Added scripting example

docs-src/content/tutorials/_index.md

@@ -1,16 +1,16 @@
 +++
-title = "Tutorials"
+title = "Tutorials and example data"
 weight = 3
 +++
 
-Here you can find complete examples of common analysis tasks using freely available example data.
+Here you can find details on different ways of using SpectralAnalysis and freely available MSI data that can be used for testing.
 
 #### Example Data
 
 ##### Mouse Brain (MALDI MSI QTOF)
 This mouse brain dataset is available alongside [SpectralAnalysis](https://github.com/AlanRace/SpectralAnalysis/tree/master/example-data/mouse-brain). It was acquired using MALDI MSI with a QSTAR XL mass spectrometer.
 
-![MALDI mouse brain data]()
+![MALDI mouse brain data](/images/SpectralAnalysis-data-mousebrain.png?width=30pc)
 
 ##### Mouse Bladder (MALDI MSI Orbitrap)
 This mouse bladded dataset is available from the [PRIDE repository](https://www.ebi.ac.uk/pride/archive/projects/PXD001283). It was acquired using MALDI MSI with a AP-SMALDI imaging source coupled to an Orbitrap mass spectrometer. 

docs-src/content/tutorials/taylor-workflow.md

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++++
+title = "Scripting - datacube / kmeans"
+weight = 3
++++
+
+This script was originally written by Adam Taylor and can be used to automatically generate a mean spectrum, detect peaks, reduce the data to the peaks with signal-to-noise greater than 3, perform *k*-means clustering on the reduced data generate mean spectra for each cluster and save out all variables.
+
+This script shows how SpectralAnalysis can be used without the interface to perform more complex and automatable analysis routines.
+
+```matlab
+spectralAnalysisPath = 'C:\path\to\SpectralAnalysis';
+
+inputFolder = [spectralAnalysisPath filesep 'example-data' filesep 'mouse-brain']; %location of imzML files to process
+outputFoler = [spectralAnalysisPath 'filesep 'example-data' filesep 'mouse-brain'];
+filesToProcess = dir([inputFolder filesep '*.imzML']); %gets all imzML files in folder
+
+% Set up datacube generation variables
+preprocessingWorkflowFile = [spectralAnalysisPath filesep 'example-data' filesep 'mouse-brain' filesep 'mouse-brain-preprocessingWorkflow.sap']; %location of preprocessing file
+nzm_multiple = 3; % multiple of non zero median
+
+% Add SpectralAnalysis to the path - this only needs to be done once per MATLAB session
+disp('Setting up ');
+addpath(genpath(spectralAnalysisPath));
+addJARsToClassPath();
+
+% Generate preprocessing workflow
+preprocessing = PreprocessingWorkflow();
+preprocessing.loadWorkflow(preprocessingWorkflowFile);
+
+peakPicking = GradientPeakDetection();
+medianPeakFilter = PeakThresholdFilterMedian(1, nzm_multiple);
+peakPicking.addPeakFilter(medianPeakFilter);
+
+%%
+for i = 1:length(filesToProcess)
+    disp(['Processing ' filesToProcess(i).name]);
+
+    input_file = [filesToProcess(i).folder filesep filesToProcess(i).name];
+
+    %% Get the filename from the path
+    [~, filename, ~] = fileparts(input_file);
+
+    %% make datacubes from each dataset
+
+    % obtain total spectrum
+    disp(['Generating Total Spectrum for ' ,input_file]);
+    parser = ImzMLParser(input_file);
+    parser.parse;
+    data = DataOnDisk(parser);
+
+    spectrumGeneration = TotalSpectrum();
+    spectrumGeneration.setPreprocessingWorkflow(preprocessing);
+
+    totalSpectrum = spectrumGeneration.process(data);
+    totalSpectrum = totalSpectrum.get(1);
+
+    %% Peak picking
+    disp('Peak picking ');
+    peaks = peakPicking.process(totalSpectrum);
+    
+    spectralChannels_all = totalSpectrum.spectralChannels;
+    spectralChannels = [peaks.centroid];
+
+    %% Make datacube old
+    disp(['! Generating data cube with ' num2str(length(peaks)) ' peaks...'])
+
+    peakTolerance = -1;
+
+    reduction = DatacubeReduction(peakTolerance);
+    reduction.setPeakList(peaks);
+
+    addlistener(reduction, 'FastMethods', @(src, canUseFastMethods)disp(['! Using fast Methods?   ' num2str(canUseFastMethods.bool)]));
+    
+    dataRepresentation = reduction.process(data);
+    dataRepresentation = dataRepresentation.get(1);
+    dataRepresentation_struct = dataRepresentation.saveobj();
+
+    datacube = dataRepresentation.data;
+    pixels = dataRepresentation.pixels;
+
+    %% K means clustering
+    disp('Performing k-means clustering on top 1000 peaks with k = 2 and cosine distance')
+
+    [~, top1000idx] = maxk([peaks.intensity], 1000);
+    datacube_small = datacube(:,top1000idx);
+
+    [kmeans_idx, kmeans_c, ~, ~ ] = kmeans(datacube_small, 2, 'distance', 'cosine');
+
+    %% Make mean spectrum
+    disp('Saving cluster mean spectra')
+
+    datacube_clust1 = datacube(kmeans_idx == 1,:);
+    datacube_clust2 = datacube(kmeans_idx == 2,:);
+
+    mean_intensity_clust1 = mean(datacube_clust1);
+    mean_intensity_clust2 = mean(datacube_clust2);
+    mean_intensity_all = mean(datacube);
+
+    %% Save all
+    disp('Saving files')
+
+    save([outputFoler filesep filename '.mat'], '-struct', 'dataRepresentation_struct', '-v7.3')
+    save([outputFoler filesep filename '.mat'], ...
+        'peaks', 'spectralChannels_all', 'spectralChannels', 'kmeans_idx', 'kmeans_c', ...
+        'top1000idx', 'mean_intensity_clust1', 'mean_intensity_clust2', 'mean_intensity_all',...
+        '-append')
+
+    disp([input_file ' complete']);
+end
+```

