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

Table 1 Estimates from the 1 normalized datasets for ArrayLeaRNA 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 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.

ISSN: 1471-2105