Rise of the Machines: The Use of Machine Learning in SIMS Data Analysis
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The document discusses machine learning methods for analyzing spectroscopic data and classifying samples. It describes how machine learning techniques like random forests are data-driven and do not require assumptions about the distribution of the data, unlike classical statistical analyses. The document provides examples of how random forest classification was able to correctly classify over 88% of test spectra from SIMS images of amino acid-coated beads and how a random forest model classified a large FTIR image of cancer tissue in under 60 seconds. In conclusion, machine learning methods seem useful for spectroscopic data analysis and increased computing power will allow their broader application.
Rise of the Machines: The Use of Machine Learning in SIMS Data Analysis
- 1. RISE OF THE MACHINES THE USE OF MACHINE LEARNING IN SIMS DATA ANALYSIS Alex Henderson University of Manchester SurfaceSpectra Ltd http://about.me/henderson.alex Twitter: @AlexHenderson00
- 2. LOOK OUT! THE MACHINES ARE COMING!!
- 3. NO NEED TO BE AFRAID… Result!
- 4. QUESTIONS WE MIGHT ASK • Exploratory data analysis • What can we find out about these samples? • No prior knowledge required • Differences in chemical or physical state between groups of samples • Highlights spectral changes as function of group membership • Need to know which group each spectrum belongs to • Trend analysis • Spectral changes as a function of dependent variable: time, concentration, disease state etc. • Classification of samples • Spectral characteristics of groups • Prediction of unseen samples into known groups
- 5. DATA ANALYSIS APPROACHES CLASSICAL ANALYSIS Hypothesis driven Assumes a distribution of spectral response MACHINE LEARNING Data driven Interrogation of data leads to hypothesis Validation always required when building a predictive model
- 6. CLASSICAL ANALYSIS Assumes data obey the Central Limit Theorem Data is Normally distributed (Gaussian or Bell shaped curve) Mathematically we can derive 4 ‘moments’ • Mean (average) • Variance (standard deviation) • Kurtosis (pointedness) • Skewness (asymmetry) Other descriptions lead from these parameters eg Student’s t-test, ANOVA, MANOVA
- 7. MACHINE LEARNING No underlying assumption Need to generate a description of data
- 8. HISTORY OF MVA Classical multivariate analysis dates from 1930s Harold Hotelling, Ronald Fisher, Herman Wold and others • Principal components analysis (PCA) • Partial least squares (PLS) • Fisher’s discriminant analysis • Linear discriminant analysis (LDA), etc. Slide rule is King!
- 9. HISTORY OF MVA CONTINUED Computers become generally available in 1950s Speed and reproducibility of calculations becomes easier New approaches are developed Term ‘Machine Learning’ coined in 1990’s, describing a branch of computer science
- 10. BRAVE NEW WORLD!
- 11. Mechanical Turk plays chess in 1770 NEW?
- 12. Mechanical Turk CHEATS at chess in 1770 WELL…
- 13. WHAT FITS WHERE? Classical Analysis Machine Learning Exploratory analysis PCA K-means, HCA Differences in state between groups of samples Discriminant analysis LDA, QDA, CVA Random Forest Classification Trend analysis Regression analysis, MCR Random Forest Regression Classification of samples LDA, QDA Random Forest Classification SVM
- 15. RANDOM FOREST Ensemble method Collect lots of weak classifiers to build one strong one Collection of Decision Trees Computationally intensive Developed 1995 – 2001 MATLAB: TreeBagger Python: scikit.RandomForestClassifier
- 16. DECISION TREE An expression of an algorithm Weak classifier Move through each step in turn Boss around? Weather? Beer Pay day? Beer Work Work WorkYes No Sunny Rainy Windy Recent Long ago
- 17. ENSEMBLE OF TREES Randomly select subsets of variables (m/z intensities) Train multiple (few hundred) decision trees, each with different variables Each tree does the best it can with only a portion of the data See which trees are best and weight them higher
- 18. VARIABLE ZOO Ratio measurements taken for many animals For example: • Length of leg • Number of legs • Number of wings • Has horns/antlers? • Length of neck • Length of tail Many examples of each animal used No tree gets all measurements
- 19. TOO EASY? The giraffe is easily recognised by the number of legs and length of neck. Oh, and it’s not a bird… If any tree has those variables it would always identify the animal as a giraffe. No need for anything else.
