Correlations (Pearson correlation coefficients) between fissure integrity and PFT measures.

<p>Correlations (Pearson correlation coefficients) between fissure integrity and PFT measures.</p

Average fissure integrity (unit: %) with standard deviation among sub-groups with different COPD severity.

<p>Average fissure integrity (unit: %) with standard deviation among sub-groups with different COPD severity.</p

Distribution (number/percentage (%)) of the CT examinations in terms of entire lung fissure (ELF) integrity and COPD severity.

<p>Distribution (number/percentage (%)) of the CT examinations in terms of entire lung fissure (ELF) integrity and COPD severity.</p

Distribution of the integrity levels of pulmonary fissures in all COPD patients.

<p>Distribution of the integrity levels of pulmonary fissures in all COPD patients.</p

Distribution (number/percentage (%)) of the CT examinations in terms of right oblique fissure (ROF) integrity and COPD severity.

<p>Distribution (number/percentage (%)) of the CT examinations in terms of right oblique fissure (ROF) integrity and COPD severity.</p

Distribution (number/percentage (%)) of the CT examinations in terms of left oblique fissure (LOF) integrity and COPD severity.

<p>Distribution (number/percentage (%)) of the CT examinations in terms of left oblique fissure (LOF) integrity and COPD severity.</p

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

MiR-199a-5p is commonly dysregulated in three experimental mouse models of liver, lung, and kidney fibrosis.

<p>(A) Venn diagram showing the relationships of miRNA expression changes in lungs from C57BL/6 14 days after Bleomycin treatment (n = 4 mice), livers from BALB/C mice 6 weeks after CCl₄ administration (n = 5 mice) and kidneys from C57BL/6 mice 28 days following unilateral ureteral obstruction (n = 4 mice). The numbers of miRNAs whose expression was differentially detected in each mouse model at p<0.01 are shown. Data on liver fibrosis are from (17). (B) List of the 5 miRNAs dysregulated in the three experimental models of fibrosis. ttest, p<0.01.</p

miR-199a-5p expression during bleomycin induced lung fibrosis.

<p>(A) Heat map representing the statistically significant (adjusted p-value<0.05) differentially expressed microRNAs in lungs from BALB/C and C57BL/6 mice in response to bleomycin at the indicated time points. Up-regulated microRNAs are shown in progressively brighter shades of red, depending on the fold difference, and down-regulated microRNAs are shown in progressively brighter shades of green. miR-199a-5p is marked in red. n = 3 mice in each group. (B) miR-199a-5p expression in lungs from BALB/C and C57BL/6 mice in response to bleomycin at the indicated time points. n = 3 mice in each group. Data from microarrays experiments are expressed as mean of normalized fluorescence intensity ± SEM. **p<0.01 (C) Paraffin sections were prepared from lungs of C57BL/6 mice 14 days following bleomycin intra-tracheal instillation. <i>In situ</i> hybridization and immunohistochemistry assays were performed to determine the colocalization of miR-199a-5p and α-SMA. Results represent one out of three independently performed experiments.</p