Additional file 1 of Feature selection of the respiratory microbiota associated with asthma
Creators: name: Salman, Reem
name: Alzaatreh, Ayman
name: Al Bataineh, Mohammad T.
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Chemical Sciences not elsewhere classified
subject: Ecology
subject: Biological Sciences not elsewhere classified
subject: Science Policy

description: Additional file 1: Figure S1. Overview of the ensemble feature selection framework. The dataset is first divided into a training dataset, and a testing dataset. Then, multiple training subsamples are generated by bootstrapping the training dataset. Next, a number of feature selection techniques FS1, . . . , FSt are applied on each subsample, generating a feature importance score ℓj ∈ R for every feature. The aggregation is thus two-fold; Within Aggregation Methodis used for aggregating the importance scores within a single feature selection method and Between Aggregation Methodis used for aggregating the importance scores between different feature selection methods. Once the feature set is sorted from the most to the least important, a rank vector is obtained and used to characterize the most important OTUs. Table S1. Description of data characteristics. Figure S2. Microbial abundance by Asthma Group.Phylum composition was compared among the asthmatic patients and healthy controls using the the 20 most abundant OTUs. In the graph, the relative abundance is expressed as the mean value for each group. At the Phylum level, Proteobacteria was more enriched in subjects with asthma relative to controls.At the Genus level, Moraxella, Alloiococcus, and Staphylococcus most frequently dominate the respiratory samples from asthmatic patients, whereas Prevotella, Porphyromonas, Fusobacterium, and Veillonella dominate the respiratory samples from healthy controls. Figure S3. Diagnostic models based on microbiota selected by a Random Forest model with 250 trees and 50 variables randomly sampled as candidates at each split. The size of terminal nodes was fixed to a minimum of 1.The most important bacterial microbiotaare listed in descending order of relevance by mean decrease in Gini Coefficient. The mean decrease in Gini coefficient is a measure of how each OTU contributes to the homogeneity of the random forest’s nodes and leaves. The greater the mean decrease in Gini coefficient, the more important the feature is for the Random Forest classifier.ROC curves for the Random Forest classifier on which the OTU importance scores were obtained. The model was used to distinguish between asthmatic patients and healthy controls based on the sample-by-taxon abundance matrix. The true positive fraction is the proportion of real positivesthat are correctly classified as positive; the false positive fraction is the proportion of false positivesthat are incorrectly classified as positive. The ROC curve summarizes the true positive and false positive rates and the AUC reflects the classifier’s ability to correctly differentiate between two classes. Table S2: Final selected OTUs. P-values characterize differences in the relative abundances between asthmatic and healthy subjects. Figure S4. Microbial abundance by Asthma and Age Subgroup. Genus composition was compared among the asthmatic pediatrics, asthmatic adults, healthy pediatrics and healthy adults using the 20 most abundant OTUs. The relative abundance is expressed as the mean value for each group. Moraxella is distinctly more abundant in pediatric asthmatics than in any other group, whereas Veillonella, Prevotella, and Neisseria were less enriched on asthmatic pediatrics. Figure S5. Overview of alpha and beta diversity analysis of the respiratory microbiota in asthmatic patients and healthy controls by Age Subgroup.The graph depicts the alpha diversity boxplots for Chao1, ACE, Shannon and Simpson diversity indices distinguishing asthmatic patients and healthy control subjects by Age Subgroup. Plotted are interquartile rangesand lines in the middle of the boxes are medians. Statistical analysis performed with paired Wilcoxon tests reveals significantly lower microbial diversity in asthmatic pediatrics.-The plots depict Principal Coordinates Analysis based on the Bray-Curtis index between the above-mentioned groups in 2D-space. Some clusters of asthmatic pediatrics can be observed on high values of PCoA1. Moreover, adult healthy subjects were generally observed to have negative loadings on each PCoA. Statistical analysis was performed with Analysis of Similarities, and p-values were significant.