example-data/mouse-brain/mouse-brain-preprocessingWorkflow.sap

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+<?xml version="1.0" encoding="utf-8"?>
+<preprocessingWorkflow version="1.0">
+   <preprocessingMethod class="QSTARZeroFilling">
+      <parameter description="Min Spectral Channel" value="53.319469"/>
+      <parameter description="Max Spectral Channel" value="999.272034"/>
+      <parameter description="Detector Bin Size" value="0.000186"/>
+   </preprocessingMethod>
+</preprocessingWorkflow>

examples/datacube_kmeans.m

@@ -0,0 +1,101 @@
+spectralAnalysisPath = '/home/alan/Documents/GitProjects/SpectralAnalysis/';
+
+inputFolder = [spectralAnalysisPath '/example-data/mouse-brain']; %location of imzML files to process
+outputFoler = [spectralAnalysisPath '/example-data/mouse-brain'];
+filesToProcess = dir([inputFolder filesep '*.imzML']); %gets all imzML files in folder
+
+% Set up datacube generation variables
+preprocessingWorkflowFile = [spectralAnalysisPath '/example-data/mouse-brain/mouse-brain-preprocessingWorkflow.sap']; %location of preprocessing file
+nzm_multiple = 3; % multiple of non zero median
+
+% Add SpectralAnalysis to the path - this only needs to be done once per MATLAB session
+disp('Setting up ');
+addpath(genpath(spectralAnalysisPath));
+addJARsToClassPath();
+
+% Generate preprocessing workflow
+preprocessing = PreprocessingWorkflow();
+preprocessing.loadWorkflow(preprocessingWorkflowFile);
+
+peakPicking = GradientPeakDetection();
+medianPeakFilter = PeakThresholdFilterMedian(1, nzm_multiple);
+peakPicking.addPeakFilter(medianPeakFilter);
+
+%%
+for i = 1:length(filesToProcess)
+    disp(['Processing ' filesToProcess(i).name]);
+
+    input_file = [filesToProcess(i).folder filesep filesToProcess(i).name];
+
+    %% Get the filename from the path
+    [~, filename, ~] = fileparts(input_file);
+
+    %% make datacubes from each dataset
+
+    % obtain total spectrum
+    disp(['Generating Total Spectrum for ' ,input_file]);
+    parser = ImzMLParser(input_file);
+    parser.parse;
+    data = DataOnDisk(parser);
+
+    spectrumGeneration = TotalSpectrum();
+    spectrumGeneration.setPreprocessingWorkflow(preprocessing);
+
+    totalSpectrum = spectrumGeneration.process(data);
+    totalSpectrum = totalSpectrum.get(1);
+
+    %% Peak picking
+    disp('Peak picking ');
+    peaks = peakPicking.process(totalSpectrum);
+
+    spectralChannels_all = totalSpectrum.spectralChannels;
+    spectralChannels = [peaks.centroid];
+
+    %% Make datacube old
+    disp(['! Generating data cube with ' num2str(length(peaks)) ' peaks...'])
+
+    peakTolerance = -1;
+
+    reduction = DatacubeReduction(peakTolerance);
+    reduction.setPeakList(peaks);
+
+    addlistener(reduction, 'FastMethods', @(src, canUseFastMethods)disp(['! Using fast Methods?   ' num2str(canUseFastMethods.bool)]));
+
+    dataRepresentation = reduction.process(data);
+    dataRepresentation = dataRepresentation.get(1);
+    dataRepresentation_struct = dataRepresentation.saveobj();
+
+    datacube = dataRepresentation.data;
+    pixels = dataRepresentation.pixels;
+
+    %% K means clustering
+    disp('Performing k-means clustering on top 1000 peaks with k = 2 and cosine distance')
+
+    [~, top1000idx] = maxk([peaks.intensity], 1000);
+    datacube_small = datacube(:,top1000idx);
+
+    [kmeans_idx, kmeans_c, ~, ~ ] = kmeans(datacube_small, 2, 'distance', 'cosine');
+
+    %% Make mean spectrum
+    disp('Saving cluster mean spectra')
+
+    datacube_clust1 = datacube(kmeans_idx == 1,:);
+    datacube_clust2 = datacube(kmeans_idx == 2,:);
+
+    mean_intensity_clust1 = mean(datacube_clust1);
+    mean_intensity_clust2 = mean(datacube_clust2);
+    mean_intensity_all = mean(datacube);
+
+    %% Save all
+    disp('Saving files')
+
+    save([outputFoler filesep filename '.mat'], '-struct', 'dataRepresentation_struct', '-v7.3')
+    save([outputFoler filesep filename '.mat'], ...
+    'peaks', 'spectralChannels_all', 'spectralChannels', ...
+    'kmeans_idx', 'kmeans_c', ...
+    'mean_intensity_clust1', 'mean_intensity_clust2', 'mean_intensity_all',...
+    'top1000idx',...
+    '-append')
+
+    disp([input_file ' complete']);
+end