composite-mass-track-assembly-and-peak-detection
Summary
Assemble aligned mass tracks from multiple LC-MS samples into a composite mass track, then detect elution peaks on this composite map using statistics-guided local maxima detection. This approach prioritizes mass resolution and selectivity, avoiding repeated peak detection on individual samples and improving reproducibility across the cohort.
When to use
After mass tracks have been aligned across all samples into a MassGrid (via sample-wise or centroid-based alignment), you have a unified set of m/z features tracked across the entire study. Use this skill when you need to identify reproducible, high-confidence peaks across the cohort by detecting peaks on the aggregate signal rather than individually per sample, especially for studies with ≤10 samples using anchor-first alignment or larger studies using centroid-based alignment.
When NOT to use
- Input mass tracks have not yet been aligned across samples into a MassGrid — run mass alignment first.
- RT calibration has not been performed — composite assembly requires rt_cal_dict; apply chromatograms.rt_lowess_calibration before this step.
- You need to detect peaks on individual samples independently (e.g., single-sample metabolomics, where cohort-level assembly is not applicable).
Inputs
- aligned_MassGrid (object containing per-sample mass tracks aligned across cohort)
- per-sample_rt_calibration_dictionaries (rt_cal_dict keyed by sample ID)
- intensity_alignment_matrix (composite signal per m/z feature × sample)
Outputs
- CompositeMap.FeatureTable (peak detections mapped to samples with areas/intensities)
- cmap.pickle (serialized composite map object for traceability)
- full_Feature_table.tsv (all detected features meeting SNR >2 and peakshape >0.5)
How to apply
Construct composite mass tracks by summing aligned intensity values across all samples after retention time (RT) calibration has been applied. Feed each composite mass track to peaks.stats_detect_elution_peaks, which uses scipy.signal.find_peaks (local maxima method) with adaptive prominence filtering (minimum 1/3 of min_peak_height, default 1e5 intensity units) and optional detrending or smoothing. Evaluate detected peaks against peakshape >0.5 (gaussian fit quality) and signal-to-noise ratio (SNR) >2 using peaks.evaluate_gaussian_peak_on_intensity_list. This statistic-guided approach leverages the composite signal to reduce noise while preserving selectivity. Map detected features back to individual samples via RT alignment dictionaries (rt_cal_dict per sample) to extract per-sample peak areas and intensities, recording results in CompositeMap.FeatureTable.
Related tools
- scipy.signal.find_peaks (Local maxima detection with prominence control on composite mass track intensity)
- scipy.signal.detrend (Optional smoothing/detrending of mass track before peak detection)
- asari.peaks.stats_detect_elution_peaks (Primary peak detection function that wraps find_peaks with adaptive prominence and noise filtering) — https://github.com/shuzhao-li/asari
- asari.peaks.evaluate_gaussian_peak_on_intensity_list (Quality filtering of detected peaks by gaussian peakshape and SNR metrics) — https://github.com/shuzhao-li/asari
- asari.chromatograms.rt_lowess_calibration (Pre-requisite RT calibration per sample to enable accurate peak mapping back to individual samples) — https://github.com/shuzhao-li/asari
- asari.MassGrid (Data structure holding aligned mass tracks across samples; input to composite assembly) — https://github.com/shuzhao-li/asari
Examples
python3 -m asari.main process -i mydir/projectx_dir --mode pos
Evaluation signals
- All detected peaks satisfy peakshape >0.5 (gaussian fit quality) and SNR >2 as recorded in CompositeMap.FeatureTable.
- Peak positions (m/z, RT) are consistent across samples where the feature is present; verify by inspecting full_Feature_table.tsv for features with >1 sample.
- cmap.pickle object is successfully serialized and can be deserialized without errors, confirming internal data structure integrity.
- Composite peak intensity values sum approximately to the sum of per-sample intensities (accounting for RT alignment shifts), validating that alignment and summing was correct.
- Feature count in preferred_Feature_table.tsv (filtered for selectivity) is lower than full_Feature_table.tsv, showing that QC filtering was applied.
Limitations
- Peak detection relies on local maxima and prominence control; overlapping or severely tailed peaks may not resolve into separate features.
