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Table 3 Additional models for confusion matrices/discrepancies

From: In-silico performance, validation, and modeling of the Nanostring Banff Human Organ transplant gene panel using archival data from human kidney transplants

A. Average discrepancies between Diagnoses All PC

  

Model

Target

Data

% Discrepancies

  

Multinomial logistic regression

Diagnoses

ALL PC

37.3

 

Boot strap forest

Diagnoses

ALL PC

24.1

 

Partial least squares

Diagnoses

ALL PC

37.3

 

B. Average discrepancies between Clusters All PC

  

Model

Target

Data

% Discrepancies

  

Multinomial logistic regression

Clusters

ALL PC

10.0

 

Boot strap forest

Clusters

ALL PC

12.2

 

Partial least squares

Clusters

ALL PC

15.1

 

C. Average discrepancies CPPC, UPC, & PBPC

  

Model

Target

Data

% Discrepancies

  

Multinomial logistic regression

Diagnoses

CPPC

36.9

 

Boot strap forest

Diagnoses

CPPC

25.6

 

Partial least squares

Diagnoses

CPPC

27.2

 

Multinomial logistic regression

Diagnoses

UPC

24.4

 

Boot strap forest

Diagnoses

UPC

24.9

 

Partial least squares

Diagnoses

UPC

27.0

 

Multinomial logistic regression

Diagnoses

PBPC

35.2

 

Boot strap forest

Diagnoses

PBPC

27.5

 

Partial least squares

Diagnoses

PBPC

29.2

 

D. Average per sample discrepancies between Diagnoses All PC

  

Model

Target

Data

% Discrepancies

  

Multinomial logistic regression vs Boot strap forest

Diagnoses

ALL PC

33.1

 

Multinomial logistic regression vs Partial least squares

Diagnoses

ALL PC

32.7

 

Boot strap forest vs Partial least squares

Diagnoses

ALL PC

33.1

 

E. Average per sample discrepancies between Clusters All PC

  

Model

Target

Data

% Discrepancies

  

Multinomial logistic regression vs Boot strap forest

Clusters

ALL PC

12.4

 

Multinomial logistic regression vs Partial least squares

Clusters

ALL PC

13.5

 

Boot strap forest vs Partial least squares

Clusters

ALL PC

14.4

 

F. Additional models for diagnoses all PC, CPPC, PBPC, & UPC*

Model

Target

Data

Mean accuracy

Average precision/recall

Percent errors

Extreme Gradient Boosting

Diagnoses

ALL PC

0.74

0.79

36.5

Linear Discriminant Analysis

Diagnoses

CPPC

0.75

0.78

32.6

Extra Tree Classifier

Diagnoses

PBPC

0.75

0.76

35.3

Linear discriminant analysis

Diagnoses

UPC

0.76

0.70

33.9

G. Additional models for clusters all PC, CPPC, PBPC, & UPC*

Model

Target

Data

Mean accuracy

Average precision/recall

Percent errors

Multinomial logistic regression

Clusters

ALL PC

0.93

0.95

6.9

Extra tree classifier

Clusters

CPPC

0.90

0.93

6.9

Extreme gradient boosting

Clusters

PBPC

0.92

0.94

6.2

Multinomial logistic regression

Clusters

UPC

0.92

0.94

6.9

  1. *Best model chosen; N = 12