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Table 1 Results of logistic regression for intensity dependence.

From: Putative null distributions corresponding to tests of differential expression in the Golden Spike dataset are intensity dependent

 

Median

MAD

dataset

original

re-loess

original

re-loess

9a

184

(8.23e-39)

4.37

(0.358)

81.5

(8.28e-17)

62.6

(8.26e-13)

9b

246

(5.02e-52)

3.2

(0.525)

48.1

(9.06e-10)

49.9

(3.79e-10)

9c

225

(1.56e-47)

3.41

(0.492)

83.4

(3.26e-17)

62.3

(9.6e-13)

9d

271

(1.85e-57)

4.02

(0.403)

71.7

(9.93e-15)

59

(4.73e-12)

9e

104

(1.28e-21)

7.61

(0.107)

24.2

(7.38e-05)

45.3

(3.37e-09)

10a

151

(1e-31)

6.61

(0.158)

82.3

(5.69e-17)

35.6

(3.47e-07)

10b

190

(4.86e-40)

3.19

(0.527)

102

(4.63e-21)

32.5

(1.54e-06)

10c

214

(4.52e-45)

8.12

(0.0874)

124

(6.76e-26)

47.7

(1.11e-09)

10d

238

(2.1e-50)

4.62

(0.329)

157

(6.06e-33)

39.4

(5.63e-08)

10e

105

(8.49e-22)

12

(0.0171)

21.9

(0.000208)

36.4

(2.43e-07)

  1. The probability that the control samples will have a value greater than the matched spike-in samples was modeled as the logit of a function of a 4thorder polynomial for rankit intensity. Values for the median and the MAD (median absolute deviation) were considered. The deviances and p-values (in bold) for the comparison of the polynomial model to a constant null model are provided and are consistent with the results presented in Figure 6. Re-loessing the data using only the fold change 1 all but eliminates the relationships between intensity and relative centering of the two sample populations. However, the relationships between intensity and relative variability of the expression values remain, although they are greatly diminished.
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BMC Genomics

ISSN: 1471-2164

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