Additional file 1 of Incorporating biological information in sparse principal component analysis with application to genomic data

Figure S1. Network structure of simulated data : Randomly specified graph ( G $\mathcal {G}$ ). Figure S2. Correlation of gene pairs by relationship types. Figure S3. BIC value by tuning parameter with GBM microarray data. X-axis is tuning parameter, y-axis is BIC value. Figure S4. Loading plots of the first two PCs by Fused and Grouped sPCA. Colored points are genes enriched in Glioblastoma related pathways found by the proposed methods but not found by existing methods. Table S1. Simulation results of Setting 1 when γequals 8. Table S2. Simulation results of Setting 2 when γ equals 8. Table S3. ν value used in the simulation settings. Table S4. Simulation results of Setting 1 when extra noise edges are added to structural information. Table S5. Simulation results of Setting 2 when extra noise edges are added to structural information. Table S6. Prediction accuracy using the PCs of PCA-based methods. ·(·) represents mean(sd). (PDF 1270 kb

Additional file 1 of Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment

Additional file 1: Fig. S1.Progression Curves. For the 11 cognitive and biomarker measurements, while we only usedthe baseline measure in our cluster and survival analysis, we also plotted their progression curve using the longitudinal measures for the subtypes and the original MCI groups. The line plots are generated by aggregatingparticipants in the same subtype, where the line is the mean at a given time point and the shading is the 95%confidence interval

Additional file 2 of Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment

Additional file 2: Table S1.Results for genetic association analysis in Fig. 9 sorted by chromosome number.Thresholding at p= 0.05 with FDR correction, only SNPs that are significant in at least one case control associationtest are included and only p-values for significant results are recorded. Mapped gene(s) for each SNP are shown asthey are recorded in GWAS Catalog - SNPs in multiple genes are separated by comma, interactions are separated by”x”, and upstream anddownstream genes are separated by a hyphen for intergenic SNPs

Comparative transcriptomes of adenocarcinomas and squamous cell carcinomas reveal molecular similarities that span classical anatomic boundaries

<div><p>Advances in genomics in recent years have provided key insights into defining cancer subtypes “within-a-tissue”—that is, respecting traditional anatomically driven divisions of medicine. However, there remains a dearth of data regarding molecular profiles that are shared across tissues, an understanding of which could lead to the development of highly versatile, broadly applicable therapies. Using data acquired from The Cancer Genome Atlas (TCGA), we performed a transcriptomics-centered analysis on 1494 patient samples, comparing the two major histological subtypes of solid tumors (adenocarcinomas and squamous cell carcinomas) across organs, with a focus on tissues in which both subtypes arise: esophagus, lung, and uterine cervix. Via principal component and hierarchical clustering analysis, we discovered that histology-driven differences accounted for a greater degree of inherent molecular variation in the tumors than did tissue of origin. We then analyzed differential gene expression, DNA methylation, and non-coding RNA expression between adenocarcinomas and squamous cell carcinomas and found 1733 genes, 346 CpG sites, and 42 microRNAs in common between organ sites, indicating specific adenocarcinoma-associated and squamous cell carcinoma-associated molecular patterns that were conserved across tissues. We then identified specific pathways that may be critical to the development of adenocarcinomas and squamous cell carcinomas, including Liver X receptor activation, which was upregulated in adenocarcinomas but downregulated in squamous cell carcinomas, possibly indicating important differences in cancer cell metabolism between these two histological subtypes of cancer. In addition, we highlighted genes that may be common drivers of adenocarcinomas specifically, such as <i>IGF2BP1</i>, which suggests a possible link between embryonic development and tumor subtype. Altogether, we demonstrate the need to consider biological similarities that transcend anatomical boundaries to inform the development of novel therapeutic strategies. All data sets from our analysis are available as a resource for further investigation.</p></div

Overall DNA methylation patterns defined by histology are consistent across esophagus and lung.

