Struct FeatureMatrix 

pub struct FeatureMatrix {
    pub run_names: Vec<String>,
    pub features: Vec<Feature>,
    pub matrix_evidence: Option<Vec<[f32; 9]>>,
    pub joint_evidence: Option<Vec<Vec<Observation>>>,
    pub joint_cell_evidence: Option<Vec<[f32; 12]>>,
}

A single-run or aligned multi-run feature set.

Fields

run_names: Vec<String>

Run intensity column names in vector order.

features: Vec<Feature>

matrix_evidence: Option<Vec<[f32; 9]>>

Optional LFQ evidence, one vector per retained consensus feature. Absent for ordinary matrices and single-run inputs: their classifier vectors keep the exact original dimensions and column order.

joint_evidence: Option<Vec<Vec<Observation>>>

Original, aligned observations per consensus row for one joint search. Quantitative cells remain on features; observations never duplicate them.

joint_cell_evidence: Option<Vec<[f32; 12]>>

Extraction summaries per joint row, independent of candidate identity.

Implementations

impl FeatureMatrix

pub fn len(&self) -> usize

pub fn is_empty(&self) -> bool

pub fn n_samples(&self) -> usize

Number of runs; 1 for single-run input.

pub fn load(features_path: impl AsRef<Path>) -> Result<Self>

Load a single-run biosaur2 feature file (*.features.tsv).

pub fn load_matrix(path: impl AsRef<Path>, min_runs: u32) -> Result<Self>

Load a koth-aligned consensus matrix (intensity_matrix.tsv): one row per consensus feature, with the leading columns massCalib, mz, charge, rtApex, im, combined_score, seed_run, n_contributing_runs followed by one intensity column per run. Unlike single-run input, each feature carries the FULL per-run intensity vector (intensities) — that’s what the multi-sample quant rollup consumes, and the ID featurizer already reads n_detected (runs with signal) and sum_int from it. The per-feature single-run quality columns (cosine/isotope/ppm/nScans) are absent here and read as None (inert in the featurizer); combined_score and n_contributing_runs survive alignment and are used.

min_runs drops consensus features detected in fewer than that many runs. This filter is REQUIRED for aligned-matrix search, not cosmetic: alignment discards the per-feature nIsotopes column that uno’s single-run path uses to reject junk, so without a reproducibility floor the random-match background p climbs to ~0.83 and the binomial finds nothing. A real peptide is detected across replicates; single-run features are dominated by noise. min_runs = 2 is a safe floor (won’t drop a condition-specific peptide that has ≥2 replicates).

Trait Implementations

impl Clone for FeatureMatrix

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for FeatureMatrix

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.