Classifier training

Classifier training

Mann Whitney

Predictor discoveryin training set

4

Training setSJIA (12 F, 12 Q)POLY (13 F, 10 Q)

1

DIGEraw gel imagesSJIA (10 F, 10 Q)POLY (5 F, 5 Q)

Spot findingspot alignment

feature extraction

889 discrete spot features

Classifier training

ClassifySJIA F vs QPOLY F vs Q

DIGE analysis Prediction analysis (LDA)

Predictor testin testing set

5

Testing setSJIA (10 F, 10 Q)POLY (10 F, 5 Q)

2

Cluster analysis

2d hierarchicalclustering

heatmap plotting

Normalizationmanual review

Manual review

MSMS ID96 spots

3

8 proteincandidates

Assay development

7 ELISA assays

7

DiscriminateSJIA F

KDFI

SJIA PLASMA BIOMARKER DISCOVERY STUDY DESIGN

ClusteringBox-and-Whisker Analysis

DiscriminateSJIA FSJIA QPOLY FPOLY Q

ELISA assays

ELISA assay

ELISA assayFC (27 )KD (10)

Two class classification

Classification analysis

9

ELISA assayQ->F (11)Q->Q (14)

Test to anticipate SJIA F in Q

LDAFisher exact test

P < 10-5

randomization

Blind testing

ClassifySJIA F vs QPOLY F vs Q

Fisher exact test

Training setSJIA F(12) KD (7), FC (15)

ClassifySJIA F vs Non SJIA F

Testing setSJIA F(10) KD (3), FC (12)

randomization

Blind testing

ClassifySJIA F vs Non SJIA F

Fisher exact test

8

Two dimensional DIGE analysis identified 96 protein spots differentially expressed between SJIA flare and quiescence

A B CF Q F Q

SJIA POLY

F Q

SJIA

F Q

POLY

DIGE analysis reveals a seven protein biomarker panel in plasma clearly differentiating SJIA flare from quiescence

B C DF Q F Q

SJIA POLY

F Q

SJIA

F Q

POLY

A2M

APOA1

SAP

CRP

HP

MRP14

SAAMRP8

A2M

APOA1

SAP

CRP

HP

MRP14

SAAMRP8

F Q F QSJIA POLY

APOA1 SAPCRP HP MRP14 SAAMRP8

Relative expression

2

3

4

1

0

A2M

A

Training setn = 24

12 12

Clinicaldiagnosis F Q

n =

LDA

11 1

1 11

Classified as F

Classified as Q

PercentAgreementwith clinical

diagnosis

91.6% 91.6%+ -

91.6%

Overall

P = 1.0 X 10-3

Testing setn = 20

10 10


n =

Testing

8 3

2 7

Classified as F

Classified as Q


diagnosis

80% 70%+ -

75%

Overall

P = 7 X 10-2

B CA

Biomarker panelof 7 members

1. A2M

2. APO AI

3. CRP

4. HP

5. MRP8/MRP14

6. SAA

7. SAP

SJIA SJIA

DTraining

SJIA F SJIA Q SJIA F SJIA Q

Testing

Pred

icte

d pr

obab

ilitie

sPatient samples

E

Sen

sitiv

ity

1- Specificity

CRP : AUC=0.58

SJIA F vs. Q

panel : AUC=0.82

ESR : AUC=0.86

ELISA analysis validates the seven protein biomarker panel in plasma

Training setn = 23

13 10


n =

LDA

10 5

3 5

Classified as F

Classified as Q


diagnosis

76.9% 50%+ -

65.2%

Overall

P = 0.41

Testing setn = 15

10 5


n =

Testing

3 0

7 5

Classified as F

Classified as Q


diagnosis

30% 100%+ -

53.3%

Overall

P = 0.20

A B

POLY POLY

CTraining

PF PQ PF PQ

Testing

Pred

icte

d pr

obab

ilitie

sPatient samples

D

1- Specificity

CRP : AUC=0.64

POLY F vs. Q

panel : AUC=0.64

Sen

sitiv

ity

ELISA analysis invalidates the seven protein biomarker panel in POLY plasma

Training setn = 25

11 14

Clinicaldiagnosis QF QQ

n =

LDA

6 2

5 12

Classified as QF

Classified as QQ


diagnosis

54.5% 85.7%+ -

72%

Overall

P = 0.08

A

SJIA

B

Pred

icte

d pr

obab

ilitie

s

Patient samples

C

CRP : AUC=0.59

SJIA QF vs. QQ

panel : AUC=0.78

ESR : AUC=0.60

Training

QQ QF

Sen

sitiv

ity

1- Specificity

ELISA analysis shows the ineffectiveness of seven protein plasma biomarker panelin prognosis of impending SJIA flare

