User tutorial of the SynergyFinder Plus

Shuyu Zheng, Wenyu Wang, and Jing Tang Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki



Combinatorial therapies is one of the major strategies for improving treatment efficacy in treating cancer. High-throughput drug combination screening has the advantage of assaying a large collection of chemical compounds in search for promising drug pairs. Studies that utilizes drug combination screening have generated dynamic dose-response profiles that allow researchers to quantify the degree of drug-drug interactions at an unprecedented level. SynergyFinder R package is a software tool to analyze such pre-clinical drug combination data sets. It provides efficient implementations for

  1. all the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug synergy;
  2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination;
  3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values;
  4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy;
  5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations.

To facilitate the use of the R package for the drug discovery community, we also provide a web server at as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data. In the web application, in addition to the functions in R package, we provide the annotation of drugs and cell lines that are tested in an experiment, including their chemical information, targets and signaling network information.

1 Installation

Note: We recommend to install Bioconductor >= 3.13 and SynergyFinder >= 3.0.1 to access the new features described in this vignette.

To install the SynergyFinder package from Bioconductor, start R (>= 4.0) and then enter:

if (!requireNamespace("BiocManager", quietly = TRUE))


Load SynergyFinder to R environment


2 Input data

synergyfinder requires a data frame that describes the drug combination data set. The dose-response data is represented as a long table where each row represent one observation in the dose-response matrix.

The input table must contain the following columns (The column naming styles used by the old versions of synergyfinder or DrugComb, i.e. ‘Alternative Column names’ are accepted):

Required Columns Alternative Column names Description
block_id PairIndex, BlockId Identifier for the drug combination blocks.
drug1 Drug1, drug_row, DrugRow Name of the first tested drug.
drug2 Drug2, drug_col, DrugCol Name of the second tested drug.
conc1 Conc1, conc_row, ConcRow Concentration of first tested drug.
conc2 Conc2, conc_col, ConcCol Concentration of second tested drug.
response Response, inhibition, Inhibition Cell response to the drug treatment (%inhibition or %viability).
conc_unit ConcUnit Unit of concentration for drugs. This column could be replaced by multiple separated columns for each tested drugs (see table below), while different unit was used for measuring the concentrations.

There are several optional columns available for the higher-order drug combination data sets or the data sets using different concentration units for drugs.

Optional Columns Alternative Column names Description
conc_unit1 conc_r_unit Unit of concentration for the first drug. Used if the concentration units are not identical across the drugs tested in one block.
conc_unit2 conc_c_unit Unit of concentration for the second drug. Used if the concentration units are not identical across the drugs in test one block.
drug[n] Name of the n_th_ tested drug. For example, “drug3” for the third tested drug. Used for higher-order drug combination data point.
conc[n] Concentration of n_th_ tested drug. Used for higher-order drug combination data point.
conc_unit[n] Unit of concentration for n_th drug. Used if the concentration units are not identical across the drugs in test one block.


  1. The duplicated concentration combinations in one block (with the same “block_id”) will be treated as replicates.
  2. There is no restriction on the number of drug combinations (blocks) for the input file. The data should however, contain at least three concentrations for each drug in each block, so that sensible synergy scores can be calculated.
  3. synergyfinder allows for missing values in the dose-response matrix. The missing value will be automatically imputed by mice R package.

SynergyFinder provides an example input data (mathews_screening_data) in the package, which is extracted from a published drug combination screening study for the treatment of diffuse large B-cell lymphoma (DLBCL) [1]. The example input data contains two representative drug combinations (ibrutinib & ispinesib and ibrutinib & canertinib) for which the %viability of a cell line TMD8 was assayed using a 6 by 6 dose matrix design. To load the example data, please run:

#>   block_id  drug_row  drug_col conc_r  conc_c  response conc_r_unit conc_c_unit
#> 1        1 ispinesib ibrutinib   2500 50.0000  7.802637          nM          nM
#> 2        1 ispinesib ibrutinib   2500 12.5000  6.831317          nM          nM
#> 3        1 ispinesib ibrutinib   2500  3.1250 15.089589          nM          nM
#> 4        1 ispinesib ibrutinib   2500  0.7812 24.503885          nM          nM
#> 5        1 ispinesib ibrutinib   2500  0.1954 38.043076          nM          nM
#> 6        1 ispinesib ibrutinib   2500  0.0000 45.790634          nM          nM

