Run Full bambamR Pipeline — bb

Orchestrates the complete RNA-seq analysis pipeline from FASTQ/BAM files or a count matrix through alignment, counting, normalization, differential expression, and visualization.

Usage

bb_pipeline(
  fastq_dir = NULL,
  bam_dir = NULL,
  count_matrix = NULL,
  output_dir = "bambamR_output",
  genome_index = NULL,
  annotation = NULL,
  sample_info = NULL,
  aligner = "STAR",
  de_method = c("DESeq2", "edgeR", "limma"),
  design = ~condition,
  skip = character(0),
  threads = 4L
)

Arguments

fastq_dir: Character or NULL. Directory containing FASTQ files. If provided, the pipeline starts from alignment.
bam_dir: Character or NULL. Directory containing BAM files. If provided, the pipeline starts from read counting.
count_matrix: A numeric matrix or NULL. Pre-computed count matrix. If provided, the pipeline starts from normalization/DE.
output_dir: Character. Output directory for results. Default "bambamR_output".
genome_index: Character or NULL. Path to genome index. Required if starting from FASTQ.
annotation: Character or NULL. Path to GTF annotation. Required if starting from FASTQ or BAM.
sample_info: A data.frame with sample metadata. Must contain a condition column (or the variable in design). Rownames should match sample identifiers.
aligner: Character. Aligner for FASTQ alignment. Default "STAR".
de_method: Character. DE method: "DESeq2", "edgeR", or "limma". Default "DESeq2".
design: A formula for DE analysis. Default ~ condition.
skip: Character vector. Steps to skip: "qc", "align", "count", "de", "viz". Default character(0).
threads: Integer. Number of threads. Default 4.

Value

A bb_result object containing counts, metadata, DE results, and plots.

Details

The pipeline allows entry at any stage:

From FASTQ: requires fastq_dir, genome_index, annotation, and sample_info
From BAM: requires bam_dir, annotation, and sample_info
From counts: requires count_matrix and sample_info

Examples

# \donttest{
# Starting from a count matrix (minimal mode, no Bioconductor needed)
set.seed(42)
counts <- matrix(rpois(600, 100), nrow = 100, ncol = 6,
  dimnames = list(paste0("gene", 1:100), paste0("S", 1:6)))
sample_info <- data.frame(
  condition = factor(rep(c("ctrl", "treat"), each = 3)),
  row.names = paste0("S", 1:6)
)
# result <- bb_pipeline(count_matrix = counts, sample_info = sample_info)
# }