peak-data-extraction-and-formatting
Summary
Extract m/z and intensity peak values from mass spectrometry backend objects and format them as NumericList or matrix structures for downstream analysis. This skill bridges MsBackend data storage and Spectra-based processing by implementing standardized accessor methods that present raw peak data in expected formats.
When to use
When you have initialized an MsBackend subclass (e.g., MsBackendTest, MsBackendMemory) containing spectral peak data stored as NumericList or matrix objects and need to retrieve, subset, or reformat m/z and intensity values for processing, visualization, or comparison within a Spectra workflow.
When NOT to use
- Input backend is read-only (e.g., MsBackendMzR, MsBackendCached) and you need to modify peak values — use intensity<- and mz<- replacement methods instead, which are only implemented for writable backends.
- You need only summary statistics (e.g., precursor m/z, total intensity) rather than full peak arrays — use spectraData() or dedicated accessor methods instead.
- Peak data is already in expected format (NumericList or matrix) and you are not extracting from raw storage — skip this skill and work directly with the formatted peaks.
Inputs
- MsBackend subclass instance (e.g., MsBackendTest) with populated intensity and mz slots
- spectrum indices or boolean mask for subsetting (optional)
- spectra variable names to filter by (optional)
Outputs
- NumericList: peak m/z values, one element per spectrum
- NumericList: peak intensity values, one element per spectrum
- list of matrices: combined m/z and intensity as [mz, intensity] rows per spectrum (from peaksData())
- DataFrame: extracted peaks data integrated with spectrum metadata (from peaksData())
How to apply
Implement the peaksData() method to extract m/z and intensity values from the backend's internal storage (NumericList slots or database) and return them as a list of matrices with one row per spectrum; implement the intensity() and mz() accessor methods to return NumericList objects preserving the one-spectrum-per-element structure; ensure m/z values are sorted increasingly within each spectrum as required by the API. Use extractByIndex() or [ subsetting to isolate specific spectra before peak extraction when processing subsets. Validate that output dimensionality matches the input spectrum count and that all m/z values are numeric and sorted.
Related tools
- Spectra (Container package defining MsBackend virtual class and Spectra object that uses backend instances via @backend slot to provide peak data access) — https://github.com/RforMassSpectrometry/Spectra
- S4Vectors (Provides NumericList and DataFrame data structures for storing and returning peak m/z and intensity values with list-like semantics)
- IRanges (Imported by Spectra; provides underlying infrastructure for efficient range and list operations on peak data)
- MsBackendMemory (Reference in-memory backend implementation that stores peaks as list of matrices; serves as model for implementing peaksData(), intensity(), and mz() methods) — https://github.com/RforMassSpectrometry/Spectra
- MsBackendMzR (Example on-the-fly backend that retrieves peaks from raw MS files; demonstrates lazy loading pattern in peaksData() without storing full NumericList in slots) — https://github.com/RforMassSpectrometry/Spectra
Evaluation signals
- peaksData() returns a list with length equal to the number of spectra in the backend, with each element being a 2-row matrix (mz, intensity) or named list containing 'mz' and 'intensity' columns.
- intensity() and mz() each return a NumericList object with length equal to spectrum count, where each element is a numeric vector corresponding to one spectrum's peaks; all NumericList elements can be accessed via integer or logical indexing.
- All m/z values are numeric, finite, and increasingly sorted within each spectrum (checksum: min(diff(mz)) >= 0 for each spectrum).
- Subsetting via extractByIndex() or [ on the backend followed by peak extraction returns correctly reduced peak arrays matching the selected spectrum indices.
- Round-trip test: extract peaks, verify dimensions match input spectra count, verify no NAs or infinities in output, and verify spectra variable count is consistent before and after subsetting.
Limitations
- Peak extraction assumes m/z and intensity data are already sorted and validated at backend initialization; no automatic sorting or duplicate-removal is performed by these accessor methods.
- For very large datasets or on-disk backends (e.g., MsBackendMzR), calling peaksData() on the entire backend may trigger full file I/O and consume significant memory; use extractByIndex() to process spectra in batches.
- Read-only backends (e.g., MsBackendMzR, MsBackendCached, MsBackendMassbankSql) do not implement intensity<-, mz<-, or peaksData<- replacement methods; peak modification requires copying data to a writable backend like MsBackendMemory.
- The API expects core variables 'dataStorage' and 'dataOrigin' to be present in every backend; peaks extraction will fail if these required variables are missing from spectraVars.
Evidence
- [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] Extract the intensity values for each spectrum in the backend. The result is expected to be a
NumericList: "Extract the intensity values for each spectrum in the backend. The result is expected to be a NumericList"
- [intro] m/z values within each spectrum are expected to be sorted increasingly.: "m/z values within each spectrum are expected to be sorted increasingly."
- [intro] Each
Spectra object contains an implementation of such a MsBackend within its @backend slot which provides the MS data to the Spectra object.: "Each Spectra object contains an implementation of such a MsBackend within its @backend slot which provides the MS data to the Spectra object."
