Additional file 1: of Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model

Table S1. Correlations between bacteria and laboratory measurements in OC-COPD. Table displays the Spearman correlations between all bacteria-laboratory measurement pairs. P values were adjusted (AdjustedP) using the Bonferroni correction. The last column (NwithGenus) is a count of the number of samples that contained the genus in that correlation-pair. (XLSX 173 kb

Additional file 3: of Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model

Table S2. Correlations between bacteria or fungi and cytokines in LHMP. Table displays the Spearman correlations between all bacteria/fungi-cytokine pairs. P values were adjusted (AdjustedP) using the Bonferroni correction. The last column (NwithGenus) is a count of the number of samples that contained the genus in that correlation-pair. (XLSX 161 kb

Additional file 2: of Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model

Figure S1. Additional OC-COPD associations between laboratory measurements and bacteria identified by LassoGLMM. Strong associations between bacteria and (a) percent neutrophils (O1), (b) partial pressure of oxygen PO2(O4) (c) SAT (O5), (d) alkaline phosphatase (O6), and (e) glucose (O10). Each horizontal grey line represents an individual. When a colored circle is located on the grey line, it is the relative abundance of that microbe for that subject. Perfect positive association between clinical variable and bacteria would form a line from the bottom-left to the top-right of the graph and would have a highly positive β coefficient in the LassoGLMM. Perfect negative association would form a line from the top-left to the bottom-right of the graph and would have a highly negative β coefficient. (PDF 287 kb

Additional file 1: of Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes

Text S1. Materials and data collection. Text S2. Details of smoothing and Feature Topology Plots (FTP). Text S3. Simulation setting to evaluate iPF. Text S4. Comprehensive validation scheme for iPF. Figure S5. (A) An illustration of integrated omics data sets, (B) A workflow to generate future topology plot (FTP). Figure S6. Flowchart of validation scheme for Integrative phenotyping framework for multiple omics data sets. Figure S7. An example of iPF that utilizes fused multiple data sets at the stage (vi). Figure S8. Examples of iPF using various combinations of the omics data sets (pooled analysis). Figure S9A. The gap statistics and its scree plot to choose the optimal number of clustering (clinical and miRNA data). Figure S9B. The gap statistics and its scree plot to choose the optimal number of clustering (mRNA and miRNA data). Figure S9C. The gap statistics and its scree plot to choose the optimal number of clustering (mRNA and clincal data). Figure S9D. The gap statistics and its scree plot to choose the optimal number of clustering (clincal data and combined data of mRNA and miRNA). Figure S10. The best choice of the number of feature modules. Figure S11. Simulation study shows robust true feature discovery in “Feature Fusion”. The x-axis represents multiplication levels of noise features. The y-axis represents average ARIs from 100 simulations. Each figure is generated based on simulation scenarios of the different number of true features (e.g., 200, 400, and 600, respectively). Figure S12. Immunomodulating drugs target overexpressed genes in module two. Table S13. The description of mRNA and miRNA lung disease data. Table S14. Various correlation types depending on variable attributes. Table S15. The demographic summary of clinical features in each sub-cluster. Table S16. Target gene enrichment analysis (via Fisher exact test) related to twelve. Table S17. Regression analysis on target miRNA features, and coefficient of determination significant miRNA features. Table S18. The top disease or functional annotations associated with genes in module two in Cluster E patients. Figure S19. Basic consensus clustering using only gene expression data. (DOCX 6398 kb

Additional file 3: of Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes

This file includes 669 clinical variables analyzed in this paper. (XLSX 45 kb

Pulmonary function measures and COPD classification of subjects classified as having lower and upper lung dominant emphysema, stratified by emphysema heterogeneity and disease severity.

A<p>Summarized by frequency (%);</p>B<p>Summarized by median (IQR).</p><p>Pulmonary function measures and COPD classification of subjects classified as having lower and upper lung dominant emphysema, stratified by emphysema heterogeneity and disease severity.</p

Multiple linear regression model for pulmonary function measures among subjects without COPD (n = 190).

<p>The cells represent the regression coefficients and estimated 95% confidence intervals (in parentheses).</p><p>Multiple linear regression model for pulmonary function measures among subjects without COPD (n = 190).</p

Distributions of computed HI% (left) and differences in computed HI% between the left and the right lungs (right).

<p>The top row represents the COPD patients (N = 160), and the bottom row represents the non-COPD patients (N = 190).</p

Scatter plots of pulmonary function measures and emphysema heterogeneity (HI%) for COPD patients.

<p>The grey line represents the estimated pulmonary function as a function of HI%. p1 denotes the p-value of HI%⁻ = min(0%, HI%) in the piecewise linear regression, and p2 corresponds to the p-value of HI%⁺ = max (0%, HI%).</p

Pulmonary function measures and COPD classification of subjects classified as having lower and upper lung dominant emphysema, stratified by emphysema heterogeneity and disease severity.

A<p>Summarized by frequency (%);</p>B<p>Summarized by median (IQR).</p><p>Pulmonary function measures and COPD classification of subjects classified as having lower and upper lung dominant emphysema, stratified by emphysema heterogeneity and disease severity.</p