Initial commit

skills/pyopenms/references/signal_processing.md

@@ -0,0 +1,433 @@
+# Signal Processing
+
+## Overview
+
+PyOpenMS provides algorithms for processing raw mass spectrometry data including smoothing, filtering, peak picking, centroiding, normalization, and deconvolution.
+
+## Algorithm Pattern
+
+Most signal processing algorithms follow a standard pattern:
+
+```python
+import pyopenms as ms
+
+# 1. Create algorithm instance
+algo = ms.AlgorithmName()
+
+# 2. Get and modify parameters
+params = algo.getParameters()
+params.setValue("parameter_name", value)
+algo.setParameters(params)
+
+# 3. Apply to data
+algo.filterExperiment(exp)  # or filterSpectrum(spec)
+```
+
+## Smoothing
+
+### Gaussian Filter
+
+Apply Gaussian smoothing to reduce noise:
+
+```python
+# Create Gaussian filter
+gaussian = ms.GaussFilter()
+
+# Configure parameters
+params = gaussian.getParameters()
+params.setValue("gaussian_width", 0.2)  # Width in m/z or RT units
+params.setValue("ppm_tolerance", 10.0)  # For m/z dimension
+params.setValue("use_ppm_tolerance", "true")
+gaussian.setParameters(params)
+
+# Apply to experiment
+gaussian.filterExperiment(exp)
+
+# Or apply to single spectrum
+spec = exp.getSpectrum(0)
+gaussian.filterSpectrum(spec)
+```
+
+### Savitzky-Golay Filter
+
+Polynomial smoothing that preserves peak shapes:
+
+```python
+# Create Savitzky-Golay filter
+sg_filter = ms.SavitzkyGolayFilter()
+
+# Configure parameters
+params = sg_filter.getParameters()
+params.setValue("frame_length", 11)  # Window size (must be odd)
+params.setValue("polynomial_order", 4)  # Polynomial degree
+sg_filter.setParameters(params)
+
+# Apply smoothing
+sg_filter.filterExperiment(exp)
+```
+
+## Peak Picking and Centroiding
+
+### Peak Picker High Resolution
+
+Detect peaks in high-resolution data:
+
+```python
+# Create peak picker
+peak_picker = ms.PeakPickerHiRes()
+
+# Configure parameters
+params = peak_picker.getParameters()
+params.setValue("signal_to_noise", 3.0)  # S/N threshold
+params.setValue("spacing_difference", 1.5)  # Minimum peak spacing
+peak_picker.setParameters(params)
+
+# Pick peaks
+exp_picked = ms.MSExperiment()
+peak_picker.pickExperiment(exp, exp_picked)
+```
+
+### Peak Picker for CWT
+
+Continuous wavelet transform-based peak picking:
+
+```python
+# Create CWT peak picker
+cwt_picker = ms.PeakPickerCWT()
+
+# Configure parameters
+params = cwt_picker.getParameters()
+params.setValue("signal_to_noise", 1.0)
+params.setValue("peak_width", 0.15)  # Expected peak width
+cwt_picker.setParameters(params)
+
+# Pick peaks
+cwt_picker.pickExperiment(exp, exp_picked)
+```
+
+## Normalization
+
+### Normalizer
+
+Normalize peak intensities within spectra:
+
+```python
+# Create normalizer
+normalizer = ms.Normalizer()
+
+# Configure normalization method
+params = normalizer.getParameters()
+params.setValue("method", "to_one")  # Options: "to_one", "to_TIC"
+normalizer.setParameters(params)
+
+# Apply normalization
+normalizer.filterExperiment(exp)
+```
+
+## Peak Filtering
+
+### Threshold Mower
+
+Remove peaks below intensity threshold:
+
+```python
+# Create threshold filter
+mower = ms.ThresholdMower()
+
+# Configure threshold
+params = mower.getParameters()
+params.setValue("threshold", 1000.0)  # Absolute intensity threshold