- Composite assembly assumes that RT calibration landmarks (mSelectivity >0.99, prominence >20% of peak height) exist in most samples; sparse landmark peaks will degrade calibration accuracy.
- Adaptive prominence threshold (default 1/3 of min_peak_height = 1e5) may suppress weak but reproducible features in low-abundance studies; user adjustment is necessary.
- Mass resolution advantages (0.001 amu binning, 5 ppm tolerance) assume high-resolution MS data; centroid data with coarser m/z spacing will reduce mass separation gains.
Evidence
- [methods] Build composite mass tracks by summing aligned intensity values across all samples after RT calibration. Feed each composite mass track to peaks.stats_detect_elution_peaks, which uses scipy.signal.find_peaks (local maxima method) with adaptive prominence filtering (minimum 1/3 of min_peak_height, default 1e5).: "Build composite mass tracks by summing aligned intensity values across all samples after RT calibration. Detect elution peaks on composite mass tracks using peaks.stats_detect_elution_peaks with"
- [intro] Peak detection on a composite map instead of repeated on individual samples.: "Peak detection on a composite map instead of repeated on individual samples"
- [methods] Evaluate peaks with peaks.evaluate_gaussian_peak_on_intensity_list for peakshape >0.5 and SNR >2.: "evaluate peaks with peaks.evaluate_gaussian_peak_on_intensity_list for peakshape >0.5 and SNR >2"
- [methods] Map detected features back to individual samples via RT alignment dictionaries to extract peak areas and intensities, recording results in CompositeMap.FeatureTable.: "Map detected features back to individual samples via RT alignment dictionaries to extract peak areas and intensities, recording results in CompositeMap.FeatureTable"
- [intro] Statistics guided peak detection based on local maxima and prominence with selective use of smoothing.: "Statistics guided peak dection, based on local maxima and prominence, selective use of smoothing"
- [intro] Reproducible tracking and backtracking between features and mass tracks (EICs).: "Reproducible, track and backtrack between features and mass tracks (EICs)"
1---2name: composite-mass-track-assembly-and-peak-detection3description: Use when after mass tracks have been aligned across all samples into a MassGrid (via sample-wise or centroid-based alignment), you have a unified set of m/z features tracked across the entire study.4license: CC-BY-4.05---67# composite-mass-track-assembly-and-peak-detection89## Summary1011Assemble aligned mass tracks from multiple LC-MS samples into a composite mass track, then detect elution peaks on this composite map using statistics-guided local maxima detection. This approach prioritizes mass resolution and selectivity, avoiding repeated peak detection on individual samples and improving reproducibility across the cohort.1213## When to use1415After mass tracks have been aligned across all samples into a MassGrid (via sample-wise or centroid-based alignment), you have a unified set of m/z features tracked across the entire study. Use this skill when you need to identify reproducible, high-confidence peaks across the cohort by detecting peaks on the aggregate signal rather than individually per sample, especially for studies with ≤10 samples using anchor-first alignment or larger studies using centroid-based alignment.1617## When NOT to use1819- Input mass tracks have not yet been aligned across samples into a MassGrid — run mass alignment first.20- RT calibration has not been performed — composite assembly requires rt_cal_dict; apply chromatograms.rt_lowess_calibration before this step.21- You need to detect peaks on individual samples independently (e.g., single-sample metabolomics, where cohort-level assembly is not applicable).2223## Inputs2425- aligned_MassGrid (object containing per-sample mass tracks aligned across cohort)26- per-sample_rt_calibration_dictionaries (rt_cal_dict keyed by sample ID)27- intensity_alignment_matrix (composite signal per m/z feature × sample)2829## Outputs3031- CompositeMap.FeatureTable (peak detections mapped to samples with areas/intensities)32- cmap.pickle (serialized composite map object for traceability)33- full_Feature_table.tsv (all detected features meeting SNR >2 and peakshape >0.5)3435## How to apply3637Construct composite mass tracks by summing aligned intensity values across all samples after retention time (RT) calibration has been applied. Feed each composite mass track to peaks.stats_detect_elution_peaks, which uses scipy.signal.find_peaks (local maxima method) with adaptive prominence filtering (minimum 1/3 of min_peak_height, default 1e5 