In this matrix, we expect that the microarrays represented by the columns are independent and that some of the rows, representing the genes will be correlated, indicating biological relationships.
However, because only 17.5% of the cells in the combined matrices were missing, we expect the difference in imputation quality to have only minor effects on the results.
The method of choice was PCA based on the covariance matrix since we expected PIs with higher variance to carry more information than PIs with less variance.
Because blade tissue was not damaged by matrix ablation we expected that most parameters characterizing the hinge and blade would be identical to those in unperturbed wings.
Although this never happens, it shows that when G is a good-enough approximation of the identity matrix, we can expect satisfying results.
For instance, in the 2-back task where items from both of the two previous time steps we expect that matrix W1,2 should provide complete disambiguation.
However, in this setting, X is a sparse matrix of binary pathways where we expect and are explicitly looking for dominant pathways.
We expect this matrix to have large elements on the diagonal encouraging self-transitions [which we model with a Dirichlet prior with parameters δ A (see Fig. 1 and Table 1)], and thus runs of repeated states.
In particular, we expect the G-matrix to be sparse, by which we mean that we favor G-matrices that are modular and low rank.
At intermediate levels of species richness we expect the forest matrix (contributing most to species richness) to be still intact.
