biological dataanalysis and chemometrics, course k27411 · dtu bioengineering, technical university...
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DTU Bioengineering, Technical University of Denmark
Fundamental disciplines in biological sciences • Classification (theory: taxonomy)
– Discrimination (diagnostics) – Identification – Nomenclature
• Cladification (theory: cladonomy, phylogeny, cladistics) • Modelling and predictions • Tests and validations
DTU Bioengineering, Technical University of Denmark
Engineering • The science by which the properties of matter and the sources of energy
in nature are made useful to man in structures, machines and products
•Measurement techniques, computers, language, definitions, properties, chemistry, physics, mathematical hard modelling, statistics, chemometrics and many other disciplines are necessary to be a good engineer
DTU Bioengineering, Technical University of Denmark
X-metrics • The use of multivariate statistics in the discipline X • Psychrometrics (used in psychology) • Taxometrics (used in taxonomy) • Biometrics (used in biology) • Technometrics (used in engineering) • Chemometrics (used in chemistry)
DTU Bioengineering, Technical University of Denmark
Chemometrics • Use of statistics and mathematics in chemical sciences, to measure and
interpret chemical data (also used for biological data)
• Biometrics has often been restricted to univariate statistics and taxometrics to biosystematics
DTU Bioengineering, Technical University of Denmark
Other definitions of chemometrics: • Empirical interactive data-driven modelling in chemistry (induction and
abduction) • Exploratory and confirmatory data-analysis (hypothesis generating and
hypothesis testing) in chemistry • Predictive multivariate modelling in chemistry
DTU Bioengineering, Technical University of Denmark
Important disciplines in chemometrics •Sampling, selection of objects and variables •Data transformations and pre-treatments •Clustering •Ordination (projection from N dimensions to few dimensions, eigen vector based analyses)
•Multivariate regressions, calibrations and predictions
•Neural networks •Validation (test set validation, boot-strapping, jackknifing, cross-validation)
•Graphical display and outlier analysis
DTU Bioengineering, Technical University of Denmark
Why use chemometrics? • Complex systems with many interactions are common in science • Indirect (and often non-destructive) observation of the world as it is • An expansion of the human perception (the full electromagnetic
spectrum) is possible • Chromatography and spectroscopy will always yield multivariate data
DTU Bioengineering, Technical University of Denmark
Most new analytical chemical methods give multivariate data •IR and NIR •UV-VIS & fluorescence •NMR and ESR •MS •TLC •HPLC •GC •CE •CCC
DTU Bioengineering, Technical University of Denmark
Interpreting chemical data •Abduction
–Sharp independent signals that can be interpreted: • NMR • IR • MS
•Induction –Soft overlayered signals (strong interaction, co-
linearity) • UV • Fluorescence • NIR • FIA
–Any chromatographic/spectrometric measurements on mixtures
DTU Bioengineering, Technical University of Denmark
From Jens Bo Holm Nielsen
NIR spectra, ”soft spectra”
DTU Bioengineering, Technical University of Denmark
No. variables > No. objects • Classical statistical methods will not work in that case (for example
multiple linear regression, linear learning machine etc.)
• Two solutions to this can be variable selection or classical statistics on scores from eigen vector analysis
DTU Bioengineering, Technical University of Denmark
Important methods in chemometrics (classification) • Cluster analysis
– Hierarchical clustering – Divisive clustering – Block-clustering and fuzzy clustering
• Ordination – Principal component analysis – Correspondence analysis – Multidimensional scaling
DTU Bioengineering, Technical University of Denmark
The Celestial Emporium of Benevolent Knowledge (encyclopedia from the 10. century) An arbitrary and idiosyncratic classification
1. Those that belong to the Emperor
2. Embalmed ones 3. Those that are trained 4. Suckling pigs 5. Mermaids 6. Fabulous ones 7. Stray dogs 8. Those that are not
included in this classification
9. Those that tremble as if they were mad
10. Innumerable ones 11. Those drawn with a very fine
camel's hair brush 12. Others 13. Those that have just broken a
flower vase 14. Those that resemble flies on a
distance
Classification of plants and animals:
Such classes are overlapping, sometimes non-existing or non-logical and idiosyncratic
DTU Bioengineering, Technical University of Denmark
Classification is central (in biology)!
