HTX: a tool for the exploration and visualization of high-throughput image assays
High-throughput screening (HTS) techniques have enabled large scale image-based studies, but extracting biological insights from the imaging data in an exploratory setting remains a challenge. Existing packages for this task either require expert annotations, which can bias the outcome of the study, or are completely unsupervised, failing to leverage the information present in the assay design. We present HTX, an interactive tool to aid in the exploration of large microscopy data sets by allowing the visualization of entire image-based assays according to visual similarities between the samples in an intuitive and navigable manner. Underlying HTX are a collection of novel algorithmic techniques for deep texture descriptor learning, 2D data visualization, adversarial suppression of batch effects, and backprop-based image saliency estimation.\n\nWe demonstrate that HTX can exploit the screen meta-data in order to learn screen-specific image descriptors, which are then used to quantify the visual similarity between samples in the assay. Given these similarities and the different visualization resources of HTX, it is shown that screens of small-molecule libraries on cell data can be easily explored, reproducing the results of previous studies where highly-specific domain knowledge was required.