Stefan Arnborg, KTH, SICS

Ingrid Agartz, Håkan Hall, Erik Jönsson, Anna Sillén, Göran Sedvall, Karolinska Institutet

’Principles of Data Mining and Knowledge Discovery’,Helsinki, Aug 2002

http://www.nada.kth.se/~stefan

Data Mining in Schizophrenia Research

HUBIN page: 1000 hits/day

Schizophrenia -Questions and Clues

• Cause(s) of schizophrenia not known.• Medication effective against some symptoms - discovered by

chance 100-2000 years ago.• Does not appear in animals-no experimental clues.• Explanation models vary over time.• Disturbed neuronal circuitry in schizophrenia?

(currently hottest hypothesis)• Influenced by genotype or/and environment?

(clustering in families)

Schizophrenia -Questions and Clues

• Which processes result in disease?• Traces of disturbed development visible in MRI

(anatomy) and blood tests?• Genetic risk factors?• Causal pathways?• MAIN PROBLEM:

Connect psychiatry to physiology

Preliminary analysis

Test case:144 subjects: 61 affected, 83 controlsVariables:•Diagnosis (DSM-IV)•Demography (age, gender, ..)•Blood tests (liver, heart,…)•Genetics (20 SNP:s, receptor, growth factors, …)•Anatomy (MRI)•Neuropsychology(working memory, reactions)•Clinicaltest batteries (type of delusions, history, medication)

Brain boxes

Picture fromBRAINS II manual,Magnotta et al,University of Iowa

Manually drawn vermis regions

ROIs drawn by GakuOkugawa

Single Nucleotide Polymorphism

A U G U U C C A U U A U U G U

A U G U U U C A U U A U U A U

RNA:

Protein A Phe

Phe

His

His

Tyr

Tyr

Cys

Phe

non-coding SNP

coding SNP

TyrProtein A’

Protein A can be slightly different from A´

Some genes studied

• DBH dopamine beta-hydroxylase• DRD2 dopamine receptor D2 +• DRD3 dopamine receptor D3• HTR5A serotonin receptor 5A• NPY neuropeptide Y• SLC6A4 serotonin transporter• BDNF brain derived neurotrophic factor• NRG1 neuregulin +

Intracranial volume (ml)

Cumulative distribution

+ = schizo = controls

Elementary Visualizations MRI Intracranial volume

Elementary VisualizationsMRI data

Total CSF volumes (ml)

Cumulative distribution

+ = schizo = controls

p < 0.0002

Men

Women25 30 35 40 45 50 55

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sub White-women

30 35 40 45 50 55 60 650

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sub White-men

Subcortical white

+ = schizo = controls

Subcortical white

+ = schizo = controls

Gender differences

MRI

Which methods to use?

• Visualizations, cdf and scatter plots, give intuitive grasp of variables - problems with many interrelated variables

• Statistical modelling required to decide significance of visible trend, and to rank effects

Statistical methods

• Bayesian methods intuitive and rational - but conventional testing required for publications

• Linear models - need to account for mixing and over-dispersion(Glenn Lawyer thesis project).

• Mixture models(Valery Savcenko thesis project).• Discretization and Bayesian analysis of discrete

distributions - intuitive, but information lost

Model adequate? -Gelman’s post-predictive check

• Best tested with classical p-values.• Determine posterior for parameter:

• Design test function • Compute p-value:

• Reject model if p small, e.g., <1%, <5%

f (λ |D)∝ f(D |λ) f (λ)

t : D→ Rp=P(t(Dr )<t(D))

Dr ~f (⋅|λ) f (λ |D)

Graphical models

Y

Z

X

Y

Z

X

Y

Z

Xf(x,y,z)=f(x)f(y)f(z)

f(x,y,z)=f(x,z)f(x,y)/f(x)

f(x,y,z)

MRI volumes, blood, demography

Dia

BrsCSF TemCSF

SubCSF TotCSF

Multivariate characterization by graphical models

Adding Vermis variables

Dia

BrsCSF TemCSF

PSV

10 20 30 40 50 60 70 80 90 100

5

10

15

10 20 30 40 50 60 70 80 90 100

5

10

15

10 20 30 40 50 60 70 80 90 100

5

10

15

left right CSF grey white CerebellumVermis CC Brainstem FrontalParietalVent2TemporalTotal ic