- 20. A Gerenuk is a four-legged mammal with a long neck. The decision tree was good, but not good enough It needed to be tamed by other trees The Random Forest model prevents some trees from dominating the overall result WRONG!
- 22. Polystrene beads Each bead coated with a different amino acid SIMS image using Biotof 256 × 256 pixels 1000 amu bin-summed to 1 amu Data courtesy of Nick Winograd, Penn State University, USA, ~1999 CLASSIFICATION EXAMPLE
- 23. Two regions on each bead and also the substrate selected One region to train, the other to test Each region 400 pixels Square root taken Vector normalised TRAINING AND TEST REGIONS Test Test Test
- 24. RANDOM FOREST MODEL Training data (3 × 400 spectra) Each spectrum labelled: bead1, bead 2, or substrate Random Forest model constructed using scikit.RandomForestClassifier in Python 3.5 300 trees selected. Other parameters left as default Code executed in PyCharm 2017.1.2
- 25. Bead 1 Bead 2 Substrate Bead 1 97.5 1.5 1.0 Bead 2 3.0 96.0 1.0 Substrate 8.3 3.3 88.5 Percentage of correctly predicted values Diagonal (trace) indicates > 88% of test spectra correctly classified Caution: Result should be verified by cross-validation or bootstrap CONFUSION MATRIX Truth Prediction
- 26. Each decision tree uses different combination of mass values Determine which m/z values were used by the most accurate trees This is a measure of the importance of those variables: m/z VARIABLE IMPORTANCE
- 27. Trained Random Forest model used to predict class for each pixel in original image Render the image using result of classification Total time 15 sec PREDICTION OF ENTIRE IMAGE
- 28. FTIR – CANCER TISSUE Epithelium 24.3% Smooth muscle 50.7% Lymphocytes 2.5% Blood 0.2% Concretion 0.0% Fibrous stroma 12.3% ECM 10.0% Random forest classifier Trained on exemplars with pathologist 6 hour data acquisition 2.5 million spectra classified in < 60 sec No staining required, or de-waxing of the sample Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410D doi:10.1117/12.2043290
- 29. SUMMARY Machine Learning methods appear to be useful tools that we should consider for adoption Unsupervised, supervised classification and supervised regression options are all available Increased computer power may be required, but Moore’s Law is on our side here
- 30. IMAGE CREDITS Mechagodzilla: http://list25.com/25-famous-fictional-robots-history/ Simply explained: http://geekandpoke.typepad.com/geekandpoke/2012/01/simply-explained-dp.html Slide rule: https://commons.wikimedia.org/wiki/File:Slide_rule_scales_back.jpg Brave new world: https://commons.wikimedia.org/wiki/File:IBM_150_Extra_Engineers_1951.jpg Mechanical Turk 1: https://commons.wikimedia.org/wiki/File:Tuerkischer_schachspieler_windisch4.jpg Mechanical Turk 2: https://commons.wikimedia.org/wiki/File:Tuerkischer_schachspieler_racknitz3.jpg Scikit-learn cheat sheet: http://scikit-learn.org/stable/tutorial/machine_learning_map/ Forest: https://commons.wikimedia.org/wiki/File:Forest_Osaka_Japan.jpg Animal silhouettes: https://clipartfest.com/categories/view/4c03d8ea8a4bc1ffca947c8b8dab48af25908403/african- animal-silhouettes-clipart.html Giraffe: https://img.clipartfest.com/4294d3fb2739e14cec3845ef668dcdc0_life-size-african-animal-wall-african-animal- silhouettes-clipart_221-203.gif Gerenuk 1: https://500px.com/cindy_wheeler Gerenuk 2: http://wordwomanpartialellipsisofthesun.blogspot.co.uk/2015/01/gerenuk-giraffe-necked-gazelle-with.html Many Gerenuks: http://wordwomanpartialellipsisofthesun.blogspot.co.uk/2015/01/gerenuk-giraffe-necked-gazelle- with.html Beads: Nick Winograd, Penn State University, USA Cancer tissue: Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410D doi:10.1117/12.2043290 Tetsujin 28: http://goldenani.blogspot.co.uk/2013/01/1963-part-1-on-outside-looking-in.html