B C

MRP14

F KD FC

SJIA

A2M

APOA1

SAP

CRP

HP

SAAMRP8

1210 12

SJIAF KD FCData set

n = 34

10

Clinicaldiagnosis

SJIAF

NOT-SJIAF

n =

Unsupervised clustering

7

3

1

23

Clustered as SJIA F

Clustered as NOT-SJIA F


diagnosis

70% 95.8%+ -

88.2%

Overall

P = 1.6 X 10-4

24

Prot

ein qu

antity

F KD FCSJIA

APOA1 SAPCRP HP MRP14 SAAMRP8A2M

2

3

4

1

0

A

DIGE analysis shows that seven protein SJIA flare panel in plasma clearly differentiating SJIA flare from confounding Kawasaki and febrile illness

Training setn = 34

12 22

Clinicaldiagnosis

n =

LDA

12 0

0 22


diagnosis

100% 100%+ -

100%

Overall

P = 7.4X 10-7

Testing setn = 25

10 15

Clinicaldiagnosis

n =

Testing

9 0

1 15


diagnosis

90% 100%+ -

93.3%

Overall

P = 4.9 X 10-6

A B

SJIAF

C

Training

FC

Testing

Pred

icte

d pr

obab

ilitie

s

Patient samples

712 15

SJIAF KD FC

NOTSJIA F

SJIAF

NOTSJIA F

310 12

SJIAF KD FC

KD SJIA F FC KD SJIA F

Clustered as SJIA F

Clustered as NOT SJIA F

ELISA analysis validates the utility of the seven protein SJIA flare panel in plasma to discriminate SJIA flare from confounding Kawasaki and febrile illness

Training setn = 39

16 23


n =

LDA

11 3

5 20

Classified as F

Classified as Q


diagnosis

68.8% 87%+ -

79.5%

Overall

P = 6.0X 10-4

Bootstrap setn = 73

34 39


n =

Testing

29 7

5 32

Classified as F

Classified as Q


diagnosis

85.3% 82.1%+ -

83.5%

Overall

P = 5.9 X 10-9

B C

SJIA SJIA

DTraining

SF SQ SF SQ

Bootstrap confirmation

Pred

icte

d pr

obab

ilitie

sPatient samples

E

1- Specificity

Bootstrap : AUC=0.90

SJIA F vs. Q

Training : AUC=0.84

Sen

sitiv

ity

A


1. TIMP1

2. MMP9

3. IL18

4. RANTES

Agilent protein array analysis reveals a four protein SJIA flare panel in plasma clearly differentiating SJIA flare from quiescence

Training setn = 13

6 7


n =

LDA

5 1

0 6

Classified as F

Classified as Q


diagnosis

100% 85.7%+ -

84.6%

Overall

P = 0.01

Bootstrap setn = 16

6 10


n =

Testing

5 2

1 8

Classified as F

Classified as Q


diagnosis

83.3% 80%+ -

81.25%

Overall

P = 0.03

A B

POLY POLY

CTraining

PF PQ PF PQ

Pred

icte

d pr

obab

ilitie

sPatient samples

D

1- Specificity

Bootstrap: AUC=0.91

POLY F vs. Q

Training: AUC=1

Sen

sitiv

ity

A


1. TIMP2

2. IGFBP-3

3. IGFBP-6

4. VCAM-1

Agilent protein array analysis reveals a four protein POLY flare panel in plasma clearly differentiating POLY flare from quiescence

Bootstrap confirmation

Classifier training

Documents

Transcript of Classifier training