Prepare Single-Cell Long-Read Data — mpaqt_prepare_long_reads

Process single-cell long-read data with cluster aggregation. For single-cell long reads, quantification is typically performed at the cluster level rather than per-cell due to coverage limitations.

mpaqt_prepare_long_reads_sc(
  index,
  count_file,
  clusters_file,
  rds_dir = NULL,
  output_prefix = NULL,
  output_dir,
  verbose = TRUE
)

Arguments

index: An mpaqt_index object
count_file: Path to count matrix (transcript_id + barcode columns)
clusters_file: Path to cluster assignment file (barcode, cluster columns)
rds_dir: Directory containing pre-computed cluster RDS files (optional)
output_prefix: Prefix for output RDS files (default: "mpaqt"). Output files will be named {output_prefix}.{cluster_id}.long_read.rds
output_dir: Output directory for results
verbose: Print progress messages (default: TRUE)

Value

A list of mpaqt_counts_lr objects, one per cluster (invisibly)

Examples

if (FALSE) { # \dontrun{
idx <- mpaqt_read_index("my_index/mpaqt.index.rds")

# From count matrix
lr_counts_list <- mpaqt_prepare_long_reads_sc(
    index = idx,
    count_file = "lr_count_matrix.csv",
    clusters_file = "clusters.csv",
    output_dir = "results"
)

# With custom output prefix
lr_counts_list <- mpaqt_prepare_long_reads_sc(
    index = idx,
    count_file = "lr_count_matrix.csv",
    clusters_file = "clusters.csv",
    output_prefix = "my_sample",
    output_dir = "results"
)
} # }