Trees

Metascape utilizes the well-adopted hypergeometric test⁵⁸ and Benjamini-Hochberg p-value correction algorithm^{59 (link)} to identify all ontology terms that contain a statistically greater number of genes in common with an input list than expected by chance. By default, Metascape pathway enrichment analysis makes use of Gene Ontology^{16 (link)}, KEGG^{17 (link)}, Reactome^{18 (link)}, MSigDB^{19 (link)}, etc. Distinguishing it from many existing portals, Metascape automatically clusters enriched terms into non-redundant groups, where it implements similar logic as found in DAVID^{6 (link)}. Briefly, pairwise similarities between any two enriched terms are computed based on a Kappa-test score^{28 (link)}. The similarity matrix is then hierarchically clustered and a 0.3 similarity threshold is applied to trim the resultant tree into separate clusters. Metascape chooses the most significant (lowest p-value) term within each cluster (Supplementary Data 4) to represent the cluster in bar graph and heatmap representations. The analysis provides other popular enrichment metrics in addition to p-values.

We attempted the classification of dementia status in 146 samples after removing missing values from the 177 that were used in the feature selection process. The 146 samples had a slight class imbalance, with 89 demented versus 57 non-demented patients. Before training our models, we randomly selected 57 patients from the demented group using the sample() function from the random module in Python3. Then, the rows were shuffled using sklearn.utils version 0.22.2.post1. As a result, 114 samples were utilized after balancing the class label. The 32 samples were held out for final assessment. The hippocampal tau stage feature, which had 50% missing values, was dropped during the training process. Age and brain weight were removed before training the models, ending up with 22 features and 114 samples for classification. The dataset was split into a training set of 70% (80 samples) and a testing set of 30% (34 samples).
Seven classification algorithms were trained to classify individuals’ dementia status from the 22 top-ranked features. Scikit-learn version 0.22.2.post1 was used to implement and train the ML classifiers, and then measure their classification performance. Logistic regression was implemented using the sklearn.linear_model package where penalty was set to 12, the regularization parameter C was set to 1, the maximum number of iterations taken for the solvers to converge was set to 2000, and other parameters were set to default values. A decision tree classifier was implemented using the sklearn.tree package. K-nearest neighbors classifier was implemented using the sklearn.neighbors with the number of neighbors set to 5, the function “uniform weights” used for prediction, the “Minkowski” distance metric utilized for the tree, and with other parameters were set to default values. The linear discriminant analysis classifier was implemented using the sklearn.discriminant_analysis package with singular value decomposition for solver hyperparameter and other parameters were set to default values. The Gaussian naïve Bayes classifier was implemented using sklearn.naive_bayes. The support vector machine with a radial basis function kernel (SVM-RBF) was implemented using sklearn.svm with the regularization parameter C set to 1, the kernel coefficient gamma = “scale” and other parameters were set to default values. The support vector machine with a linear kernel (SVM-LINEAR) was implemented using the sklearn.svm package with regularization parameter C set to 1, with a “linear” kernel, gamma coefficient “scale” and other parameters were set to default. The sklearn.metrics package was used to report classification performance. Training and performance evaluation were performed 500 times, from which the average performance measure was calculated as overall performance. Accuracy, balanced accuracy, F1-score, precision, sensitivity and specificity utilizing regression plots were measures used for performance. ML models and feature selection libraries were built using Python 3.7.3.

Phylogenetic analyses were conducted using 80 protein-coding genes and 52 complete chloroplast genomes (after removing one inverted repeat). In total 38 Artemisia species from four subgenera and 10 sections, including 17 subg. Seriphidium species from three sections, were used for phylogenetic analysis (Fig. 4). Ajania pacifica (Accessions NC_050690 and MN883841) was used as the outgroup. Genome alignment was performed by MAFFT v. 7 [77 (link)] and trimmed using the “-gappyout” setting in trimAI v. 1.2, a PhyloSuite [84 (link)] plugin. According to the Bayesian information criterion (BIC), the most appropriate substitution models, estimated using jModelTest2 [85 (link)], were TVM + I + G for the complete chloroplast genome sequences and the protein-coding genes. Maximum likelihood (ML) analyses were conducted using RaxML-HPC v.8 [86 (link)], with 1000 bootstrap iterations. Based on the eight hypervariable regions screened and their tandem sequences, using ML method to reconstruct phylogenetic tree respectively in accordance with the above method. Only first the eight hypervariable regions screened were manually extracted and concatenated from the whole chloroplast genomes of 17 subg. Seriphidium species (16 newly sequenced and one published) by Geneious v. 9.1.7 [75 (link)]. Bayesian inference (BI) analysis was carried out using MrBayes v.3.2 [87 (link)], with Markov chain Monte Carlo simulations algorithm (MCMC) for 2,000,000,000 generations, using four incrementally-heated chains. This was conducted on the CIPRES Science Gateway portal [88 ]. The final trees were visualized and edited using FigTree v. 1.4.2 [89 ].