1:RAVTA16:RAVLTATOT11:TMT14:WAIS-R15:WCST64

Pos Corr

Neg Corr

Area Code

Learning, Intelligence, Executive vs Anatomy

10 20 30 40 50 60 70 80 90 100

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Color code(0 to 1):

p-values

20 40 60 80 100

5

10

15

rho:s

20 40 60 80 100

5

10

15

cw

20 40 60 80 100

5

10

15

20 40 60 80 100

5

10

15

cm

20 40 60 80 100

5

10

15

20 40 60 80 100

5

10

15

aw

20 40 60 80 100

5

10

15

20 40 60 80 100

5

10

15

am

20 40 60 80 100

5

10

15

20 40 60 80 100

5

10

15

20 40 60 80 100

10

20

30

20 40 60 80 100

10

20

30

1:RAVTA16:RAVLTATOT11:TMT14:WAIS-R15:WCST64

leftrightCSFgreywhiteCerebellar-TonsilCerebellumVermisCorpusCallosumIntracranialBrainstemFrontalOccipitalParietalSubcorticalVentr2Temporal

2 4 6 8 10 12 14 16 18

2

4

6

8

10

12

14

16

18

1RAVLTA1 Verbal inlärning2RAVLTA2 Verbal inlärning3RAVLTA3 Verbal inlärning4RAVLTA4 Verbal inlärning5RAVLTA5 Verbal inlärning6RAVLTATOT Verbal inlärn., total7RAVLTB Verbal inlärn, distraktion8 RAVLTA6 Verbalt minne, om.9 RAVLTA7 Verbalt minne, fördröjt10 CPT d' Uppmärksamhet11 TMT A Visuo-motorik, snabbhet12 TMT B Visuo-motorik, flexibilitet13 LNS Arbetsminne 14 WAIS-R Verbal begåvning, IQ.15 WCST64 categories Exekutiv funktion16 WCST64 total errors Exekutiv funktion17 WCST64 pers errors Exekutiv funktion18 WCST64 pers. respons Exekutiv funktion

Covariances: Cognitive Performance Index

5 10 15 20 25 30 35 40 45 50

5

10

15

20

25

30

35

40

45

50

Correlationcoefficient-left vs right

CSF grey white w+g

MR brain volumes

0.5 1 1.5 250

52

54

56

58

60

62

64

66

68

VermisMiddle-*w+g*-left-

rho=0.33118 pv=0.1686

0.5 1 1.5 2 2.5 340

45

50

55

60

65

70

VermisMiddle-*w+g*-left-

rho=0.26018 pv=0.1407

0 0.5 1 1.535

40

45

50

55

60

65

70

75

VermisMiddle-*w+g*-left-

rho=0.66363 pv=0.0221

0 0.5 1 1.5 220

25

30

35

40

45

50

55

60

65

VermisMiddle-*w+g*-left-

rho=0.14339 pv=0.2405

wc mcwa ma

cpd

0.5 1 1.5 27

8

9

10

11

12

13

14

15

VermisLower-*w+g*-right-

rho=0.27032 pv=0.2196

0 0.5 1 1.5 27

8

9

10

11

12

13

14

15

16

VermisLower-*w+g*-right-

rho=0.15481 pv=0.2615

0 0.5 1 1.5 22

4

6

8

10

12

14

VermisLower-*w+g*-right-

rho=-0.68411 pv=0.9983

0 0.5 1 1.5 24

6

8

10

12

14

16

VermisLower-*w+g*-right-

rho=-0.12977 pv=0.7403

wc mcwa ma

Executiveability

Pairs associated to Diagnosis

Y

Z

D

Y

Z

D

Y

Z

D

Y

Z

D

Y and Z co-vary differentlyfor Affected and Controls

Age-dependency of Posterior Superior Vermis

Age at MRI

Post sup vermis

+ = schizo = controls

70 80 90 100 110 120 1300.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

ParWhite

0

No co-variation between Posterior inferior vermis and parietal white for affected

Parietal white

Post inf vermis

+ = schizo = controls

PSV has best explanatory power

affected - healthy

0.05 0.1 0.15 0.2 0.25 0.30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

PS VermisPosterior superior vermis

+ = schizo = controls

Classification explains data!(Can Mert Thesis project)

XY

Z

XY

Z

H

W W

But where is the model?

