koth-ms 0.10.0

High-performance LC-MS feature finder for timsTOF and mzML data
Documentation 
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use super::*;
use crate::lfq::LfqEntry;

/// Minimal `LfqEntry` for TDC tests — only the four fields `compute_qvalues`
/// reads (feature_idx, run_idx, hybrid_score, is_decoy) are meaningful.
fn entry(feature_idx: usize, run_idx: usize, score: f32, is_decoy: bool) -> LfqEntry {
    LfqEntry {
        owned_samples: 0,
        preceding_signal_fraction: 0.0,
        excluded_samples: 0,
        competing_feature: None,
        ownership_status: "exclusive",
        feature_idx,
        run_idx,
        intensity: 1.0,
        hybrid_score: score,
        spectral_bhattacharyya: 0.0,
        n_isotopes_found: 0,
        rt_score: 0.0,
        int_score: 0.0,
        coelution: 0.0,
        is_decoy,
        is_mbr: false,
        expected_rt: 0.0,
        apex_rt: 0.0,
        peak_width_rt: 0.0,
        expected_mz: 0.0,
        observed_mz: 0.0,
        expected_im: 0.0,
        observed_im: 0.0,
    }
}

/// With no decoys the running FDR is 0 at every target, so every q-value is 0.
#[test]
fn all_targets_get_zero_q() {
    let entries = vec![
        entry(0, 0, 0.9, false),
        entry(1, 0, 0.8, false),
        entry(2, 0, 0.7, false),
    ];
    let q = compute_qvalues(&entries);
    assert_eq!(q.len(), 3);
    for v in q.values() {
        assert_eq!(*v, 0.0);
    }
}

/// Hand-computed interleaving: scores desc T(1.0) D(0.9) T(0.8) T(0.7).
/// Forward FDR: T@1.0 → 0/1=0, T@0.8 → 1/2=0.5, T@0.7 → 1/3. Backward
/// monotonisation (min at-or-below rank): 0 / 1/3 / 1/3.
#[test]
fn running_fdr_counts_decoys_over_targets_and_monotonises() {
    let entries = vec![
        entry(0, 0, 1.0, false),
        entry(9, 0, 0.9, true),
        entry(1, 0, 0.8, false),
        entry(2, 0, 0.7, false),
    ];
    let q = compute_qvalues(&entries);
    assert!((q[&(0, 0)] - 0.0).abs() < 1e-9);
    assert!(
        (q[&(1, 0)] - 1.0 / 3.0).abs() < 1e-9,
        "q was {}",
        q[&(1, 0)]
    );
    assert!(
        (q[&(2, 0)] - 1.0 / 3.0).abs() < 1e-9,
        "q was {}",
        q[&(2, 0)]
    );
    // decoy entries are not reported
    assert_eq!(q.len(), 3);
}

/// Q-values must be monotone non-decreasing as score falls (the backward
/// pass guarantees a higher-scoring target never has a larger q than a
/// lower-scoring one).
#[test]
fn qvalues_monotone_in_score() {
    let entries = vec![
        entry(0, 0, 0.95, false),
        entry(1, 0, 0.90, true),
        entry(2, 0, 0.85, false),
        entry(3, 0, 0.80, false),
        entry(4, 0, 0.75, true),
        entry(5, 0, 0.70, false),
    ];
    let q = compute_qvalues(&entries);
    // targets in descending score order
    let qs: Vec<f64> = [(0, 0), (2, 0), (3, 0), (5, 0)]
        .iter()
        .map(|k| q[k])
        .collect();
    for w in qs.windows(2) {
        assert!(
            w[1] >= w[0] - 1e-12,
            "q not monotone non-decreasing: {qs:?}"
        );
    }
}