+mower.setParameters(params)
+
+# Apply filter
+mower.filterExperiment(exp)
+```
+
+### Window Mower
+
+Keep only highest peaks in sliding windows:
+
+```python
+# Create window mower
+window_mower = ms.WindowMower()
+
+# Configure parameters
+params = window_mower.getParameters()
+params.setValue("windowsize", 50.0)  # Window size in m/z
+params.setValue("peakcount", 2)  # Keep top N peaks per window
+window_mower.setParameters(params)
+
+# Apply filter
+window_mower.filterExperiment(exp)
+```
+
+### N Largest Peaks
+
+Keep only the N most intense peaks:
+
+```python
+# Create N largest filter
+n_largest = ms.NLargest()
+
+# Configure parameters
+params = n_largest.getParameters()
+params.setValue("n", 200)  # Keep 200 most intense peaks
+n_largest.setParameters(params)
+
+# Apply filter
+n_largest.filterExperiment(exp)
+```
+
+## Baseline Reduction
+
+### Morphological Filter
+
+Remove baseline using morphological operations:
+
+```python
+# Create morphological filter
+morph_filter = ms.MorphologicalFilter()
+
+# Configure parameters
+params = morph_filter.getParameters()
+params.setValue("struc_elem_length", 3.0)  # Structuring element size
+params.setValue("method", "tophat")  # Method: "tophat", "bothat", "erosion", "dilation"
+morph_filter.setParameters(params)
+
+# Apply filter
+morph_filter.filterExperiment(exp)
+```
+
+## Spectrum Merging
+
+### Spectra Merger
+
+Combine multiple spectra into one:
+
+```python
+# Create merger
+merger = ms.SpectraMerger()
+
+# Configure parameters
+params = merger.getParameters()
+params.setValue("average_gaussian:spectrum_type", "profile")
+params.setValue("average_gaussian:rt_FWHM", 5.0)  # RT window
+merger.setParameters(params)
+
+# Merge spectra
+merger.mergeSpectraBlockWise(exp)
+```
+
+## Deconvolution
+
+### Charge Deconvolution
+
+Determine charge states and convert to neutral masses:
+
+```python
+# Create feature deconvoluter
+deconvoluter = ms.FeatureDeconvolution()
+
+# Configure parameters
+params = deconvoluter.getParameters()
+params.setValue("charge_min", 1)
+params.setValue("charge_max", 4)
+params.setValue("potential_charge_states", "1,2,3,4")
+deconvoluter.setParameters(params)
+
+# Apply deconvolution
+feature_map_out = ms.FeatureMap()
+deconvoluter.compute(exp, feature_map, feature_map_out, ms.ConsensusMap())
+```
+
+### Isotope Deconvolution
+
+Remove isotopic patterns:
+
+```python
+# Create isotope wavelet transform
+isotope_wavelet = ms.IsotopeWaveletTransform()
+
+# Configure parameters
+params = isotope_wavelet.getParameters()
+params.setValue("max_charge", 3)
+params.setValue("intensity_threshold", 10.0)
+isotope_wavelet.setParameters(params)
+
+# Apply transformation
+isotope_wavelet.transform(exp)
+```
+
+## Retention Time Alignment
+
+### Map Alignment
+
+Align retention times across multiple runs:
+
+```python
+# Create map aligner
+aligner = ms.MapAlignmentAlgorithmPoseClustering()
+
+# Load multiple experiments
+exp1 = ms.MSExperiment()
+exp2 = ms.MSExperiment()
+ms.MzMLFile().load("run1.mzML", exp1)
+ms.MzMLFile().load("run2.mzML", exp2)
+
+# Create reference
+reference = ms.MSExperiment()
+
+# Align experiments
+transformations = []
+aligner.align(exp1, exp2, transformations)
+
+# Apply transformation
+transformer = ms.MapAlignmentTransformer()
