Table 1 Accuracy of age prediction from fibroblast transcriptomes, for various algorithms on two datasets. Cross-validation age prediction metrics are reported for our dataset of 133 individuals between 1 and 94 years old and for dataset E-MTAB-3037 with 22 individuals from newborn to 89 years old. Metrics: mean absolute error (MAE), median absolute error (MED), and R2 goodness-of-fit for the line of best fit. Parameters shown for regression algorithms are the best ones found for reducing MAE from a grid search of the parameter space. LDA ensemble with 20-year bins (in italics) achieves a lower MAE and MED and a higher R2 than competing methods. Other window sizes (15, 25, 35) did not improve performance above that of the 20-year bin size
From: Predicting age from the transcriptome of human dermal fibroblasts
Algorithm | Parameters | Mean absolute error | Median absolute error | R 2 | |
|---|---|---|---|---|---|
Our dataset (133 individuals) | |||||
LDA ensemble | Age bin width = 10 | 9.5 | 4.0 | 0.68 | |
Age bin width = 20 | 7.7 | 4.0 | 0.81 | ||
Age bin width = 30 | 8.2 | 4.0 | 0.77 | ||
Gaussian naive Bayes ensemble | Age bin width = 10 | Uninformative priors | 16.5 | 7.0 | 0.20 |
Age bin width = 20 | 16.0 | 8.0 | 0.27 | ||
Age bin width = 30 | 15.7 | 7.0 | 0.30 | ||
k-nearest neighbors ensemble | Age bin width = 10 | Euclidean distance metric k = 5 | 22.3 | 14.0 | − 0.19 |
Age bin width = 20 | 19.7 | 11.0 | 0.04 | ||
Age bin width = 30 | 19.7 | 14.0 | 0.09 | ||
Random forest ensemble | Age bin width = 10 | n_trees = 100, min_impurity_split =2 | 14.2 | 5.0 | 0.38 |
Age bin width = 20 | 11.8 | 5.0 | 0.57 | ||
Age bin width = 30 | 11.8 | 5.0 | 0.55 | ||
Linear regression | N/A | 12.1 | 10.0 | 0.73 | |
Elastic net regression | Alpha = 0.1 L1/L2 ratio = 0.0 | 12.0 | 11.0 | 0.73 | |
Support vector regression | Kernel = second order polynomial C = 10, epsilon = 0.05 gamma = 0.0002 | 11.9 | 10.2 | 0.72 | |
E-MTAB-3037 (22 individuals) | |||||
LDA ensemble | Age bin width = 20 | 18.1 | 14.5 | 0.20 | |
Gaussian naive Bayes ensemble | Age bin width = 20, uninformative prior | 36.4 | 39.5 | − 1.47 | |
k-nearest neighbors ensemble | Age bin width = 20, Euclidean distance metric k = 5 | 34.9 | 36 | − 1.25 | |
Random forest ensemble | Age bin width = 20, n_trees = 100, min_impurity_split =2 | 31.9 | 28 | − 0.82 | |
Linear regression | N/A | 23.5 | 18.8 | 0.04 | |
Elastic net regression | Alpha = 1.0 L1/L2 ratio = 0.6 | 20.0 | 18.8 | 0.36 | |
Support vector regression | Kernel = second order polynomial C = 1, epsilon = 0.05 gamma = 0.0002 | 19.7 | 15.4 | 0.31 | |