Classification
Phenotype data
(differentiation)
Nomenclature Identification
Cladification
Phylogeny
Chemometrics
Cladistics
DTU Bioengineering, Technical University of Denmark
2.00 4.00 6.00 8.00 10.00 12.00 14.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.000.0
1.0
2.0
3.810.88
1.05
3.041.95
12.05
4.93
10.6510.109.106.785.23
6.18 7.73
12.58
13.00 13.63
3.850.70
0.55
0.96
3.05
2.751.10
1.47
3.12
12.0710.93
9.144.92
4.51
6.835.71
10.16
9.65
13.1914.73
13.85
Aurasperone B
Pyranonigrin A
Ochratoxin A (shoulder)
Malformin C, A, B1, B, A1
Rubrofusarin B
Malformin B2 Pyranonigrin B/C
Pyranonigrin A
Fumonisin B4
Fumonisin B2 Nigragillin
Funalenone
Nigragillin analogue Tensidol B
Fonsecin B
Fonsecin
Aurasperone G & E
Demethylkotanin
Tensyuic acid A
Aurasperone C
ESI+
TIC
(m/z
100
-900
) U
V/VI
S (2
00-7
00 n
m)
Time (min)
Aspergillus niger secondary metabolite HPLC profile (sharp signals)
DTU Bioengineering, Technical University of Denmark
Time2.00 4.00 6.00 8.00 10.00 12.00
%
5
2.00 4.00 6.00 8.00 10.00 12.00
%
0
Kir24877 1: TOF MS ES+ 404
2.71e310.27
9.698.15 12.90
Kir24877 1: TOF MS ES+ TIC
1.62e51.69
1.03
10.203.60
3.232.99
2.26
9.509.177.717.233.87
5.955.584.62
6.53 8.888.00
10.71 11.12
11.80
Ochratoxin A Calculated mass 404.0901 Deviation -4.2 ppm
HPLC-ESI+ chromatograms (Luna C18 (2) column) A. niger NRRL 3122 from YES agar. Upper extracted ion chromatogram m/z 404 and lower total ion chromatogram (m/z 100-900)
Fumonisin B2 Fumonisin B4
Chlorine isotope pattern
DTU Bioengineering, Technical University of Denmark
Clustering of some common Penicillia based on 31 extrolite biosynthetic families
P. verrucosum sensu latoUPGMA Yule
-1.00 -0.50 0.00 0.50 1.00
ver1 ver2 ver3 ver4 nor1 nor2 nor3 nor4 vir1 vir2 vir3 vir4 aur1 aur4 aur2 aur3 cyc1 cyc4 cyc2 cyc3 pol1 pol4 pol2 pol3 cru1 cru4 cru3 cru2 com1 com4 com2 com3 aet1 aet4 aet2 aet3
Series Verrucosa
Series Viridicata
Series Camemberti
DTU Bioengineering, Technical University of Denmark
Look, daddy, look at the big AACACTGTATCTAATTATT!!!!
Aren’t they cool the new barcodes they’ve given us?
As if that’s
something
Biosystematics: Genome or phenome?
Politiken, 16/3 2003
DTU Bioengineering, Technical University of Denmark
Methods used in cladistics • Parsimony • Maximum likelihood • Nearest neighbour • Bayes analysis
• Validation: Often boot-strapping
DTU Bioengineering, Technical University of Denmark
Penicillium verrucosum Stud. Mycol. 49, 2004
Cladification Based on beta-tubulin sequences
DTU Bioengineering, Technical University of Denmark
Important methods in chemometrics (regresssion) • Regression
– MLR (multiple linear regression) – PCR (principal component regression) – PLSR (partial least squares regression) – RR (ridge regression)
• Neural Networks
DTU Bioengineering, Technical University of Denmark
The different kinds of reseach
Pure basic reseach (Bohr)
Use-inspired basic research (Pasteur)
Pure applied research (Edison)
Considerations of use
Que
st fo
r fun
dam
enta
l und
erst
andi
ng
Yes
No
No Yes
DTU Bioengineering, Technical University of Denmark
The scientific method • Hypothesis • Prediction • Test and validation • Repeat The scientific method is a recursive system of matching theory with
observation A hypothesis is a tentatively held conjecture for the purposes of developing
predictions of empirical observations
DTU Bioengineering, Technical University of Denmark
The scientific method (deduction) • Discovery, observations, ideas, intuition, former results • Propose a hypothesis and connect it with logical derivatisations from
known theory and propose a mathematical model, prove by several experiments/observations (tests) and also try to disprove hypothesis (deduction: from the general to the specific)
DTU Bioengineering, Technical University of Denmark
The scientific method (induction) •Gather many objects and measure by a series of features.