+transformer.transformRetentionTimes(exp2, transformations[0])
+```
+
+## Mass Calibration
+
+### Internal Calibration
+
+Calibrate mass axis using known reference masses:
+
+```python
+# Create internal calibration
+calibration = ms.InternalCalibration()
+
+# Set reference masses
+reference_masses = [500.0, 1000.0, 1500.0]  # Known m/z values
+
+# Calibrate
+calibration.calibrate(exp, reference_masses)
+```
+
+## Quality Control
+
+### Spectrum Statistics
+
+Calculate quality metrics:
+
+```python
+# Get spectrum
+spec = exp.getSpectrum(0)
+
+# Calculate statistics
+mz, intensity = spec.get_peaks()
+
+# Total ion current
+tic = sum(intensity)
+
+# Base peak
+base_peak_intensity = max(intensity)
+base_peak_mz = mz[intensity.argmax()]
+
+print(f"TIC: {tic}")
+print(f"Base peak: {base_peak_mz} m/z at {base_peak_intensity}")
+```
+
+## Spectrum Preprocessing Pipeline
+
+### Complete Preprocessing Example
+
+```python
+import pyopenms as ms
+
+def preprocess_experiment(input_file, output_file):
+    """Complete preprocessing pipeline."""
+
+    # Load data
+    exp = ms.MSExperiment()
+    ms.MzMLFile().load(input_file, exp)
+
+    # 1. Smooth with Gaussian filter
+    gaussian = ms.GaussFilter()
+    gaussian.filterExperiment(exp)
+
+    # 2. Pick peaks
+    picker = ms.PeakPickerHiRes()
+    exp_picked = ms.MSExperiment()
+    picker.pickExperiment(exp, exp_picked)
+
+    # 3. Normalize intensities
+    normalizer = ms.Normalizer()
+    params = normalizer.getParameters()
+    params.setValue("method", "to_TIC")
+    normalizer.setParameters(params)
+    normalizer.filterExperiment(exp_picked)
+
+    # 4. Filter low-intensity peaks
+    mower = ms.ThresholdMower()
+    params = mower.getParameters()
+    params.setValue("threshold", 10.0)
+    mower.setParameters(params)
+    mower.filterExperiment(exp_picked)
+
+    # Save processed data
+    ms.MzMLFile().store(output_file, exp_picked)
+
+    return exp_picked
+
+# Run pipeline
+exp_processed = preprocess_experiment("raw_data.mzML", "processed_data.mzML")
+```
+
+## Best Practices
+
+### Parameter Optimization
+
+Test parameters on representative data:
+
+```python
+# Try different Gaussian widths
+widths = [0.1, 0.2, 0.5]
+
+for width in widths:
+    exp_test = ms.MSExperiment()
+    ms.MzMLFile().load("test_data.mzML", exp_test)
+
+    gaussian = ms.GaussFilter()
+    params = gaussian.getParameters()
+    params.setValue("gaussian_width", width)
+    gaussian.setParameters(params)
+    gaussian.filterExperiment(exp_test)
+
+    # Evaluate quality
+    # ... add evaluation code ...
+```
+
+### Preserve Original Data
+
+Keep original data for comparison:
+
+```python
+# Load original
+exp_original = ms.MSExperiment()
+ms.MzMLFile().load("data.mzML", exp_original)
+
+# Create copy for processing
+exp_processed = ms.MSExperiment(exp_original)
+
+# Process copy
+gaussian = ms.GaussFilter()
+gaussian.filterExperiment(exp_processed)
+
+# Original remains unchanged
+```
+
+### Profile vs Centroid Data
+
+Check data type before processing:
+
+```python
+# Check if spectrum is centroided
+spec = exp.getSpectrum(0)
+
+if spec.isSorted():
+    # Likely centroided
+    print("Centroid data")
+else:
+    # Likely profile
+    print("Profile data - apply peak picking")
+```