intensity units) and optional detrending or smoothing. Evaluate detected peaks against peakshape >0.5 (gaussian fit quality) and signal-to-noise ratio (SNR) >2 using peaks.evaluate_gaussian_peak_on_intensity_list. This statistic-guided approach leverages the composite signal to reduce noise while preserving selectivity. Map detected features back to individual samples via RT alignment dictionaries (rt_cal_dict per sample) to extract per-sample peak areas and intensities, recording results in CompositeMap.FeatureTable.3839## Related tools4041- **scipy.signal.find_peaks** (Local maxima detection with prominence control on composite mass track intensity)42- **scipy.signal.detrend** (Optional smoothing/detrending of mass track before peak detection)43- **asari.peaks.stats_detect_elution_peaks** (Primary peak detection function that wraps find_peaks with adaptive prominence and noise filtering) — https://github.com/shuzhao-li/asari44- **asari.peaks.evaluate_gaussian_peak_on_intensity_list** (Quality filtering of detected peaks by gaussian peakshape and SNR metrics) — https://github.com/shuzhao-li/asari45- **asari.chromatograms.rt_lowess_calibration** (Pre-requisite RT calibration per sample to enable accurate peak mapping back to individual samples) — https://github.com/shuzhao-li/asari46- **asari.MassGrid** (Data structure holding aligned mass tracks across samples; input to composite assembly) — https://github.com/shuzhao-li/asari4748## Examples4950```51python3 -m asari.main process -i mydir/projectx_dir --mode pos52```5354## Evaluation signals5556- All detected peaks satisfy peakshape >0.5 (gaussian fit quality) and SNR >2 as recorded in CompositeMap.FeatureTable.57- Peak positions (m/z, RT) are consistent across samples where the feature is present; verify by inspecting full_Feature_table.tsv for features with >1 sample.58- cmap.pickle object is successfully serialized and can be deserialized without errors, confirming internal data structure integrity.59- Composite peak intensity values sum approximately to the sum of per-sample intensities (accounting for RT alignment shifts), validating that alignment and summing was correct.60- Feature count in preferred_Feature_table.tsv (filtered for selectivity) is lower than full_Feature_table.tsv, showing that QC filtering was applied.6162## Limitations6364- Peak detection relies on local maxima and prominence control; overlapping or severely tailed peaks may not resolve into separate features.65- Composite assembly assumes that RT calibration landmarks (mSelectivity >0.99, prominence >20% of peak height) exist in most samples; sparse landmark peaks will degrade calibration accuracy.66- Adaptive prominence threshold (default 1/3 of min_peak_height = 1e5) may suppress weak but reproducible features in low-abundance studies; user adjustment is necessary.67- Mass resolution advantages (0.001 amu binning, 5 ppm tolerance) assume high-resolution MS data; centroid data with coarser m/z spacing will reduce mass separation gains.6869## Evidence7071- [methods] Build composite mass tracks by summing aligned intensity values across all samples after RT calibration. Feed each composite mass track to peaks.stats_detect_elution_peaks, which uses scipy.signal.find_peaks (local maxima method) with adaptive prominence filtering (minimum 1/3 of min_peak_height, default 1e5).: "Build composite mass tracks by summing aligned intensity values across all samples after RT calibration. Detect elution peaks on composite mass tracks using peaks.stats_detect_elution_peaks with"72- [intro] Peak detection on a composite map instead of repeated on individual samples.: "Peak detection on a composite map instead of repeated on individual samples"73- [methods] Evaluate peaks with peaks.evaluate_gaussian_peak_on_intensity_list for peakshape >0.5 and SNR >2.: "evaluate peaks with peaks.evaluate_gaussian_peak_on_intensity_list for peakshape >0.5 and SNR >2"74- [methods] Map detected features back to individual samples via RT alignment dictionaries to extract peak areas and intensities, recording results in CompositeMap.FeatureTable.: "Map detected features back to individual samples via RT alignment dictionaries to extract peak areas and intensities, recording results in CompositeMap.FeatureTable"75- [intro] Statistics guided peak detection based on local maxima and prominence with selective use of smoothing.: "Statistics guided peak dection, based on local maxima and prominence, selective use of smoothing"76- [intro] Reproducible tracking and backtracking between features and mass tracks (EICs).: "Reproducible, track and backtrack between features and mass tracks (EICs)"
HolobiomicsLab