User instructions and tutorials of the synergyfinder package

Recently, drug combination therapies provide a promising strategy in treating cancer by inhabiting redundant pathways simultaneously [1]. Drug combination screening in the cancer cell models is often utilized as a starting point to prioritize the most potential hits for further experimental investigation and therapy optimization [2]. To facilitate the drug combination discovery, high-throughput drug combination screening has the advantage of assaying a large collection of chemical compounds, generating dynamic dose-response profiles that allow us to quantify the degree of drug-drug interactions at an unprecedented level. A drug interaction is usually classified as synergistic, antagonistic or non-interactive, based on the deviation of the observed drug combination response from the expected effect of non-interaction (the null hypothesis). To quantify the degree of drug synergy, several models have been proposed, such as those based on the Highest single agent model (HSA) [3], the Loewe additivity model (Loewe) [4] and the Bliss independence model (Bliss) [5]. However, these existing drug synergy scoring models, together with their software implementations, were proposed initially for low-throughput experiments, which are not flexible enough to analyse high throughput drug combination screening data. We have recently developed a response surface model, called Zero Interaction Potency (ZIP), which combines the advantages of the Loewe and the Bliss models, and proposed a delta score to characterize the synergy landscape over the full dose-response matrix, which is designed to analyse high throughput drug combination screening data [6]. The package synergyfinder provides efficient implementations for all the popular synergy scoring models, including HSA, Loewe, Bliss and ZIP. We will demonstrate how to use the synergyfinder package to high throughput drug combination screening data in this vignette.