csv_to_bedpe


chr3d.utils.csv_to_bedpe(
    loops_csv: str,
    peaks_path: str,
    output_bedpe: str,
    p_value_col: str = "p_adj_fdr_bh",
    significant_col: str = "significant_fdr_bh",
    canonical_chroms_only: bool = True,
    max_score: float = 300.0,
) -> Tuple[pl.DataFrame, dict]

Convert loops CSV → BEDPE with importance score = -log10(p_value).

Uses Polars for fast, multi-threaded data processing. Returns both the final DataFrame and a statistics dictionary. Writes BEDPE to disk.

Parameters

Parameter	Type	Description
loops_csv	`str`	Path to loops CSV file
peaks_path	`str`	Path to narrowPeak file
output_bedpe	`str`	Path for output BEDPE file
p_value_col	`str`	Column name for p-values (default: `"p_adj_fdr_bh"`)
significant_col	`str`	Column name for significance flag (default: `"significant_fdr_bh"`)
canonical_chroms_only	`bool`	Filter to canonical chromosomes (default: `True`)
max_score	`float`	Maximum -log10(p) score (default: 300)

Returns:

Tuple[pl.DataFrame, dict] — (bedpe_dataframe, stats_dict)

Statistics keys:

'n_peaks': Number of peaks loaded
'n_loops_raw': Raw loops in CSV
'n_loops_significant': Loops passing significance filter
'n_after_join': Loops after joining peaks
'n_chrom_mismatch': Chromosome mismatches dropped
'n_swapped': Pairs reordered (start1 > start2)
'n_final': Final BEDPE rows
'total_seconds': Processing time