Crate rsomics_bam_fingerprint

Expand description

ChIP-enrichment fingerprint, matching deeptools plotFingerprint default semantics.

§What the fingerprint is

The genome is sampled at evenly spaced windows (bins). Each bin’s value is the per-base read coverage summed over the bin. Sorting those per-bin values and plotting their normalised cumulative sum against the normalised bin rank gives a Lorenz curve: a diagonal means coverage is uniform (no enrichment), a sharp elbow near the right means a few bins hold most of the signal (strong ChIP enrichment).

§Deterministic sampling (source parity with deeptools)

deeptools does not pick bins at random. With the whole genome it sets stepSize = max(genomeSize / numberOfSamples, 1) and walks each chromosome at stepSize intervals, taking one bin [i, i + binSize) per step. The walk happens inside genome “chunks” (mapReduce partitions) of length chunkSize, and a bin that would cross a chunk boundary (i + binSize > chunkEnd) is dropped — so chunk geometry, not just stepSize, decides which bins exist. chunkSize itself is derived from the BAM’s mapped-read count:

reads_per_bp = max_mapped / genomeSize
chunkSize    = floor(stepSize * 1000 / (reads_per_bp * nBams))
chunkSize    = max(chunkSize, stepSize, binSize)

Reproducing all of this makes our sampled-bin set identical to deeptools’ when run with one BAM at --numberOfProcessors 1 (no task shuffle).

§Per-bin coverage rule (`SumCoveragePerBin`)

For each bin [reg0, reg1) (a single-tile region, tileSize = binSize): every read with FLAG & 0x4 == 0 is decomposed into aligned blocks (get_blocks(): reference-consuming M/=/X runs, split by D and N). Each block contributes min(blockEnd, reg1) - max(blockStart, reg0) bases (clamped to [0, binSize]) to the bin. Default filters: skip unmapped only — no MAPQ floor, no FLAG include/exclude, duplicates kept, reads not extended (extendReads=False → defaultFragmentLength == "read length").

§Output (`--out-raw-counts`)

Mirrors plotFingerprint --outRawCounts: a #plotFingerprint --outRawCounts header, a quoted label line, then one integer per sampled bin (the per-base coverage, formatted %d exactly as deeptools casts the float column). Bins appear in genome order (chromosome header order, ascending position).

§Fingerprint + summary (`--out-fingerprint`, `--out-quality-metrics`)

The fingerprint is the sorted, cumulative, max-normalised coverage vs the normalised rank — the data behind deeptools’ PNG. The quality metrics (AUC, X-intercept, elbow) use deeptools’ exact formulas over the sorted cumulative curve.

§Scope

We emit the data tables (raw counts, fingerprint curve, summary metrics), not the PNG plot — the same split as rsomics-bam-signal, which emits bedGraph rather than bigWig. Synthetic/JSD/CHANCE columns (only produced with --JSDsample against a reference BAM) are out of scope for this single-input crate.

Structs§

Fingerprint: The fingerprint result: per-bin per-base coverage in genome order.
FingerprintOpts
QualityMetrics

Functions§

fingerprint: Compute the per-bin per-base coverage fingerprint for input.
write_fingerprint: Write the fingerprint curve: rank<TAB>fraction, one row per sampled bin.
write_quality_metrics: Write the quality-metrics summary: deeptools’ AUC / X-intercept / elbow.
write_raw_counts: Write the --outRawCounts table: deeptools header, quoted label, one %d per sampled bin in genome order.