3 Reshaping and pre-processing

We provide a function ReshapeData to reshape and pre-process the input data for further analysis:

Important parameters:

res <- ReshapeData(
  data = mathews_screening_data,
  data_type = "viability",
  impute = TRUE,
  impute_method = NULL,
  noise = TRUE,
  seed = 1)

The output data is a list containing following components:

#> List of 2
#>  $ drug_pairs: tibble [2 × 7] (S3: tbl_df/tbl/data.frame)
#>   ..$ block_id  : int [1:2] 1 2
#>   ..$ drug1     : chr [1:2] "ispinesib" "canertinib"
#>   ..$ drug2     : chr [1:2] "ibrutinib" "ibrutinib"
#>   ..$ conc_unit1: chr [1:2] "nM" "nM"
#>   ..$ conc_unit2: chr [1:2] "nM" "nM"
#>   ..$ input_type: chr [1:2] "viability" "viability"
#>   ..$ replicate : logi [1:2] FALSE FALSE
#>  $ response  : tibble [72 × 5] (S3: tbl_df/tbl/data.frame)
#>   ..$ block_id       : int [1:72] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..$ conc1          : num [1:72] 2500 2500 2500 2500 2500 2500 625 625 625 625 ...
#>   ..$ conc2          : num [1:72] 50 12.5 3.125 0.781 0.195 ...
#>   ..$ response       : num [1:72] 92.2 93.2 84.9 75.5 62 ...
#>   ..$ response_origin: num [1:72] 7.8 6.83 15.09 24.5 38.04 ...

4 Synergy and sensitivity analysis

4.0 Baseline Correction

Typically, the models for estimating synergy or sensitivity of drug combinations expect that the drug’s effect to be expressed as continuous values ranged between 0 and 1 (or between 0% and 100%). However, in practice the readouts from the drug combination screening could be negative. In this cases, the technical error might be introduced in the dose-response data. The base line correction function is designed to adjust the “baseline” of the dose-response data closer to 0. Following is the main steps for the process of “baseline correction”:

  1. Extract all the single drug treatment (monotherapy) dose-response data from the combination.
  2. Fit the four-parameter log logistic model for each monotherapy with the data table from step 1.
  3. Pick the minimum fitted response value from all the models fitted in step 2 as “baseline”.
  4. Adjusted response value = observed response value - ((100 – observed response value) / 100 * baseline).

By setting the value of the parameter correct_baseline while calling function CalculateSynergy or CalculateSensitivity, user could chose:

4.1 Drug synergy scoring

The synergistic effect can be determined as the excess of observed effect over expected effect calculated by a reference models (synergy scoring models). All of the models make different assumptions regarding the expected effect. Currently, 4 reference models are available in SynergyFinder.

\[y_{HSA} = max(y_1, y_2)\] where \(y_1\) and \(y_2\) are the monotherapy effect of combined drug 1 and drug 2

\[ y_{Bliss} = y_1 + y_2 - y_1 \cdot y_2 \] where \(y_1\) and \(y_2\) are the monotherapy effect of combined drug 1 and drug 2

\[ \frac {x_1}{\chi_{Loewe}^1} + \frac{x_2}{\chi_{Loewe}^2} = 1 \]

where \(x_{1,2}\) are drug doses and \(\chi_{Loewe}^1,\ \chi_{Loewe}^2\) are the doses of drug 1 and 2 alone that produce \(y_{Loewe}\). Using 4-parameter log-logistic (4PL) curves to describe dose-response curves the following parametric form of previous equation is derived:

\[ \frac {x_1}{m_1(\frac{y_{Loewe}-E_{min}^1}{E_{max}^1 - y_{Loewe}})^{\frac{1}{\lambda_1}}} + \frac{x_2}{m_2(\frac{y_{Loewe}-E_{min}^2}{E_{max}^2 - y_{Loewe}})^{\frac{1}{\lambda_2}}} = 1 \] where \(E_{min}, E_{max}\in[0,1]\) are minimal and maximal effects of the drug, \(m_{1,2}\) are the doses of drugs that produce the midpoint effect of \(E_{min} + E_{max}\), also known as relative \(EC_{50}\) or \(IC_{50}\), and \(\lambda_{1,2}(\lambda>0)\) are the shape parameters indicating the sigmoidicity or slope of dose-response curves. A numerical nonlinear solver can be then used to determine \(y_{Loewe}\) for (\(x_1\), \(x_2\)).

