HolobiomicsLab Signal Processing Prominence Filtering
Summary
Filter and rank peaks in 1D mass spectrometry signals by their prominence—a measure of how much a peak stands out relative to surrounding baseline and neighboring peaks. This skill is essential for distinguishing true signal peaks from noise in intensity arrays extracted from unprocessed MS data.
When to use
When you have a 1D intensity array from a mass spectrum (m/z or retention time dimension) and need to identify and rank local maxima that are biochemically meaningful rather than noise-driven. Use this skill when threshold-based filtering alone is insufficient—for example, when working with heterogeneous MS data from orthogonal separations (LC-IM-MS) where signal-to-noise varies across the spectrum.
When NOT to use
- Input intensity array is already filtered or denoised via other methods (e.g., median filtering, wavelet denoising)—applying prominence filtering may over-suppress weak but genuine peaks.
- Peak widths vary so dramatically that a single prominence threshold cannot accommodate both narrow and broad peaks; consider using alternative peak-finding strategies (e.g., find_peaks_1d_gauss) or adaptive prominence windows.
- The 1D signal is severely saturated or clipped; prominence calculation assumes a continuous baseline, which breaks down when many peaks reach maximum detector capacity.
Inputs
- 1D intensity array (numpy array or list of float values)
- Height threshold parameter (minimum intensity, units depend on instrument)
- Prominence parameter (vertical distance units, same scale as intensity)
- Optional: ground-truth peak locations for validation
Outputs
- Peak indices (positions in the 1D array)
- Peak heights (intensity values at detected peaks)
- Prominence values (height above neighboring baseline)
- Optionally: m/z or retention time values corresponding to peak indices
How to apply
Load or generate a 1D intensity array (e.g., from an extracted ion chromatogram or MS1 spectrum stored in MZA HDF5 format). Call scipy.signal.find_peaks on the intensity array, specifying both a height threshold (minimum intensity) and a prominence parameter (the vertical distance a peak must rise above surrounding baseline). Extract the peak indices from the output, along with prominence values computed by scipy. Rank peaks by prominence to prioritize high-confidence detections. Validate the result by comparing detected peak count, positions, and heights against synthetic or known ground-truth peaks to ensure the parameter choices effectively separate signal from noise at your instrument's sensitivity.
Related tools
- scipy.signal.find_peaks (Core function that performs 1D peak detection, computes prominence for each local maximum, and returns peak indices and attributes) — https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
- mzapy.peaks.find_peaks_1d_localmax (Wrapper function in mzapy that applies scipy.signal.find_peaks to mass spectrometry intensity arrays with domain-specific parameter defaults and returns structured output compatible with MZA workflows) — https://github.com/PNNL-m-q/mzapy
- h5py (Loads 1D intensity arrays from MZA HDF5 files (Arrays_intensity group) for prominence-based peak filtering) — https://github.com/h5py/h5py
- numpy (Array manipulation and indexing of peak indices and prominence values during extraction and validation) — https://numpy.org/
Examples
from scipy.signal import find_peaks; import h5py; f = h5py.File('example.mza', 'r'); intensity = f['Arrays_intensity/630'][()]; peaks, props = find_peaks(intensity, height=20, prominence=10); mz = f['Arrays_mz/630'][()]; print(f'Detected {len(peaks)} peaks at m/z: {mz[peaks]}')
Evaluation signals
- Detected peak count matches expected number from ground-truth synthetic peaks or published standards within ±10% tolerance.
- Peak positions (indices or m/z/RT coordinates) align with ground truth within instrument resolution (e.g., ±5 ppm for m/z).
- Prominence values decrease monotonically when peaks are sorted by rank; no negative or zero prominences are returned.
- False-positive rate (noise peaks detected above prominence threshold) remains below domain-specific acceptable level, validated by visual inspection or known-negative control spectra.
- Parameter sensitivity: small perturbations to prominence and height thresholds produce stable peak rankings; dramatic changes in peak count indicate parameters are near a decision boundary and may require refinement.
Limitations
- Prominence calculation assumes a continuous or piecewise-continuous baseline; in spectra with sharp cliffs, saturation, or extreme dynamic range (>1000:1), baseline estimation may fail.
- Single global prominence threshold cannot accommodate peaks with fundamentally different widths; narrow peaks in high-noise regions may be missed while broad peaks in low-noise regions are over-detected.
- The method is sensitive to preprocessing choices (e.g., smoothing kernel size, despiking, baseline subtraction) applied before prominence filtering; different preprocessing pipelines on the same raw data may yield different results.
- In multidimensional MS workflows (LC-IM-MS) with heterogeneous retention time or arrival time distributions, prominence filtering applied independently to each 1D slice may miss weak features that become visible only after 2D or 3D integration.
Evidence
- [other] mzapy.peaks.find_peaks_1d_localmax is one of two functions provided for performing peak fitting on 1-dimensional data.: "mzapy.peaks.find_peaks_1d_localmax is one of two functions provided for performing peak fitting on 1-dimensional data."
- [other] Call scipy.signal.find_peaks on the intensity array with appropriate threshold and prominence parameters to identify local maxima.: "Call scipy.signal.find_peaks on the intensity array with appropriate threshold and prominence parameters to identify local maxima."
- [other] Extract peak indices, heights, and prominence values from the scipy output.: "Extract peak indices, heights, and prominence values from the scipy output."
- [other] Validate output against known synthetic peaks by comparing detected peak count and positions to ground truth.: "Validate output against known synthetic peaks by comparing detected peak count and positions to ground truth."
- [intro] A Python package that provides an interface to unprocessed MS data in the MZA format.: "A Python package that provides an interface to unprocessed MS data in the MZA format."
- [readme] scipy.signal provides peak detection routines (find_peaks) with prominence-based filtering.: "Spectra are stored omitting zero-intensity values and as two jagged arrays, one in each corresponding group"