Build PAVE vs TRACE CN bundle comparison data — compare_pave_cn_bundle

Classify CN seeds as `unmatched_blank` (PAVE non-blank, TRACE blank) or `matched_cn` (non-blank in both), then extract each seed's CN bundle from the MSdev object. Bundle correlation and m/z error are calculated from the CN pattern edges in each bundle.

Usage

compare_pave_cn_bundle_data(
  pave.trace.merged,
  object,
  i.pol = 0,
  mz_error = c("max", "mean")
)

Arguments

pave.trace.merged: Data frame from a PAVE/TRACE feature merge.
object: MSdev object with TRACE results in `object@advancedAna$TRACE_temp`.
i.pol: Polarity index. `0` for negative and `1` for positive.
mz_error: Character. Use `"max"` or `"mean"` absolute m/z error (ppm) across bundle edges.

Value

A data frame with one row per CN seed (`feature_id`), comparison group (`cn_cmp`), and bundle metrics `cn_bundle_cor` / `cn_bundle_mz_ppm`.