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From the remaining data, we only excluded one paired-end file with an exceptional FDU of 0.83 to achieve a final number of 749 datasets for the subsequent composition analysis.
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The main advantage of the feature elimination is to avoid overfitting, which means a perfect fit of the decision function for the training dataset, but a failed fit for the subsequent test datasets.
To improve reliability, we selected conserved blocks from aligned amino acid sequences for each of protein-coding genes using the Gblocks program [ 66] and a concatenated dataset was then prepared for the subsequent phylogenetic analyses.
Because of the loss of phylogenetic signal brought by the inclusion of the outgroup, it was removed from datasets for subsequent phylogenetic analyses, but the global structure previously found was kept.
This could therefore have introduced some ascertainment bias into the dataset, although the subsequent selection process for analysis sought to minimise any further selection bias.
Records that matched in each iteration were removed from the source datasets for subsequent iterations.
After literature search we decided to include the following 13 datasets for subsequent analysis which were divided in a training-group for identification of the Epigenetic-Aging-Signature and a validation-group.
After the exclusion of nematodes, the concatenated amino acid sequence dataset consisting of 1,556 homologous positions was used for the subsequent analyses.
A dataset (Original dataset) of 2646 amino acids with posterior probabilities above five was accepted for the subsequent phylogenetic analysis.
For the subsequent analysis only voxels included in both datasets were considered.
These HIPVs were therefore used as variables for the subsequent PCA to explore the internal differences in the damaged leaf dataset.
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