Chunk Wise Data Processing Optimization

---
name: chunk-wise-data-processing-optimization
description: Use when you have a large mass spectrometry dataset stored across multiple
  mzML, mzXML, or CDF files and need to perform operations (e.g., normalization, filtering,
  feature extraction) on the full dataset but memory constraints prevent loading all
  peak data simultaneously.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3891
  edam_topics:
  - http://edamontology.org/topic_3520
  - http://edamontology.org/topic_0091
  tools:
  - MsBackendMzR
  - R
  - S4Vectors
  - Spectra
  - mzR
  techniques:
  - LC-MS
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.3390/metabo12020173
  title: spectra
evidence_spans:
- Backends such as the `MsBackendMzR` for example retrieve the data on the fly from
  the raw MS data files
- library(Spectra) library(IRanges)
- library(Spectra)
- return the **full** spectra data within a backend as a `DataFrame` object (defined
  in the `r Biocpkg("S4Vectors")`
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_spectra
    doi: 10.3390/metabo12020173
    title: spectra
  dedup_kept_from: coll_spectra
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.3390/metabo12020173
  all_source_dois:
  - 10.3390/metabo12020173
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# chunk-wise-data-processing-optimization

## Summary

Split large MS datasets into memory-efficient chunks grouped by data source files, then process each chunk independently to reduce peak-data memory footprint while maintaining parallel processing capability. This skill applies the backendParallelFactor() method to enable lazy loading of m/z and intensity values on-the-fly rather than realizing all spectra into memory at once.

## When to use

You have a large mass spectrometry dataset stored across multiple mzML, mzXML, or CDF files and need to perform operations (e.g., normalization, filtering, feature extraction) on the full dataset but memory constraints prevent loading all peak data simultaneously. Chunk-wise processing is indicated when your analysis workflow can tolerate sequential or batch processing of spectra groups without requiring global access to all peaks at once.

## When NOT to use

- Your workflow requires simultaneous access to peak data from all spectra (e.g., global normalization across the entire dataset without reformulation for chunk-local processing)
- The dataset is already small enough to fit entirely in memory; chunk-wise processing adds overhead with minimal benefit
- You are using an in-memory backend such as MsBackendMemory or MsBackendDataFrame where all peak data is already loaded

## Inputs

- MsBackendMzR object initialized with mzML, mzXML, or CDF files
- Spectra object with @backend slot containing file path metadata (dataStorage variable)
- Analysis function or workflow to apply to peak data

## Outputs

- Processed Spectra object with reduced memory footprint
- Memory profile comparison report (whole-load vs. chunk-wise)
- Chunk grouping factor (Factor object mapping spectra to source files)
- List of peak data matrices (m/z and intensity pairs) for each chunk

## How to apply

First, implement backendInitialize() to load MS data via MsBackendMzR and populate the dataStorage metadata variable with source file paths for each spectrum. Next, implement backendParallelFactor() to extract unique file paths from dataStorage and return a factor grouping spectra by their source file—this factor defines chunk boundaries. Implement peaksData() to extract m/z and intensity values on-the-fly as a list of matrices, supporting subsetting by index so only the current chunk's peak data is realized in memory. During processing, iterate over chunks grouped by the parallel factor, apply your analysis function to each chunk, and collect results. Finally, validate memory reduction by profiling peak-data memory usage under whole-load versus chunk-wise approaches and document the percentage reduction.

## Related tools

- **Spectra** (Provides the S4 container class and virtual MsBackend API for defining chunk-aware data backends) — https://github.com/RforMassSpectrometry/Spectra
- **MsBackendMzR** (On-disk backend that retrieves peak data on-the-fly from raw MS files and implements backendParallelFactor() to enable file-based chunking) — https://github.com/RforMassSpectrometry/Spectra
- **S4Vectors** (Provides DataFrame and NumericList classes for storing spectra variables and peak data in the backend)
- **mzR** (Underlying package for reading mzML, mzXML, and CDF files in MsBackendMzR)

## Examples

```
bpf <- backendParallelFactor(spectra@backend); chunks <- split(seq_along(spectra), bpf); for (i in seq_along(chunks)) { chunk_peaks <- peaksData(spectra@backend[chunks[[i]]]); result[[i]] <- processChunk(chunk_peaks) }
```

## Evaluation signals

- backendParallelFactor() returns a factor with length equal to the number of spectra, with levels corresponding to unique source file paths
- peaksData() called on a chunk subset returns only the peak data for that chunk's spectra, not the entire dataset
- Memory usage profiling shows measurable reduction (e.g., >30%) when processing chunks compared to loading all spectra at once
- Processing results (e.g., feature tables, normalized intensities) are identical whether computed chunk-wise or in whole-load mode
- dataStorage variable correctly populated with file paths and matches the grouping structure defined by the parallel factor

## Limitations

- Chunk-wise processing assumes independence of operations across file boundaries; global statistics or cross-file normalization require reformulation or additional aggregation steps
- Performance gains depend on efficient file I/O and disk caching; benefit diminishes if the same peaks are re-accessed across multiple chunks
- The parallel factor is tied to source file identity; datasets with many small files may yield many fine-grained chunks with higher overhead, while datasets with few large files yield fewer, larger chunks with less memory benefit

## Evidence

- [other] MsBackendMzR's backendParallelFactor() returns a factor based on dataStorage file names to split the backend for parallel or serial processing.: "MsBackendMzR's backendParallelFactor() returns a factor based on dataStorage file names to split the backend for parallel or serial processing"
- [other] Chunk-wise processing via this splitting reduces memory demand because only the peak data of the current chunk—not all spectra—needs to be realized in memory during operations.: "Chunk-wise processing via this splitting reduces memory demand because only the peak data of the current chunk—not all spectra—needs to be realized in memory during operations"
- [readme] Backends such as the MsBackendMzR for example retrieve the data on the fly from the raw MS data files: "Backends such as the `MsBackendMzR` for example retrieve the data on the fly from the raw MS data files"
- [intro] The peaksData() method extracts the MS peaks data from a backend, which includes the m/z and intensity values of each MS peak of a spectrum.: "The `peaksData()` method extracts the MS peaks data from a backend, which includes the m/z and intensity values of each MS peak of a spectrum"
- [intro] dataStorage and dataOrigin are two special spectra variables that define for each spectrum where the data is stored and from where the data derived: "`dataStorage` and `dataOrigin` are two special spectra variables that define for each spectrum where the data is stored and from where the data derived"