Integration of Transcriptomic Data Identifies Global and Cell-Specific Asthma-Related Gene Expression Signatures

Abstract

Over 140,000 transcriptomic studies performed in healthy and diseased cell and tissue types, at baseline and after exposure to various agents, are available in public repositories. Integrating results of transcriptomic datasets has been an attractive approach to identify gene expression signatures that are more robust than those obtained for individual datasets, especially datasets with small sample size. We developed Reproducible Analysis and Validation of Expression Data (RAVED), a pipeline that facilitates the creation of R Markdown reports detailing reproducible analysis of publicly available transcriptomic data, and used it to analyze asthma and glucocorticoid response microarray and RNA-Seq datasets. Subsequently, we used three approaches to integrate summary statistics of these studies and identify cell/tissue-specific and global asthma and glucocorticoid-induced gene expression changes. Transcriptomic integration methods were incorporated into an online app called REALGAR, where end-users can specify datasets to integrate and quickly obtain results that may facilitate design of experimental studies.

Figure 1.

Steps followed by RAVED to analyze microarray and RNA-Seq data are provided on the left. Specific programs and R packages (in bold) used for each step are shown on the right.

Figure 2.

Overview of the integration analysis. 25 transcriptomic asthma and glucocorticoid datasets, consisting of various asthma endotypes and cell/tissue types, were integrated using three summary statistic-based methods.

Figure 3.

Sample of RAVED quality control outputs. Boxplots of relative log expression (RLE) and normalized unscaled standard error (NUSE) (left), MA plots (top right) and spatial plots (bottom right) are generated during the microarray data quality control procedure.

Figure 4.

Comparison of genes with significant expression changes across cell/tissue types for asthma studies (left) and glucocorticoid response studies (center), and in asthma vs. glucocorticoid response datasets across all cell/tissue types (right). The total number of genes available for each comparison is shown on the bottom right of the corresponding diagram.

Figure 5.

Integration results for NSFL1C. A) Q-values corresponding to integration of all cell/tissue types, blood cells, and structural cells for asthma and glucocorticoid (GC) datasets obtained via three methods. Dashed line indicates q-value=0.05. B) Overall fold changes from effect size-based integration (circles and triangles) and fold changes from individual studies (dots). Bars represent 95% confidence intervals. Dashed line indicates fold change=1.

Figure 6.

Representative distribution of effect sizes from an individual microarray study GSE65401 (left) and an RNA-Seq study SRP033351 (right).