Additional file 1 of Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis
Creators: name: Buschur, Kristina L.
name: Riley, Craig
name: Saferali, Aabida
name: Castaldi, Peter
name: Zhang, Grace
name: Aguet, Francois
name: Ardlie, Kristin G.
name: Durda, Peter
name: Craig Johnson, W.
name: Kasela, Silva
name: Liu, Yongmei
name: Manichaikul, Ani
name: Rich, Stephen S.
name: Rotter, Jerome I.
name: Smith, Josh
name: Taylor, Kent D.
name: Tracy, Russell P.
name: Lappalainen, Tuuli
name: Graham Barr, R.
name: Sciurba, Frank
name: Hersh, Craig P.
name: Benos, Panayiotis V.
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Figure S1. PCA of COPDGene primary analysis RNA-seq data colored according to batch (A) before and (B) after batch correction. Batch detection with guided principal component analysis showed strong batch effects before batch correction (p &lt; 0.001) that were removed after batch correction (p = 0.538). Figure S2. PCA of COPDGene validation RNA-seq dataset colored according to batch (A) before and (B) after batch correction. Batch detection with guided principal component analysis showed strong batch effects before batch correction (p = 0.001) that were removed after batch correction (p = 0.937). Figure S3. PCA of MESA validation RNA-seq dataset colored according to batch (A) before and (B) after batch correction. Batch detection with guided principal component analysis showed strong batch effects before batch correction (p = 0.003) that were removed after batch correction (p = 1). Figure S4. Clustering tree illustrates the stability of clusters over a range of values for clustering resolution (res). We chose 4 as optimal number of clusters, because cluster number and content (samples) remains constant for res=0.6 to 0.9. When res&gt;0.9 produced some subclusters of these four, but samples did not move across the four branches extending from these clusters. Figure S5. PC elbow plot of the COPDGene discovery set ssNPA features. We heuristically chose 6 principal components for clustering because they captured a large percentage of the variance in the data. Figure S6. Clustering tree illustrates the stability of clusters over a range of values for k in the kNN classification for the COPDGene validation analysis. We chose k=3 because the clusters were stable by this value. Figure S7. Clustering tree illustrates the stability of clusters over a range of values for k in the kNN classification for the MESA validation analysis. We chose k=3 because the clusters were stable by this value. Figure S8. Participant GOLD stage composition according to cluster. The reference group was composed of only GOLD 0 participants by design. Figure S9. Clustering based on FEV1 percent predicted does not sufficiently separate COPD individuals with different mortalities. Figure S10. COPDGene validation samples were projected into the same PCA space as the discovery analysis and assigned to clusters with kNN. Density clouds show the distribution of samples in each cluster from the discovery analysis. Individual points represent validation COPDGene samples and are colored according to the cluster to which they were assigned. Figure S11. Heatmaps display the p-value bins for the inter-cohort pairwise comparisons of cluster means by Wilcoxon test for: physiology (A) FEV1 percent predicted, (B) FEV1/FVC, (C) DLCO, (D) FRC/DLC ratio, (E) distance walked in 6 minutes; symptoms (F) SGRQ total score, (G) MMRC dyspnea score, and (H) CAT score. The upper right triangle shows the pairwise comparisons between cluster in the COPDGene discovery set, and the lower left triangle shows the comparisons between clusters in the COPDGene validation set. Blue (red) arrows indicate concordance in the significance (non-significance) of the comparisons between the two cohorts. Figure S12. MESA validation samples were projected into the same PCA space as the discovery analysis and assigned to clusters with kNN. Density clouds show the distribution of samples in each cluster from the discovery analysis. Individual points represent validation MESA samples and are colored according to the cluster to which they were assigned. Figure S13. Heatmaps display the p-value bins for the pairwise comparisons of cluster means by Wilcoxon test for (A) FEV1 percent predicted, (B) FEV1/FVC, (C) FEF 25-75%, and (D) percent emphysema. The upper right triangle shows the pairwise comparisons between cluster in the COPDGene discovery set, and the lower left triangle shows the comparisons between clusters in the COPDGene validation set. Blue (red) arrows indicate concordance in the significance (non-significance) of the comparisons between the two cohorts. Figure S14. ssNPA feature values show a difference in the degree of deregulation of (A) MUC16, (B) ZMAT4, (C), GSTM1, (D) CTNNA2, (E) MRGPRE, (F) SLC44A5, (G) ADARB2, and (H) ADAM29 across clusters. Wilcoxon test p-values highlight where there are differences in the distributions between clusters 0 and 1 and between clusters 2 and 3. Table S3. Analysis of various COPDGene comorbidities did not show any significant difference between the four identified subtypes. All comorbidities recorded at the time blood samples were collected. p-val: chi-square p-value. Table S4. The genes with the top 5 loadings for each of the first 6 PCs used for clustering the COPD samples in the training COPDGene dataset. Genes are sorted by decreasing contribution to the clustering (sum of the absolute values of the loadings across the first 6 PCs). Loading value is not provided if gene did not rank among the top 5 loadings for a given PC. The sample clustering is driven by differences in the regulation of these genes.