The stages presented so far transform samples, i.e., files, into feature vectors suitable as input for machine learning algorithms.
The simulation of Y is realized, applying the inverse transform sampling of the marginal distributions of Y that can be found in (Tian et al. 2009).
The algorithm combines the conveniences of deconvolution in Laplace domain with a new approach to transform sampled data from time domain to Laplace domain, which removes the necessity to extrapolate the data beyond the sampling interval imposed by Laplace transformation.
DOI: http://dx.doi.org/10.7554/eLife.06088.013 Discrete seeding points that are randomly positioned along the oocyte membrane according to the probability density p Σ (s i | h 0 i, k i ) (Equation 8) can be achieved by inverse transform sampling.
Apply ICP alignment between S ref, S i, then transform S refby the rigid transformation of ICP, the transformed sample is denoted by S i ′ ; 3.
The cut-off θ is estimated by applying the Normal method in the transformed sample following the application of the inverse transformation function.
The blue curve in Figure S6, Supporting Information, corresponds to the fully transformed sample.
All the heat-maps were made using GC-RMA log transformed sample averages.
*Interpreted as relative increase in proportion of matching eligibility criteria per each one unit increase in log transformed sample size.
The time-domain transformed samples are indexed by κ.
