Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier

Lauss, Martin; Frigyesi, Attila; Ryden, Tobias; Höglund, Mattias

doi:10.1186/1471-2407-10-532

Table 2 Balanced accuracies of prediction using the metagene classifier (ROC) and various classification algorithms and a t-test as feature selection criteria.

From: Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier

Phenotype/ Method	Training¹	Validation	Validation	Training	Validation	Validation	Training	Validation	Validation	Mean
ER ² (BC)	vandeVijver	WangY	Sotiriou	WangY	vandeVijver	Sotiriou	Sotiriou	vandeVijver	WangY
ROC⁵	0.95	0.89	0.74	0.89	0.94	0.77	0.77	0.90	0.88	0.85
SVM	0.94	0.82	0.71	0.82	0.95	0.73	0.69	0.95	0.82	0.83
SVMradial	0.94	0.86	0.70	0.87	0.93	0.73	0.76	0.93	0.84	0.83
SVMpoly	0.92	0.80	0.67	0.77	0.89	0.65	0.75	0.84	0.72	0.76
knn	0.94	0.83	0.70	0.84	0.93	0.78	0.77	0.95	0.78	0.83
rforest	0.98	0.85	0.73	0.84	0.93	0.73	0.76	0.98	0.83	0.84
rpart	1.00	0.79	0.61	0.82	0.90	0.63	0.72	1.00	0.75	0.78
bagging	1.00	0.84	0.72	0.86	0.91	0.72	0.81	1.00	0.84	0.84
lda	0.95	0.85	0.76	0.86	0.94	0.73	0.76	0.96	0.83	0.84
dlda	0.96	0.89	0.79	0.88	0.94	0.77	0.87	0.94	0.85	0.86
slda	0.94	0.86	0.74	0.85	0.94	0.77	0.77	0.94	0.85	0.85
neuralnet	0.94	0.78	0.69	0.81	0.92	0.72	0.76	0.97	0.80	0.81
ncc	0.96	0.89	0.78	0.88	0.94	0.75	0.82	0.95	0.87	0.86
MYCN ³ (NB)	WangQ	JanoueixL	Attiyeh	JanoueixL	WangQ	Attiyeh	Attiyeh	WangQ	JanoueixL
ROC	0.94	0.91	0.82	0.87	0.89	0.79	0.81	0.95	0.89	0.88
SVM	0.98	0.83	0.75	0.88	0.97	0.67	0.85	0.98	0.87	0.84
SVMradial	0.95	0.93	0.82	0.93	0.94	0.81	0.87	0.95	0.86	0.88
SVMpoly	0.95	0.86	0.74	0.79	0.88	0.69	0.82	0.90	0.79	0.81
knn	0.95	0.93	0.86	0.88	0.92	0.85	0.86	0.98	0.85	0.90
rforest	0.95	0.93	0.80	0.91	0.94	0.76	0.84	0.95	0.88	0.88
rpart	1.00	0.93	0.90	0.83	0.89	0.72	0.90	1.00	0.72	0.86
bagging	1.00	0.90	0.89	0.81	0.91	0.72	0.87	1.00	0.86	0.88
lda	1.00	0.70	0.83	0.69	0.94	0.73	0.86	0.98	0.65	0.80
dlda	0.94	0.90	0.86	0.90	0.94	0.86	0.84	0.95	0.85	0.89
slda	0.95	0.93	0.85	0.93	0.98	0.83	0.89	0.98	0.91	0.91
neuralnet	0.99	0.81	0.89	0.85	0.96	0.80	0.88	0.97	0.82	0.87
ncc	0.94	0.91	0.86	0.93	0.94	0.85	0.86	0.98	0.87	0.90
AD/SQ ⁴ (LC)	Angulo	Takeuchi		Takeuchi	Angulo
ROC	0.93	0.96		0.95	0.96					0.96
SVM	0.89	0.91		0.93	0.84					0.88
SVMradial	0.97	0.93		0.96	0.96					0.95
SVMpoly	0.89	0.92		0.90	0.96					0.94
knn	0.94	0.94		0.95	0.94					0.94
rforest	0.94	0.95		0.96	0.94					0.94
rpart	0.86	0.93		0.95	0.82					0.87
bagging	0.91	0.96		0.95	0.93					0.94
lda	0.80	0.91		0.93	0.83					0.87
dlda	0.94	0.95		0.95	0.92					0.93
slda	0.93	0.95		0.95	0.96					0.96
neuralnet	0.91	0.95		0.94	0.92					0.94
ncc	0.93	0.96		0.95	0.93					0.95

¹ Accuracies given in italics indicates accuracies obtained in the training set using LOOCV
² ER+ vs. ER-
³ MYCN amplification positive vs. MYCN amplification negative
⁴ adenocarcinoma vs. squamous cell carcinoma
⁵ the metagene classifier uses AUC values as feature selection criteria.
Abbreviations; BC = Breast Cancer, NB = Neuroblastoma, LC = Lung Cancer

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