Digital pathology, such as quantification of tissue microarrays and fluorescent images, is becoming increasingly important. Pixel-based measurements generate a tremendous amount of data and insight similar to omics data. However, there are very limited tools that facilitate complex computational bioimage analysis, especially for non-expert users. To address this need, we developed hourglass, an open-source R package and an accompanying cross-platform desktop application using the kivy design language. hourglass streamlines post-processing and visualization of multiparametric numeric data. We developed and compiled a number of individual data filtering functions (such as subset retrieval, imputation and outlier removal) and plotting functions into a package. Resembling the shape of an hourglass, the software takes in large data files, specifically 1) numeric matrix file, 2) row annotations (including clinical and sample descriptions), and 3) column annotations (including feature and parameter descriptions). Similar to the structure of other dataset objects, this generic input allows hourglass to be adopted by omics data analysis as well. Next, it systematically decomposes data into phenotype groups and features of interest. Finally, it supplies a variety of plots in a structured, coherent file hierarchy. Plot types include overview and individual box plots for each feature, heat maps, correlation plots, profiles of feature makeup by patient/sample, and corresponding statistics. Paired and survival analyses are also supported. Finally, we demonstrate the application of hourglass for interrogating new and known functions of the cytokine Interleukin-6 in pancreatic ductal adenocarcinoma (PDAC). In conclusion, bioimage analysis data can be integrated with clinical and sample annotations to provide a flexible and powerful combination of options. This multipurpose streamlined approach may serve as both a screening and exploratory tool when delving into topics such as cancer biology. 

Keywords: bioimage analysis, bioinformatics, data visualization