoothlatticeMatrixMatrixGenericsmatrixStatsmimeopensslR6S4ArraysS4VectorsSingleCellExperimentSparseArraySummarizedExperimentsysUCSC.utilsXVectorzlibbioc

BUScorrect_user_guide

Rendered fromBUSseq_user_guide.Rnwusingknitr::knitron Jun 30 2024.

Last update: 2021-06-01
Started: 2021-05-07

Readme and manuals

Help Manual

Help pageTopics
Batch Effect Correction with Unknow Subtypes for scRNA-seq dataBUSseq-package BUSseq
Obtain the Log-Scale Baseline Expression Levels from the Output of the 'BUSseq_MCMC' Functionbaseline_expression_values
Obtain BIC from the Output of the 'BUSseq_MCMC' FunctionBIC_BUSseq
Condcut MCMC sampling and posterior inference for the BUSseq ModelBUSseq_MCMC
An external example of the output of the 'BUSseq_MCMC'BUSseqfits_example
Obtain the cell-specific size effects from the Output of the 'BUSseq_MCMC' Functioncell_effect_values
Obtain the Cell-type Effects from the Output of the 'BUSseq_MCMC' Functioncelltype_effects
Obtain the Cell-Type-Specific Mean Expression Levels from the Output of the 'BUSseq_MCMC' Functioncelltype_mean_expression
Obtain the Cell-type Indicators from the Output of the 'BUSseq_MCMC' Functioncelltypes
Generate the Corrected Read Count Matrix within the Output of the 'BUSseq_MCMC'corrected_read_counts
Obtain the Coefficients of the Logistic Regression for the Dropout Events from the Output of the 'BUSseq_MCMC' Functiondropout_coefficient_values
Draw the Heatmap of the Log-scale Read Count Data for the Output of the 'BUSseq_MCMC' Functionheatmap_data_BUSseq
Obtain the Imputed Read Count Matrix from the Output of the 'BUSseq_MCMC' Functionimputed_read_counts
Obtain the Intrinsic Gene Indicators from the Output of the 'BUSseq_MCMC' Functionintrinsic_genes_BUSseq
Obtain the Location Batch Effects from the Output of the 'BUSseq_MCMC' Functionlocation_batch_effects
Obtain the Overdispersion Parameters from the Output of the 'BUSseq_MCMC' Functionoverdispersions
Obtain the Raw Read Count Matrix from the Output of the 'BUSseq_MCMC' Functionraw_read_counts