\[ y_{ZIP} = \frac{(\frac{x_1}{m_1}) ^ {\lambda_1}}{(1 + \frac{x_1}{m_1})^{\lambda_1}}+\frac{(\frac{x_2}{m_2})^{\lambda_2}}{(1 + \frac{x_2}{m_2})^{\lambda_2}} - \frac{(\frac{x_1}{m_1})^{\lambda_1}}{(1 + \frac{x_1}{m_1})^{\lambda_1}} \cdot \frac{(\frac{x_2}{m_2})^{\lambda_2}}{(1 + \frac{x_2}{m_2})^{\lambda_2}} \] where \(x_{1, 2}, m_{1, 2}, and\ \lambda_{1, 2}\) are defined in the Loewe model.

For the input data without replicates, the statistical analysis for synergy scores of the whole dose-response matrix is estimated under the null hypothesis of non-interaction (zero synergy score). Suppose that \(S_1, S_2, ...S_n\) are the synergy scores for all the concentration combinations (excluding monotherapies) for the dose-response matrix with mean of \(\bar s\). The p-value is:

\[p = exp(-0.717z - 0.416z^2),\ where\ z = abs(\bar s')/\sqrt{\frac{1}{n - 1}\sum_{i = 1}^n(s_i' - \bar s')^2}\]

The function CalculateSynergy is designed to calculate the synergy scores. Important parameters are:

The output adds one data frame “synergy_scores” to the input R object. It contains:

The mean of synergy scores for the combination matrix (excluding the monotherapy observations) and the p-values for them are added to the “drug_pairs” table in the input R object.

4.2 Sensitivity scoring

SynergyFinder mainly calculates 3 sensitive scores: relative IC50 and relative inhibition (RI) for single drug treatment, and combination sensitivity score (CSS) for drug combinations.

\[y = y_{min} + \frac{y_{max} - y_{min}}{1 + 10^{\lambda(log_{10}IC_{50} - x')}}\] where \(y_{min}, y_{max}\) are minimal and maximal inhibition and \(x' = log_{10}x\)

Figure 1. Concept of RI

Figure 1. Concept of RI

\[AUC = \int_{c_1}^{c_2}y_{min} + \frac{y_{max}-y_{min}}{1 + 10^{\lambda(log_{10}IC_{50}-x')}}dx'\] where \([c_1, c_2]\) is the concentration range of the foreground drug tested.[6]

The function CalculateSensitivity is designed to calculate the synergy scores. Important parameters are:

Following sensitivity scores are added to the “drug_pairs” table in the input R object:

5 Visualization

5.1 Dose-response curve

The PlotDoseResponseCurve function will plot the dose-response curve fitted by 4-parameter log logistic function for selected drug. Important parameters are:

This function will return an object of class recordedplot. User could use replayPlot to plot it. User could modify the parameter plot_setting to control the themes of some items in the plot.

If parameter record_plot is set as TRUE, the function will return a plot object recorded by recordPlot.

5.2 Two-drug combination visualization

SynergyFinder provides 3 types (Heatmap, 2D contour plot, or 3D surface) of plots to visualize dose-response map or synergy map for two-drug combinations.

From the plots for dose-response landscape, user can assess the therapeutic significance of the combination, e.g. by identifying the concentrations at which the drug combination can lead to a maximal effect on cancer inhibition.

The landscape of drug interaction scoring is very informative when identifying the specific dose regions where a synergistic or antagonistic drug interaction occurs.

Important parameters shared by all the three functions are:

Following shows the examples for dose-response and ZIP synergy landscape.

5.2.3 3D surface plot

If parameter dynamic is set as FALSE, this function will return an object of class recordedplot. User could use replayPlot to plot it.

It is recommend to plot the 3D surface in dynamic (dynamic=TRUE) way, with which user could adjust the view point in an interactive way to show the surface at the best angle.

5.3 Plotting wrapper for two-drug combination

SynergyFinder provides two plotting wrapper for two-drug combination dose-response visualization (PlotDoseResponse) and synergy score visualization (PlotSynergy).

The function PlotDoseResponse combined the dose-response curve and dose-response heatmap in one figure. In addition to the parameters inherited from PlotDoseResponseCurve and Plot2DrugHeatmap, this function has the parameters for saving the plot as a file, including: