# Loess Smoothing Parameter Tuning

> Use when your metabolomics SummarizedExperiment object shows signal drift across replicate measurements within individual batches (visible as systematic trends in PCA plots or replicate scatter), and you have intra-batch replicate samples available to estimate smooth drift corrections.

- Skill: `holobiomicslab/loess-smoothing-parameter-tuning` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/loess-smoothing-parameter-tuning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/loess-smoothing-parameter-tuning/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/loess-smoothing-parameter-tuning

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# loess-smoothing-parameter-tuning

## Summary

Apply loess smoothing with optimized parameters (e.g., loessShort with k=5) to correct intra-batch signal drift in metabolomics assays. This non-linear smoothing technique removes unwanted variation within experimental batches while preserving true biological signal.

## When to use

Your metabolomics SummarizedExperiment object shows signal drift across replicate measurements within individual batches (visible as systematic trends in PCA plots or replicate scatter), and you have intra-batch replicate samples available to estimate smooth drift corrections. Use this when batch effects manifest as continuous signal degradation rather than discrete batch offsets.

## When NOT to use

- Input assay has not been log-transformed or imputed (rawImpute assay does not exist).
- Batch effects are discrete/offset-based rather than drift-based (consider robust linear smoothing instead).
- No intra-batch replicate samples are available to estimate smooth drift corrections.
- Metabolite data is already normalised by an orthogonal method (e.g., probabilistic quotient normalisation).

## Inputs

- SummarizedExperiment object with rawImpute assay (log-transformed, imputed metabolomics signals)
- batch metadata column identifying experimental run/batch membership
- replicate metadata column identifying intra-batch and inter-batch replicate samples

## Outputs

- loessShort_concatenate assay (normalised metabolomics signals with intra-batch drift corrected)
- SummarizedExperiment object with updated assay slot containing corrected signals

## How to apply

Within the hRUV hierarchical normalisation framework, apply loess smoothing as the intra-batch normalisation step by setting intra='loessShort' and intra_k=5 (the number of short replicate neighbors for RUV-III estimation). The loessShort variant applies non-linear smoothing to metabolite signals across the sequence of replicate samples within each batch, correcting for instrumental signal drift. The k=5 parameter controls the local neighborhood size for the RUV-III algorithm that estimates unwanted variation from replicate correlations. After smoothing, extract the resulting loessShort_concatenate assay from the returned SummarizedExperiment object. This approach is applied after log transformation and k-nearest neighbor imputation on the rawImpute assay.

## Related tools

- **hRUV** (Wrapper function implementing hierarchical RUV-based normalisation; calls loess smoothing as intra-batch normalisation step with parameter intra='loessShort') — https://github.com/SydneyBioX/hRUV
- **RUV-III** (Statistical algorithm for unwanted variation estimation using replicates; applied post-loess smoothing to refine intra-batch correction) — https://github.com/SydneyBioX/hRUV
- **SummarizedExperiment** (Bioconductor data structure holding metabolomics assay matrix, batch/replicate metadata, and output normalised assays) — https://bioconductor.org/packages/SummarizedExperiment

## Examples

```
hRUV::hRUV(dat, intra='loessShort', inter='concatenate', intra_k=5, inter_k=5)
```

## Evaluation signals

- PCA plot of loessShort_concatenate assay shows reduced within-batch scatter and replicate clustering compared to rawImpute assay.
- Replicate samples within each batch have correlated values in the loessShort_concatenate assay (Pearson r > 0.95 within replicates).
- Metabolite signal drift across batch sequence (e.g., time-ordered instrumental degradation) is visually attenuated or eliminated in smoothed assay.
- MA plots or Bland-Altman plots between intra-batch replicates show symmetric scatter around zero post-correction.
- Output assay dimensions match input rawImpute assay (same features × samples); no features or samples are dropped.

## Limitations

- loessShort parameter choice (k=5) is fixed empirically; no automatic parameter selection method is provided for datasets with different replicate spacing or batch sizes.
- Loess smoothing assumes monotonic or smooth drift; it may over-smooth sharp instrumental transitions or sample preparation artifacts.
- Effectiveness depends critically on intra-batch replicate density and placement; sparse or irregularly spaced replicates may yield poor drift estimates.
- No changelog is available; version stability and backward compatibility of loessShort implementation across hRUV releases are undocumented.

## Evidence

- [intro] For intra batch normalisation, we perform loess smoothing on samples and RUV-III using short replicates with parameter k set to 5: "For intra batch normalisation, we perform loess smoothing on samples and RUV-III using short replicates with parameter k set to 5"
- [full_text] Call the hRUV function with parameters: intra='loessShort' (intra-batch non-linear smoothing), inter='concatenate' (inter-batch hierarchical structure), intra_k=5 (short replicate neighbors for intra-batch RUV-III): "Call the hRUV function with parameters: intra='loessShort' (intra-batch non-linear smoothing), inter='concatenate' (inter-batch hierarchical structure), intra_k=5 (short replicate neighbors for"
- [full_text] After applying hruv normalisation with loessShort intra-batch and concatenate inter-batch parameters, the resulting loessShort_concatenate assay eliminates batch effects visible in PCA plots and corrects signal drift across experimental runs.: "After applying hruv normalisation with loessShort intra-batch and concatenate inter-batch parameters, the resulting loessShort_concatenate assay eliminates batch effects visible in PCA plots and"
- [readme] Our novel tool is a novel hierarchical approach to removing unwanted variation by harnessing information from sample replicates embedded in the seequence of experimental runs/batches and applying signal drift correction with robust linear or non-linear smoothers.: "Our novel tool is a novel hierarchical approach to removing unwanted variation by harnessing information from sample replicates embedded in the seequence of experimental runs/batches and applying"
- [full_text] Load the SummarizedExperiment object containing the rawImpute assay and batch/replicate metadata. Call the hRUV function with parameters... Extract the resulting normalised loessShort_concatenate assay from the returned SummarizedExperiment object.: "Load the SummarizedExperiment object containing the rawImpute assay and batch/replicate metadata. Call the hRUV function with parameters... Extract the resulting normalised loessShort_concatenate"