Firstly, genes with top-ranked expression variations across samples, which explain most of the total variance potentially contributed by known or unknown factors (for example, the hidden cancer subtypes), were selected as "feature genes" in the initial gene selection as implemented in several previous studies [16,17].
These were around 13% of all pregnancies, and so represent a relatively large proportion of the sample, which explains the relatively large bias.
The degradation products were particularly distinct in the necrotic sample, which explains the difference in the read size distribution between the two libraries.
Because there are very many potential types of deletions and much fewer clonal expansions per sample, only a small proportion of possible types of deletions are found in each sample, which explains why two samples typically have almost no deletions in common.
Table 3 shows the cross-sectional model for the QoL follow-up sample, which explained 55% of the variance in self-rated active ageing (R = 0.550).
Infiltration and high loading rate, however, affect nanoindentation measures by increasing the stiffness of the sample [37,38], which explains our relatively high, but not unreasonable values for mouse bone [36,39].
Left triangle demonstrates that the samples are strongly occupied in Na+ + K+ field rather than Ca2+ + Mg2+ field, while the upper diagram indicates that almost all the samples fall near Cl− type, which explains the simple dissolution and evaporation dominance.
Naturally, the complete data case provides the best results: when η tends to zero, the number of samples in X 0 decreases, which explains the increasing MSE for smaller η values.
HT screens are expensive and laborious which explains the small sample sizes (often smaller than 3 vs 3) found in literature.
