Table 1: Selected visualization methodologies in HCS.

Method and solutionDescriptionReference

Visual data exploration (VDE)The basic idea of VDE is to present the data in some visual forms that allow users to gain insight into the data and generate hypotheses by directly interacting with the data. The advantage of VDE is that users are directly involved in the data mining process to combine the flexibility, creativity, and general knowledge of the human with the enormous storage capacity and the computational power of computers. This process is especially useful when little is known about the data and the exploration goals are vague, such as in analyzing a huge number of RNAi-HCS images. However, access to plots, data tables, and image viewer in one view frame available with one click are not available. [19]

Cellomics Discovery ToolBox and visualization methodThe methods focus on visualizing simple quantitative readouts of markers instead of images and especially the relationships among images that convey profound information closely related to effects of chemical compounds, gene functions, and biological processes. and [20]

PhotoFinder and Personal Photo LibrariesThose methods focus on image database visualization targeted at personal photo albums, which are much smaller than HCS image databases and did not consider computational needs specific to HCS image analyses.[21, 22]

ImCellPhen—interactive mining of cellular phenotypesThis is a method and a tool for interactive mining of cellular phenotypes which provides intelligent interfaces for visualizing large-scale RNAi-HCS image databases and interactive mining of cellular phenotypes. However, this method does not provide easy-to-use (with one click access) filtering functionality for image properties and image processing results. [19]

The Open Microscopy Environment (OME)OME provides an open-source browser to navigate HCS image databases that are described as a quasi-hierarchical structure representing the relationship between projects and datasets. However, this navigation scheme was not designed to facilitate discovery of screening hits among all available parameters and categories of data.[23]

Advanced Cell ClassifierAdvanced Cell Classifier (ACC) is a data analyzer method program to evaluate cell-based high-content screens. The basic aim is to provide a very accurate analysis with minimal user interaction using advanced machine learning methods and visual learning of image data sets. However, ACC do not provide full interactivity, and at same time, filtering options for 3 data sources on same view: library, images, and image processing results.[24]