•Classify and find latent features. •Predict by regression. •Validate. •Connect with known theory and set up hypotheses or set up experimental designs to find important features and their dimensionality
•(induction: from the specific to the general)
DTU Bioengineering, Technical University of Denmark
Abduction, deduction and induction • Science often exhibits a subtle interplay between abduction, induction
and deduction. Abduction is a common process of creating new generalizations, theories and hypothesis. Deduction takes a hypothesis to make a specific prediction. ”Then” induction is used to fit the evidence to the hypothesis.
• In chemometrics induction is often the first step (using a lot of objects and variables)
• Experimental design, including multivariate design, can be used to suggest system models, and sometimes such a model can give ideas for a more strict mathematical model (for example based on partial differential equations)
• However, in biology the systems to be modelled are very complex and full of interactions, thus multivariate statistical modelling is to be preferred
DTU Bioengineering, Technical University of Denmark
Levels of knowledge • One (few) example(s): ”Laymans science” • Neural networks and validation • X-metrics and validation • Statistics and distributions • Mathematical exact modelling • Essentialism (only few phenomena in this world are ”essential”)
DTU Bioengineering, Technical University of Denmark
Technology and science ”It’s alright in practice, will it ever work in theory?” • Theory (Plato): Know what (clever people) • Practice: Know how (skilled people)
DTU Bioengineering, Technical University of Denmark
Advantages of technology (applied research) • Holistic, not reductionistic • Context driven, not subject driven • Mission-oriented research, not ”blue skies” • Team work, not individual scholar • Divergent, not convergent thinking • Decisive criterion: does it work?
DTU Bioengineering, Technical University of Denmark
Beware of pure reductionism
We must reject this primitive and almost cannibalistic delution about knowledge, that an understanding of something requires first that we dismantle it, like a child who pulls a watch to pieces and spread out the wheels in order to understand the mechanism” (Thom, 1975)
DTU Bioengineering, Technical University of Denmark
Models are not reality
Precision
Realism Generality
Model
DTU Bioengineering, Technical University of Denmark
Systematic generalization (hierarchical reductionism)
”We very soon got six yards to the mile. Then we tried hundreds yeards to the mile. And then came the grandest idea of all! We actually made a map of the country, on the scale of a mile to a mile!”
”Have you used it much?” I enquired. ”It has never been spread out, yet, ” said Mein Herr: ”the farmers objected: they said it would cover the whole country, and shut out the sunlight! So now we use the country itself, as its own map, and I assure you it does nearly as well”
(Lewis Carroll, Sylvie and Bruno, 1893)
DTU Bioengineering, Technical University of Denmark
Chemometrics and science? •Find an important problem in your field of interest (FOI) to which there is yet no solution (think!). Propose a preliminary hypothesis.
•Observe and measure within the FOI •Use statistical and multivariate design •Propose a hypothesis (think!) •Experiments and/or observations: tests and predictions based on proposed model
•Reject hypothesis or accept it for the time being
DTU Bioengineering, Technical University of Denmark
This course • In this course you will have hands-on experience in how to treat data
with a lot of features (variables) measured on several or a lot of objects
DTU Bioengineering, Technical University of Denmark
Learning objectives of the course • Give an overview of important chemometric methods • Identify situations where exploratory data-analysis is required • Describe and use different forms of scaling, transformation and normalization • Understand and describe the difference between classification and regression • Understand and describe the difference between clustering and ordination • Apply and interpret principal component analysis (PCA) on multivariate data • Apply and interpret the principles of validation and outlier detection • Use and interpret cluster analysis • Describe, apply and interpret multiple linear regression (MLR) and ridge regression
(RR) and where to apply them in two data matrix problems • Describe, apply and interpret principal component regression (PCR) and partial least
squares regression (PLSR) and where to apply them in two data matrix problems • Apply and interpret correspondence analysis • Describe the method metric multidimensional scaling
DTU Bioengineering, Technical University of Denmark
Programs used •R, R-Studio (free software)
•(NTSYS (Exeter publishing) version 2.2 –A whole package on the methods used most frequently: used earlier in this course
–(http://www.exetersoftware.com)(USA))
•(UNSCRAMBLER version 10.3 (CAMO, Norway) –(you can buy your own version (but it is expensive)) (http://www.camo.com))