---
name: r-statistical-model-serialization
description: Use when after training a customized R statistical or machine learning model on annotated training data, you need to persist the trained model object for reuse in downstream analysis workflows without retraining.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3443
  edam_topics:
  - http://edamontology.org/topic_0091
  - http://edamontology.org/topic_3520
  tools:
  - R
  - R 4.2.1
  - DNMS2Purifier_model_generation.r
  - DNMS2Purifier.r
  techniques:
  - LC-MS
derived_from:
- doi: 10.1021/acs.analchem.3c00736
  title: DNMS2Purifier
evidence_spans:
- The program is written in R (ver 4.2.1).
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_dnms2purifier_cq
    doi: 10.1021/acs.analchem.3c00736
    title: DNMS2Purifier
  dedup_kept_from: coll_dnms2purifier_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1021/acs.analchem.3c00736
  all_source_dois:
  - 10.1021/acs.analchem.3c00736
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# R statistical model serialization

## Summary

Serialize trained R statistical models to .rds binary files for persistence and downstream reuse in reproducible bioinformatic workflows. This skill ensures trained models (e.g., machine learning classifiers) can be saved, version-controlled, and reliably loaded in production pipelines.

## When to use

After training a customized R statistical or machine learning model on annotated training data, you need to persist the trained model object for reuse in downstream analysis workflows without retraining. Typical trigger: you have a fitted model in R memory and require it to be available to other scripts or users without access to the original training code or data.

## When NOT to use

- Model is still in development or requires frequent retraining; use version control on source scripts instead of serialized objects.
- Training data or model parameters are expected to change frequently; serialize only finalized, validated models.
- Cross-platform or cross-language portability is required; .rds is R-specific and not recommended for polyglot environments.

## Inputs

- Fitted R statistical or machine learning model object (in-memory)
- Training dataset with annotations (e.g., MS/MS spectra with purity labels in DDA mode)

## Outputs

- .rds serialized model file
- Deserialized model object for downstream analysis

## How to apply

Train your statistical or machine learning model in R 4.2.1 using your prepared training dataset (e.g., annotated MS/MS spectra with purity labels for DNMS2Purifier). After validation, serialize the fitted model object to a binary .rds file using R's native serialization function. Store the .rds file in a version-controlled repository or shared location. In downstream workflows, load the serialized model object from the .rds file and apply it to new data without retraining. Verify model loading by inspecting object structure and class attributes to confirm integrity post-deserialization.

## Related tools

- **R 4.2.1** (Runtime environment for model training and serialization using saveRDS() and readRDS() functions)
- **DNMS2Purifier_model_generation.r** (Script that trains the customized MS/MS purification model and serializes it to .rds for use in DNMS2Purifier.r workflow) — https://github.com/HuanLab/DNMS2Purifier
- **DNMS2Purifier.r** (Production script that loads and applies serialized purification models for MS/MS spectrum quality filtering) — https://github.com/HuanLab/DNMS2Purifier

## Examples

```
# In DNMS2Purifier_model_generation.r after model training:
saveRDS(trained_model, 'DNMS2Purifier_model.rds')
# In DNMS2Purifier.r for downstream use:
loaded_model <- readRDS('DNMS2Purifier_model.rds')
```

## Evaluation signals

- Serialized .rds file is created and has non-zero file size
- Deserialized model object class and attributes match the original fitted model (verify via class(), str(), or attributes() in R)
- Model predictions on held-out test data are identical before serialization and after deserialization (compare via all.equal() or identical())
- Downstream workflow (e.g., DNMS2Purifier.r) successfully loads and applies the model without errors or warnings
- Model object is not corrupted: object structure is valid and all slots/fields are accessible post-load

## Limitations

- .rds format is R-specific and not portable to Python, MATLAB, or other languages; use onnx or protobuf for cross-language interchange.
- Serialized models are sensitive to R version changes; models trained in R 4.2.1 may not deserialize reliably in significantly older or newer R versions.
- Large models may produce large .rds files; disk space and I/O time should be considered for high-throughput applications.
- No built-in versioning or changelog mechanism in .rds files; external metadata or repository tags are required to track model provenance and iterations.

## Evidence

- [other] Execute the model generation script to train the customized purification model on the prepared training dataset.: "Execute the model generation script to train the customized purification model on the prepared training dataset."
- [other] Serialize and save the trained model object to an .rds file for downstream use in the DNMS2Purifier.r purification workflow.: "Serialize and save the trained model object to an .rds file for downstream use in the DNMS2Purifier.r purification workflow."
- [readme] The program is written in R (ver 4.2.1).: "The program is written in R (ver 4.2.1)."
- [readme] we also provide the script DNMS2Purifier_model_generation.r for customized model training: "we also provide the script DNMS2Purifier_model_generation.r for customized model training"