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.