- [intro] slots = c(
spectraVars = "data.frame",
mz = "NumericList",
intensity = "NumericList"
): "slots = c(
spectraVars = "data.frame",
mz = "NumericList",
intensity = "NumericList"
)"
1---2name: peak-data-extraction-and-formatting3description: Use when when you have initialized an MsBackend subclass (e.4license: CC-BY-4.05---67# peak-data-extraction-and-formatting89## Summary1011Extract m/z and intensity peak values from mass spectrometry backend objects and format them as NumericList or matrix structures for downstream analysis. This skill bridges MsBackend data storage and Spectra-based processing by implementing standardized accessor methods that present raw peak data in expected formats.1213## When to use1415When you have initialized an MsBackend subclass (e.g., MsBackendTest, MsBackendMemory) containing spectral peak data stored as NumericList or matrix objects and need to retrieve, subset, or reformat m/z and intensity values for processing, visualization, or comparison within a Spectra workflow.1617## When NOT to use1819- Input backend is read-only (e.g., MsBackendMzR, MsBackendCached) and you need to modify peak values — use intensity<- and mz<- replacement methods instead, which are only implemented for writable backends.20- You need only summary statistics (e.g., precursor m/z, total intensity) rather than full peak arrays — use spectraData() or dedicated accessor methods instead.21- Peak data is already in expected format (NumericList or matrix) and you are not extracting from raw storage — skip this skill and work directly with the formatted peaks.2223## Inputs2425- MsBackend subclass instance (e.g., MsBackendTest) with populated intensity and mz slots26- spectrum indices or boolean mask for subsetting (optional)27- spectra variable names to filter by (optional)2829## Outputs3031- NumericList: peak m/z values, one element per spectrum32- NumericList: peak intensity values, one element per spectrum33- list of matrices: combined m/z and intensity as [mz, intensity] rows per spectrum (from peaksData())34- DataFrame: extracted peaks data integrated with spectrum metadata (from peaksData())3536## How to apply3738Implement the peaksData() method to extract m/z and intensity values from the backend's internal storage (NumericList slots or database) and return them as a list of matrices with one row per spectrum; implement the intensity() and mz() accessor methods to return NumericList objects preserving the one-spectrum-per-element structure; ensure m/z values are sorted increasingly within each spectrum as required by the API. Use extractByIndex() or [ subsetting to isolate specific spectra before peak extraction when processing subsets. Validate that output dimensionality matches the input spectrum count and that all m/z values are numeric and sorted.3940## Related tools4142- **Spectra** (Container package defining MsBackend virtual class and Spectra object that uses backend instances via @backend slot to provide peak data access) — https://github.com/RforMassSpectrometry/Spectra43- **S4Vectors** (Provides NumericList and DataFrame data structures for storing and returning peak m/z and intensity values with list-like semantics)44- **IRanges** (Imported by Spectra; provides underlying infrastructure for efficient range and list operations on peak data)45- **MsBackendMemory** (Reference in-memory backend implementation that stores peaks as list of matrices; serves as model for implementing peaksData(), intensity(), and mz() methods) — https://github.com/RforMassSpectrometry/Spectra46- **MsBackendMzR** (Example on-the-fly backend that retrieves peaks from raw MS files; demonstrates lazy loading pattern in peaksData() without storing full NumericList in slots) — https://github.com/RforMassSpectrometry/Spectra4748## Evaluation signals4950- peaksData() returns a list with length equal to the number of spectra in the backend, with each element being a 2-row matrix (mz, intensity) or named list containing 'mz' and 'intensity' columns.51- intensity() and mz() each return a NumericList object with length equal to spectrum count, where each element is a numeric vector corresponding to one spectrum's peaks; all NumericList elements can be accessed via integer or logical indexing.52- All m/z values are numeric, finite, and increasingly sorted within each spectrum (checksum: min(diff(mz)) >= 0 for each spectrum).53- Subsetting via extractByIndex() or [ on the backend followed by peak extraction returns correctly reduced peak arrays matching the selected spectrum indices.54- Round-trip test: extract peaks, verify dimensions match input spectra count, verify no NAs or infinities in output, and verify spectra variable count is consistent before and after subsetting.5556## Limitations5758- Peak extraction assumes m/z and intensity data are already sorted and validated at backend initialization; no automatic sorting or duplicate-removal is performed by these accessor methods.59- For very large datasets or on-disk backends (e.g., MsBackendMzR), calling peaksData() on the entire backend may trigger full file I/O and consume significant memory; use extractByIndex() to process spectra in batches.60- Read-only backends (e.g., MsBackendMzR, MsBackendCached, MsBackendMassbankSql) do not implement intensity<-, mz<-, or peaksData<- replacement methods; peak modification requires copying data to a writable backend like MsBackendMemory.61- The API expects core variables 'dataStorage' and 'dataOrigin' to be present in every backend; peaks extraction will fail if these required variables are missing from spectraVars.6263## Evidence6465- [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."66- [intro] Extract the intensity values for each spectrum in the backend. The result is expected to be a `NumericList`: "Extract the intensity values for each spectrum in the backend. The result is expected to be a `NumericList`"67- [intro] m/z values within each spectrum are expected to be sorted increasingly.: "m/z values within each spectrum are expected to be sorted increasingly."68- [intro] Each `Spectra` object contains an implementation of such a `MsBackend` within its `@backend` slot which provides the MS data to the `Spectra` object.: "Each `Spectra` object contains an implementation of such a `MsBackend` within its `@backend` slot which provides the MS data to the `Spectra` object."69- [intro] slots = c(70 spectraVars = "data.frame",71 mz = "NumericList",72 intensity = "NumericList"73 ): "slots = c(74 spectraVars = "data.frame",75 mz = "NumericList",76 intensity = "NumericList"77 )"
HolobiomicsLab 