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Table 1 Estimates from the 1 normalized datasets for ArrayLeaRNA approach

From: Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA – a Bayesian approach

Parameters and Statistics Dataset I Dataset II Dataset III
Average of 2R from genomic features 0 0 0
Standard deviation of R from genomic features 0.566 0.244 0.566
Parameter b of the Gumbel distribution for R 0.882 0.381 0.883
Parameter α of the prior distribution for A 1.65 4.15 2.87
Parameter β of the prior distribution for A 2.11 6.51 4.26
Prior expected value for A -0.509 -0.220 -0.509
Prior variance for A 0.614 0.0393 0.318
Parameter α of the 3posterior distribution for A 7.65 8.15 3.87
Parameter β of the posterior distribution for A 8.11 10.5 5.26
Posterior expected value for A -0.110 -0.121 -0.389
Posterior variance for A 0.150 0.0189 0.122
  1. 1 Normalization is based on the genomic features measurements; 2 Logarithm to the base 2 of the ratio between the intensities; 3 Assuming p1= p0 = 0.5, R = 0 and 6, 4 and 1 replicates for Datasets I, II and III, respectively.