qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data

Abstract

High throughput screening (HTS) is widely used in drug discovery and chemical biology to identify and characterize agents having pharmacologic properties often by evaluation of large chemical libraries. Standard HTS data can be simply plotted as an x-y graph usually represented as % activity of a compound tested at a single concentration vs compound ID, whereas quantitative HTS (qHTS) data incorporates a third axis represented by concentration. By virtue of the additional data points arising from the compound titration and the incorporation of logistic fit parameters that define the concentration-response curve, such as EC50 and Hill slope, qHTS data has been challenging to display on a single graph. Here we provide a flexible solution to the rapid plotting of complete qHTS data sets to produce a 3-axis plot we call qHTS Waterfall Plots. The software described here can be generally applied to any 3-axis dataset and is available as both an R package and an R shiny application.

Keywords: 3-axis plots; Concentration–response; Dose–response; Efficacy; Pharmacology; Quantitative high-throughput screening; qHTS Waterfall plots.

Fig. 1

Various outputs for 3D visualization algorithm. A–C Multiple graph options obtained using a single readout dataset covering 5191 compounds. A Active compounds are displayed using data points and the corresponding concentration response curve (CRC) fit, while inactive compound data responses are plotted as gray dots only. Compounds are randomly ordered in this representation. B Data and CRCs are grouped according to qHTS curve classification (CC) criteria which take into consideration the nature of the pharmacological response as described in ref. [1]. Inactive responses are not shown. For A and B, colors correspond to CC criteria ranging from a fully efficacious sigmoidal response (red curve) to partial or incomplete responses (yellow, green, and blue) described in detail in ref. [1]. C Illustration of data demonstrating the ability to rotate the view to better appreciate differences in potency. Here, white curves are a combination of the yellow, green, and blue curves represented in A and B. D Gain-of-signal (blue), loss-of-signal (red) and inactive compound (grey dots) outputs plotted from a 51,441 compound qHTS assessing the library effect on the enzymatic activity of pyruvate kinase. E Chemotypes a, c and e are associated with loss-of-signal response output, while chemotypes d and e display a gain-of-signal response as discussed in Martinez et al. [26]. Data for graphs was obtained from the following PubChem AIDs, for plots in A–C: 1,347,405, 1,347,407 and 1,347,411; for plot D: 361; for plot E: 1,508,643

Fig. 2

qHTSWaterfall code repository and operating environments

Fig. 3

qHTSWaterfall interface showing a plot of sample data, hiding inactive results. The green and blue curves are individual coincidence reporter responses

Fig. 4

File format overview. A Top row format tags which include compound annotations in column 5 (e.g., SMILES), and concentration–response curve parameters (Log AC₅₀, S₀, S_inf, and Hill slope) in columns 6–9. B Example data columns, here an example of an 11-point titration with log base 10 transformed molar concentrations in the upper row, aligned with normalized data below