Model is parametrized mixture

XY

Z

H

WParameter is mixing vectorand classification partition!

P(W,Y,Z| H,λ ) =P(λ ) λiP(W(i))P(Y(i))P(Z(i))i

∑

P(H,λ |W,Y,Z) =P(W,Y,Z |H,λ ) f(H |λ ) f (λ )

Autoclass1

Total gray

A= schizC= controls

Weak signals in genetics data

• Numerous investigations have indicated ‘almost significant’ signals of SNP:s to diagnosis

• Typically, these findings cannot be confirmed in other studies - populations genetically heterogeneous and measurements nonstandardized.

• We try to connect SNP:s both to diagnosis and to other phenotypical variables

• Multiple testing and weak signal problems.

Genetics data - weak statistics

Gene SNP type genotypes

DRD3 SerGly A/C 49 59 14DRD2 Ser311Cys C/G 118 4 0NPY Ley7Pro A/C 1 7 144DBH Ala55Ser G/T 98 24 0BDNF Val66Met A/G 5 37 80HTR5A Pro15Ser C/T 109 11 2PNOC Gln172Arg A/G 11 37 28SLC6A4 (del(44bp)in pr) S/L 20 60 42

Empirical distribution by genotypeGene BDNF (schiz + controls)

Frontal CSF

A/A A/G G/G

Cumulativedistribution

25 30 35 40 45 50 55 60 65 70 750

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Frontal-CSF-right-

G/G G/A A/A

0 20 40 60 80 100 120 1400

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

Benjamini & Yekutieli, Annals of Math Stat, (ta)

‘no effect’Observedp-values

FDRi 71

FDRd 62

Bonferroni-Hochberg-Benjamini methodsMRI and lab data

Number of p-values

p-values

Compensating multiple comparisons

• Bonferroni 1937: For level and n tests, use level /n

• Hochberg 1988: step-up procedure• Benjamini,Hochberg 1996: False Discovery

Rate• J. Storey, 2002: pFDRi, pFDRd• Bayesian interpretations being developed

(Wasserman & Genovese, 2002)

Diagnosis-genotype

bdnf drd2 nrg1

0.1136 0.0735 0.8709

0.0801 0.2213 0.7666

0.0316 0.0823 0.6426

0.5499 1.0000 0.0244

0.7314 0.7312 0.0103

bdnf drd2 nrg1

0.1137 0.0749 0.8744

0.7293 0.7276 0.0096

21 tests on three genes

multiple comparisons:

what is the significanceof min p-values 1,1,2,3%in 20 tests?What is the probability of obtainingmore extreme result when no effect?

| | | |

| | | |

| | | |

| | | |

| | | |

| | | | . . .

7% - not quite significant!but better than Bonferroni: 20%

p-values 3%, 2%, 1%, 1% in 20 tests

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1TPH:SNP000002367

q-value - FDR rate in prefix

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1BDNF:SNP000006430

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1sex

Beyond Robustness: SVM

Support Vector Methods are - distribution-independent- insensitive to dimension- error of classifier no more than

R

γbl

+cl

R2

γ2 log2l +log1/δ( )⎛

⎝ ⎜ ⎞

⎠

with probability training set size l,unclassified examples b,margin pdf support within R-sphere

+

+ ++

-

-

-

-

+

-

--

b=1

Beyond Robustness: SVM

Support Vector Methods are - distribution-independent- insensitive to dimension- error of classifier no more than

R

γbl

+cl

R2

γ2 log2l +log1/δ( )⎛

⎝ ⎜ ⎞

⎠

with probability training set size l,unclassified examples b,margin all examples within R-sphere

+

+ ++

-

-

-

-

+

-

--

b=0

That’s all, folks!

• High-quality databases for medical research of the HUBIN type open up for intelligent data analysis methods used in engineering and business

• Already with the limited data presently available, interesting clues emerge

• Multiple testing considerations are important• Long term effort - stable economy and

engagement is vital.