Fig 1.
Estimation error of candidate methods with a threshold
of 0.1.
The estimation error is evaluated under different heterogeneity levels (h = 10 or 20), different numbers of transferable source datasets (), and different distributions of the error (ε(s) follows normal distribution (N), t-distribution (t), contaminated normal distribution (CN), and skew t-distribution (St)).
Fig 2.
Relative prediction error of candidate methods relative to the PNLR with a threshold of 0.1.
The relative prediction error is evaluated under different heterogeneity levels (h = 10 or 20), different numbers of transferable source datasets (), and different distributions of the error (ε(s) follows normal distribution (N), t-distribution (t), contaminated normal distribution (CN), and skew t-distribution (St)). We randomly split the target dataset into five folds, using four folds to train and the remaining fold to calculate the mean prediction error
with
.
Table 1.
Precision and recall (in parentheses) of candidate methods under different heterogeneity levels (h = 10 or 20), different numbers of transferable source datasets (), and different distributions of the error (ε(s) follows normal distribution (N), t-distribution (t), contaminated normal distribution (CN), and skew t-distribution (St)).
Fig 3.
Distribution of error by normal linear regression for six tissues compared with normal distribution.
Fig 4.
Relative prediction error of PtLR, Naive Trans-PNLR, Naive Trans-PtLR, Trans-PNLR, and Trans-PtLR relative to the PNLR.
We randomly split the data in target tissues into five folds, using four folds to train and the remaining fold to calculate the mean prediction error with
.