RXR network predicted by IMP using <i>THRB</i> (square node) as a seed from the empirical epistasis network analysis.

<p>Empirical seed was chosen from the top enriched pathway from the epistasis network analysis of the smallpox vaccine GWAS. Variants in <i>RXRA</i> (purple node) have been previously associated with variation in smallpox vaccination response using an epistasis network centrality approach.</p

Positive/negative epistasis degree plot shows the overall epistatic network effect and main effect of the top variants for smallpox vaccine immune response.

<p>For each variant (mapped to gene symbol), the sum of positive interaction coefficients (positive epistasis degree) versus negative epistasis degree is plotted. The diagonal is the line of zero sum of epistasis degree. Plot symbols (size and color) are labeled by their main effect (magnitude and direction of effect on vaccine immune response). The gray box highlights the <i>THBR</i> variant.</p

SNPrank centrality score elbow plot.

<p>The SNPrank scores are plotted for the top 500 variants. The red dashed line represents the null centrality line.</p

Whole Transcriptome Profiling Identifies CD93 and Other Plasma Cell Survival Factor Genes Associated with Measles-Specific Antibody Response after Vaccination

<div><p>Background</p><p>There are insufficient system-wide transcriptomic (or other) data that help explain the observed inter-individual variability in antibody titers after measles vaccination in otherwise healthy individuals.</p><p>Methods</p><p>We performed a transcriptome(mRNA-Seq)-profiling study after <i>in vitro</i> viral stimulation of PBMCs from 30 measles vaccine recipients, selected from a cohort of 764 schoolchildren, based on the highest and lowest antibody titers. We used regression and network biology modeling to define markers associated with neutralizing antibody response.</p><p>Results</p><p>We identified 39 differentially expressed genes that demonstrate significant differences between the high and low antibody responder groups (p-value≤0.0002, q-value≤0.092), including the top gene <i>CD93</i> (p<1.0E^-13, q<1.0E^-09), encoding a receptor required for antigen-driven B-cell differentiation, maintenance of immunoglobulin production and preservation of plasma cells in the bone marrow. Network biology modeling highlighted plasma cell survival (<i>CD93</i>, <i>IL6</i>, <i>CXCL12</i>), chemokine/cytokine activity and cell-cell communication/adhesion/migration as biological processes associated with the observed differential response in the two responder groups.</p><p>Conclusion</p><p>We identified genes and pathways that explain in part, and are associated with, neutralizing antibody titers after measles vaccination. This new knowledge could assist in the identification of biomarkers and predictive signatures of protective immunity that may be useful in the design of new vaccine candidates and in clinical studies.</p></div

Interaction simulation comparison of feature ranking methods for different numbers of null genes.

<p>Comparison of optimized-k Relief-F, standard Relief-F using k = 10 nearest neighbors, edgeR, and tuned Random Forest for detecting an interaction between two genes amongst various numbers of null genes. The panels are sorted in order of increasing negative binomial parameter θ. Each point is the average of the worst ranking gene of the two simulated interacting genes across n = 100 replicate simulations and then divided by the total number of simulated genes. The number of null or background genes increases from 100 to 12,800 total genes, plotted on the log2 scale. Each simulation contains one pure interaction (no main effects) XOR model between two negative binomial genes.</p

ReliefSeq: A Gene-Wise Adaptive-K Nearest-Neighbor Feature Selection Tool for Finding Gene-Gene Interactions and Main Effects in mRNA-Seq Gene Expression Data

<div><p>Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: <a href="http://insilico.utulsa.edu/ReliefSeq.php" target="_blank">http://insilico.utulsa.edu/ReliefSeq.php</a>.</p></div

Genes for which cis-acting methylation sites are highly correlated with expression.

<p>Genes for which cis-acting methylation sites are highly correlated with expression.</p

Genes showing associations in multiple analyses.

<p>Genes showing associations in multiple analyses.</p

Genes whose expression is highly correlated with cis-acting CpGs show functional enrichment.

<p><b>A)</b> Genes with significant association (p < 1E^-4) indicate 32 GO terms enriched at the p < 0.01 level and annotating at least 3 genes, across time points. Color intensity is used to signify statistical significance. Genes are mapped to network biology resources (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0152034#sec002" target="_blank">Methods</a>) and the associations at <b>B)</b> baseline, <b>C)</b> during early and <b>D)</b> late time periods shown, represented in the same location in all panels; (for brevity, only genes within the largest connected components are shown). We color genes in the network that have a significant association at each time period (baseline teal, early green, late orange). The network layout is manually adjusted and edges bundled to improve legibility. See the online version for sufficient resolution to view gene names.</p

Methylation-HAI network based on linear regression models.

<p>Day0 methylation levels of cis-acting CpGs and the change in HAI titer between Day28 and Day0 are used to generate linear regression models. Coloring and display is as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0152034#pone.0152034.g001" target="_blank">Fig 1</a>.</p