Busseq

BUSseq

Workflows

Standard Workflow

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.

When to Use

• To simultaneously correct batch effects and cluster cells into unknown cell types using single-cell RNA-seq data.
• To model count data overdispersion and dropout events using a Bayesian hierarchical model.
• To identify intrinsic genes driving cell-type differences using intrinsic_genes_BUSseq.
• To extract cell-type-specific mean expression levels using celltype_mean_expression and batch-corrected counts using corrected_read_counts.

When NOT to Use

• For normalized or log-transformed expression data; use packages like limma or ComBat because BUSseq requires raw count data to model the negative binomial distribution and dropout events.
• When the gene sets differ across batches; all input batches must have the exact same genes.

Data Requirements

• Raw count data formatted as a SingleCellExperiment object with a "counts" assay, or as a list of matrices where each element represents a batch.
• Batch indicators provided as a factor in colData of the SingleCellExperiment object.

Key Parameters

• ObservedData: A SingleCellExperiment object or a list representing the raw count data.
• seed (1234): Random seed for reproducible MCMC sampling.
• n.cores (2): Number of CPU cores to use for parallel computing.
• n.celltypes (4): The specified number of cell types present in the population.
• n.iterations (500): Total number of MCMC iterations.
• n.burnin (n.iterations/2): Number of burn-in iterations discarded before posterior inference.
• data_type ("Raw"): The type of data to plot in heatmap_data_BUSseq ("Raw", "Imputed", or "Corrected").

Best Practices

• Ensure that the gene numbers and gene names of all batches are identical before running BUSseq_MCMC.
• If the number of cell types is unknown, vary n.celltypes and use the Bayesian Information Criterion (BIC) to select the optimal number.
• Use corrected_read_counts to obtain batch-corrected expression data for downstream analyses.

Common Pitfalls

• MCMC chain fails to converge: Increase n.iterations and adjust n.burnin to ensure proper posterior sampling.
• High false discovery rate in intrinsic gene identification: Adjust the FDR threshold or increase the number of iterations to improve posterior estimation.

Alternatives

• Seurat for alternative single-cell integration and clustering.
• scater for single-cell QC and visualization.
• scran for single-cell normalization and pooling.

Citations

• Song, F., Chan, G. M., & Wei, Y. (2020). Flexible and interpretable Bayesian hierarchical model for batch effect correction in scRNA-seq.

References

• Homepage: bioconductor.org/packages/busseq
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/busseq/inst/doc/BUSseq_user_guide.html