Algorithm documentation¶
This section explains the algorithms and data structures behind each pathotypr module — the core ideas, not the command-line switches.
Each page below focuses on a single module: the concept it implements, the data structures it relies on, and how the pieces fit together end to end. For command-line usage and options, see the command guides in See also.
Tip
The table is ordered roughly along the pathotypr pipeline — from turning k-mers into feature vectors, through training and prediction, to marker- and reference-based calling. Reading it top to bottom is a good first pass.
| Module | Document | Core Idea |
|---|---|---|
| Feature Hashing | feature-hashing.md | The hashing trick: k-mers → fixed-size sparse vectors |
| Random Forest | random-forest.md | Sparse CART trees with bootstrap aggregation |
| Training Pipeline | training.md | End-to-end: vectorize → evaluate → train → OOB → export |
| Prediction | prediction.md | Streaming batch prediction with majority voting |
| Marker Genotyping | marker-genotyping.md | Diagnostic k-mers + Bloom filter for FASTQ scanning |
| Reference Matching | reference-matching.md | K-mer containment scoring with streaming batches |
| Assembly Classification | assembly-classification.md | Marker calling on FASTA assemblies with GFF annotation |