The bottom-up PC approach was designed to estimate the genetic parameters of large, over-parameterized datasets, for which the estimation of the complete, full rank dataset might not be possible.
For the estimation of one curve, the dataset of the corresponding cell line was used to estimate the four parameters of one curve and the dataset of the other cell line was used separately to estimate the parameters of the other curve.
This procedure was repeated after shuffling the order of the pairs in order to obtain an estimation of the random correlation between the datasets.
This is because the more stable variance estimation of test datasets was used for Bayesian integration.
The empirical relationship that was obtained for the calibration dataset allowed estimation of the water retention data of the validation dataset (N = 70) with an estimation error (RMSE = 0.042 cm3 cm− 3) lower than that of the traditional approach based on the logistic model.
However, in the current case, the sample size is not considered large enough to (1) provide stable estimation of the weights in the test dataset; and (2) maintain adequate power in the validation dataset to test for the significance of β1.
The study has several limitations owing to the method of cost estimation and the dataset.
In comparison with earlier work on treating incomplete high-throughput datasets (Oba et al., 2003; Scholz et al., 2005), our aim is not to estimate the missing values, but rather to improve the estimation of the model parameters from the incomplete datasets.
This association was further confirmed by the estimation of the haplotype diversity in our dataset, which highlighted the presence of the haplotype bearing the c.-242-110delAGTA c.-242-110delAGTA c.-242-110delAGTA the breast cancer case subset.
The number of probes that are found in the original data, but not in the average permuted dataset is an estimation of the amount of truly aging-associated genes [ 10], therefore from the dataset permutation analysis we can conclude that in the significantly age-regulated datasets the ratio signal to noise is very small.