<p>(A) Heatmap depicting DNA methylation of differentially methylated CpG sites between EAC and ESCC in ADCs and SCCs of esophagus and lung, with hierarchical clustering. (B) Heatmap depicting DNA methylation of differentially methylated CpG sites between LUAD and LUSC in ADCs and SCCs of esophagus and lung, with hierarchical clustering.</p

Global miRNA expression patterns defined by histology are consistent across both esophagus and lung.

<p>(A) Heatmap depicting miRNA expression of differentially expressed miRNAs between EAC and ESCC in ADCs and SCCs of esophagus and lung, with hierarchical clustering. (B) Heatmap depicting miRNA expression of differentially expressed miRNAs between LUAD and LUSC in ADCs and SCCs of esophagus and lung, with hierarchical clustering.</p

Global molecular patterns defined by histology are consistent across both esophagus and lung.

<p>(A) Heatmap depicting mRNA expression of DEGs between EAC and ESCC in ADCs and SCCs of esophagus and lung, with hierarchical clustering. (B) Heatmap depicting mRNA expression of DEGs between LUAD and LUSC in ADCs and SCCs of esophagus and lung, with hierarchical clustering.</p

Genes correlated with survival in ADCs.

<p>(A) Genes whose expression is correlated with worse survival in ADCs. (B) Genes whose expression is correlated with better survival in ADCs. (C) Kaplan-Meier curve depicting survival in high <i>IGF2BP1</i> and low <i>IGF2BP1</i> expression groups in EAC (log-rank test). (D) Kaplan-Meier curve depicting survival in high <i>IGF2BP1</i> and low <i>IGF2BP1</i> expression groups in LUAD (log-rank test). (E) Kaplan-Meier curve depicting survival in high <i>IGF2BP1</i> and low <i>IGF2BP1</i> expression groups in pooled ADCs, excluding EAC and LUAD (log-rank test). (F) Kaplan-Meier curve depicting survival in high <i>IGF2BP1</i> and low <i>IGF2BP1</i> expression groups in pooled SCCs (including ESCC and LUSC) (log-rank test).</p

Histology explains a greater degree of gene expression variation than organ site.

<p>(A) Principal Component Analysis (PCA) plot depicting two largest components of variance in gene expression in four cancer types. (B) Elbow plot showing the proportion of variance explained by the first 20 principal components (PC1 explains 0.34 or 34% of total variance). (C) Bar chart depicting relative importance (percent variance explained) by histology and organ site, respectively, based on a multiple linear regression model with PC1 as the response variable. Regression model created was [PC1 = 47.2 × Histo + 21.66 × Organ − 42.85], where explanatory variables were demarcated as Histo (0 = SCC, 1 = ADC) and Organ (0 = Esophagus, 1 = Lung). (D) Heatmap of differentially expressed genes between esophageal cancers and lung cancers, with hierarchical clustering. (E) Bar chart depicting relative importance (percent variance explained) by histology and organ site, respectively, based on a multiple linear regression model with Cluster number (1 or 2) as the response variable. Regression model created was [Cluster = 0.80 × Histo + 0.12 × Organ + 1.01], where explanatory variables were demarcated as Histo (0 = ADC, 1 = SCC) and Organ (0 = Lung, 1 = Esophagus).</p

Validation in endocervical adenocarcinoma and cervical squamous cell carcinoma.

<p>(A) Heatmap depicting mRNA expression of common DEGs between ADC and SCC (esophagus and lung) in the cervix, with hierarchical clustering. (B) Heatmap depicting DNA methylation of common differentially methylated CpG sites between ADC and SCC (esophagus and lung) in the cervix, with hierarchical clustering. (C) Heatmap depicting miRNA expression of common differentially expressed miRNAs between ADC and SCC (esophagus and lung) in the cervix, with hierarchical clustering. (D) Pathways identified using DEGs from ECA versus CESC, compared to results from the esophagus and lung. (E) Upstream regulators identified using DEGs from ECA versus CESC, compared to results from the esophagus and lung.</p