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 ca
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Correct batch effects, cluster unknown cell types, identify intrinsic genes, and obtain batch-corrected expression data from multi-batch scRNA-seq count data.
library(BUSseq)
library(SingleCellExperiment)
# 1. Read user input files and prepare data
CountData <- assay(BUSseqfits_example, "counts")
batch_ind <- unlist(colData(BUSseqfits_example))
sce_input <- SingleCellExperiment(
assays = list(counts = CountData),
colData = DataFrame(Batch_ind = factor(batch_ind))
)
# 2. Conduct MCMC sampling and posterior inference
BUSseqfits_res <- BUSseq_MCMC(
ObservedData = sce_input,
seed = 1234,
n.cores = 2,
n.celltypes = 4,
n.iterations = 500
)
# 3. Extract the imputed read counts, estimated cell types, and batch effects
Imputed_count <- assay(BUSseqfits_res, "imputed_data")
celltyes_est <- celltypes(BUSseqfits_res)
location_batch_effects_est <- location_batch_effects(BUSseqfits_res)
overdispersion_est <- overdispersions(BUSseqfits_res)
cell_effects_est <- cell_effect_values(BUSseqfits_res)
celltype_effects_est <- celltype_effects(BUSseqfits_res)
# 4. Identify intrinsic genes that drive cell-type differences
intrinsic_gene_indicators <- intrinsic_genes_BUSseq(BUSseqfits_res)
index_intri <- which(unlist(intrinsic_gene_indicators) == "Yes")
# 5. Compute and incorporate batch-corrected read counts
BUSseqfits_res <- corrected_read_counts(BUSseqfits_res)
# 6. Visualize the raw, imputed, and corrected expression data
heatmap_data_BUSseq(BUSseqfits_res, data_type = "Raw",
project_name = "BUSseq_raw_allgenes",
image_dir = "./heatmap")
heatmap_data_BUSseq(BUSseqfits_res, data_type = "Imputed",
project_name = "BUSseq_imputed_allgenes",
image_dir = "./heatmap")
heatmap_data_BUSseq(BUSseqfits_res, data_type = "Corrected",
project_name = "BUSseq_corrected_allgenes",
image_dir = "./heatmap")
Note: Input is a SingleCellExperiment object containing raw counts and batch indicators; output is a SingleCellExperiment object containing imputed and batch-corrected assays.
intrinsic_genes_BUSseq.celltype_mean_expression and batch-corrected counts using corrected_read_counts.limma or ComBat because BUSseq requires raw count data to model the negative binomial distribution and dropout events.SingleCellExperiment object with a "counts" assay, or as a list of matrices where each element represents a batch.colData of the SingleCellExperiment object.SingleCellExperiment object or a list representing the raw count data.heatmap_data_BUSseq ("Raw", "Imputed", or "Corrected").BUSseq_MCMC.n.celltypes and use the Bayesian Information Criterion (BIC) to select the optimal number.corrected_read_counts to obtain batch-corrected expression data for downstream analyses.n.iterations and adjust n.burnin to ensure proper posterior sampling.Seurat for alternative single-cell integration and clustering.scater for single-cell QC and visualization.scran for single-cell normalization and pooling.No reviews yet. Tell others what you thought.
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