Spectra Object Manipulation R

Spectra Object Manipulation in R

Summary

Load, filter, and manipulate tandem MS/MS spectra as S4 Spectra objects in R, applying intensity thresholds and mass tolerance constraints to prepare concatenated replicate spectra for denoising and analysis.

When to use

When you have extracted and concatenated MS/MS spectra from multiple replicates for a set of metabolomic features (stored in a preprocessed list), and need to apply intensity-based filtering (e.g., top x% TIC selection) or prepare spectra objects for downstream consensus generation, frequency labeling, or spectral matching workflows.

When NOT to use

• Input is already a consensus spectrum or aggregated frequency table; Spectra object manipulation is for raw MS/MS data, not summary statistics.
• You have only a single replicate per feature; the skill is designed for replicate concatenation and filtering, not singleton spectra.
• Your data are in formats other than mzML (e.g., raw Thermo .raw files without prior conversion); the dures package requires mzML input.

Inputs

• Preprocessed spectra list (l1) from preprocess() containing concatenated MS/MS replicates
• Folder path to mzML files
• Mass tolerance parameter (Da)
• Top TIC threshold parameter (0–1, e.g., 0.8)

Outputs

• Spectra object (S4 class) containing intensity-filtered spectra
• Data frame with before/after spectra counts per feature

How to apply

Load the preprocessed spectra list (l1) output from the preprocess() step using the dures package. Call extract_raw_spectra() with parameters: folder_path, the l1 list, mass tolerance (e.g., 0.05 Da for intra-spectrum grouping), and a top TIC threshold (e.g., 0.8 for top 80% intensity cutoff). The function returns a Spectra object (sps_top_tic_2) containing filtered spectra and a dataframe reporting spectra counts before and after filtering for each feature. Verify the reduction by checking that features meet expected thresholds (e.g., feature 1982: 83→66 spectra; feature 872: 43→34 spectra). Use the filtered Spectra object as input to downstream steps such as call_aggregate() for consensus spectrum generation.

Related tools

• Spectra (S4 class for storing and manipulating MS/MS spectra objects; core data structure for filtering and accessing spectrum metadata and intensity data)
• dures (Provides extract_raw_spectra() function that wraps Spectra object creation, applies top x% TIC filtering, groups fragments within mass tolerance, and returns filtered Spectra object and summary statistics) — https://github.com/BiosystemEngineeringLab-IITB/dures
• S4Vectors (Bioconductor package providing S4 class infrastructure used by Spectra for object-oriented spectrum data storage)
• dplyr (Used for filtering and summarizing spectra count dataframes by feature)

Examples

l2 = extract_raw_spectra(folder_path = "~/metabolomics/test_1/", l1, 0.05, 0.8)

Evaluation signals

• Verify that the returned Spectra object has fewer spectra per feature than the input l1 list, consistent with the specified top TIC cutoff (e.g., 80% intensity retention)
• Check that before/after spectra count dataframe matches expected reductions for known features (e.g., feature 1982: 83→66; feature 872: 43→34)
• Confirm that mass tolerance grouping reduced fragment counts within each spectrum (e.g., 98→81 fragments after 0.05 Da grouping)
• Validate that no spectra are returned for features with zero MS/MS data or those below the TIC threshold
• Ensure Spectra object is S4-compliant and compatible with downstream dures functions (call_aggregate, label_individual_spectrum)

Limitations

• Top TIC cutoff may be too aggressive on features with few high-intensity fragments, removing informative signal; tuning via Pareto front analysis or Wilcoxon rank-sum tests is recommended.
• Mass tolerance parameter (e.g., 0.05 Da) assumes accurate mass calibration; miscalibrated instruments may cause over-grouping or under-grouping of fragments.
• Spectra object manipulation does not denoise; it only filters by intensity and groups fragments. Actual denoising (removal of noise fragments based on recurrence frequency) occurs in downstream steps (generate_denoised_spectra).
• Requires input in mzML format; conversion from other formats (e.g., .raw) must be completed beforehand using external tools (e.g., MSConvert).

Evidence

• [other] Call extract_raw_spectra() with parameters: folder_path, l1, mass tolerance 0.05 Da, and top TIC threshold 0.8 (80%). Extract the output Spectra object (sps_top_tic_2) and dataframe (df) showing before/after spectra counts per feature.: "Call extract_raw_spectra() with parameters: folder_path, l1, mass tolerance 0.05 Da, and top TIC threshold 0.8 (80%). Extract the output Spectra object (sps_top_tic_2) and dataframe (df) showing"
• [methods] The extracted spectra are then concatenated for all replicate spectra belonging to a given feature: "The extracted spectra are then concatenated for all replicate spectra belonging to a given feature"
• [methods] Extract the top x% TIC spectra, and Group fragments within a specified mass tolerance: "Extract the top x% TIC spectra, and Group fragments within a specified mass tolerance"
• [readme] l2 = extract_raw_spectra(folder_path = folder_path, l1_subset, 0.05, 0.8): "l2 = extract_raw_spectra(folder_path = folder_path, l1_subset, 0.05, 0.8)"
• [methods] Top 80% TIC cutoff reduced 83 spectra to 66 spectra for feature 1982: "Top 80% TIC cutoff reduced 83 spectra to 66 spectra for feature 1982"
• [methods] After grouping, the number of fragments reduced to 81: "After grouping, the number of fragments reduced to 81"