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Table 1 Operating characteristics for simulated data

From: A Bayesian calibration model for combining different pre-processing methods in Affymetrix chips

 

DE

Non DE

FP (%)

TN (%)

TP (%)

FN (%)

sd

Combined

200

800

21 (2.6)

779 (97.4)

179 (89.5)

21 (10.5)

5.0

Method 1 (exp(λ) = 1, ϕ = 1)

200

800

61 (7.6)

739 (92.4)

139 (69.5)

61 (30.5)

5.5

Method 2 (exp(λ) = 0.5, ϕ = 0.5)

200

800

80 (10.0)

720 (90.0)

120 (60.0)

80 (40.0)

4.8

Method 3 (exp(λ) = 0.5, ϕ = 2)

200

800

26 (3.2)

774 (96.8)

174 (87.0)

26 (13.0)

4.1

Method 4 (exp(λ) = 2, ϕ = 0.5)

200

800

122 (15.2)

678 (84.8)

78 (49.0)

122 (61.0)

3.3

Method 5 (exp(λ) = 2, ϕ = 2)

200

800

45 (5.6)

755 (94.4)

155 (77.5)

45 (22.5)

5.8

  1. The table presents the number of False Positives (FP), True Negatives (TN), True Positives (TP) and False Negatives (FN) in the first 200 genes ranked accordingly to their tail posterior probability. Note that FP = FN since the size of the list of differentially expressed genes is equal to the number of true positives. The combined method shows the smallest number of FP and FN. Out of the 5 pre-processing methods, the one characterised by a relative bias parameter ϕ j > 1 and a variability parameter exp(λ j ) < 1 (Method 3) shows the best performance, due to the combination of high signal and low variability around the mean gene expression. The standard deviation calculated on the 10 runs is also reported in the table. Note that as FP, FN, TP and TN are a linear function of each other given a fixed sum, the standard deviation is the same for the 4 variables.
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BMC Bioinformatics

ISSN: 1471-2105

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