We first load the necessary packages and set some pre-defined values needed to replicate the analysis.

```
library(BIGL)
library(knitr)
library(rgl)
library(ggplot2)
set.seed(12345)
if (!requireNamespace("rmarkdown", quietly = TRUE) || !rmarkdown::pandoc_available("1.14")) {
warning(call. = FALSE, "These vignettes assume rmarkdown and pandoc
version 1.14. These were not found. Older versions will not work.")
::knit_exit()
knitr }
```

```
<- 4 # Dataset has 11 experiments, we consider only 4
nExp <- 0.95 # Cutoff for p-values to use in plot.maxR() function cutoff
```

The data for the analysis must come in a data-frame with required
columns `d1`

, `d2`

and `effect`

for
doses of two compounds and observed cell counts respectively. The
`effect`

column may represent also a type of normalized data
and subsequent transformation functions should be adjusted.

We will use sample data included in the package -
`directAntivirals`

.

```
data("directAntivirals", package = "BIGL")
head(directAntivirals)
```

```
## experiment cpd1 cpd2 effect d1 d2
## 1 1 cpd1_A cpd2_A 509.64 500 500
## 2 1 cpd1_A cpd2_A 589.67 500 500
## 3 1 cpd1_A cpd2_A 524.71 500 130
## 4 1 cpd1_A cpd2_A 575.45 500 130
## 5 1 cpd1_A cpd2_A 634.53 500 31
## 6 1 cpd1_A cpd2_A 702.35 500 31
```

This data consists of 11 experiments that can be processed separately. For initial illustration purposes we choose just one experiment and retain only the columns of interest. We define a simple function to do just that.

```
<- function(data, i) {
subsetData ## Subset data to a single experiment and, optionally, select the necessary
## columns only
subset(data, experiment == i)[, c("effect", "d1", "d2")]
}
```

Now let us only pick `Experiment 1`

to illustrate the
functionality of the package.

```
<- 1
i <- subsetData(directAntivirals, i) data
```

Dose-response data for `Experiment 1`

will be used for a
large share of the analysis presented here, therefore this subset is
stored in a dataframe called `data`

. Later, we will run the
analysis for some other experiments as well.

If raw data is measured in cell counts, data transformation might be of interest to improve accuracy and interpretation of the model. Of course, this will depend on the model specification. For example, if a generalized Loewe model is assumed on the growth rate of the cell count, the appropriate conversion should be made from the observed cell counts. The formula used would be \[y = N_0\exp\left(kt\right)\] where \(k\) is a growth rate, \(t\) is time (fixed) and \(y\) is the observed cell count. If such a transformation is specified, it is referred to as the biological transformation.

In certain cases, variance-stabilizing transformations (Box-Cox) can
also be useful. We refer to these transformations as power
transformations. In many cases, a simple logarithmic transformation can
be sufficient but, if desired, a helper function
`optim.boxcox`

is available to automate the selection of
Box-Cox transformation parameters.

In addition to specifying biological and power transformations, users are also asked to specify their inverses. These are later used in the bootstrapping procedure and plotting methods.

As an example, we might define a `transforms`

list that
will be passed to the fitting functions. It contains both biological
growth rate and power transformations along with their inverses.

```
## Define forward and reverse transform functions
<- list(
transforms "BiolT" = function(y, args) with(args, N0*exp(y*time.hours)),
"InvBiolT" = function(T, args) with(args, 1/time.hours*log(T/N0)),
"PowerT" = function(y, args) with(args, log(y)),
"InvPowerT" = function(T, args) with(args, exp(T)),
"compositeArgs" = list(N0 = 1,
time.hours = 72)
)
```

`compositeArgs`

contains the initial cell counts
(`N0`

) and incubation time (`time.hours`

). In
certain cases, the `getTransformations`

wrapper function can
be employed to automatically obtain a prepared list with biological
growth rate and power transformations based on results from
`optim.boxcox`

. Its output will also contain the inverses of
these transforms.

```
<- getTransformations(data)
transforms_auto fitMarginals(data, transforms = transforms_auto)
## In the case of 1-parameter Box-Cox transformation, it is easy
## to retrieve the power parameter by evaluating the function at 0.
## If parameter is 0, then it is a log-transformation.
with(transforms_auto, -1 / PowerT(0, compositeArgs))
```

Once dose-response dataframe is correctly set up, we may proceed onto
synergy analysis. We will use `transforms`

as defined above
with a logarithmic transformation. If not desired,
`transforms`

can be set to `NULL`

and would be
ignored.

Synergy analysis is quite modular and is divided into 3 parts:

- Determine marginal curves for each of the compounds. These curves are computed based on monotherapy data, i.e. those observations where one of the compounds is dosed at 0.
- Compute expected effects for a chosen null model given the previously determined marginal curves at various dose combinations.
- Compare the expected response with the observed effect using statistical testing procedures.

The first step of the fitting procedure will consist in treating marginal data only, i.e. those observations within the experiment where one of the compounds is dosed at zero. For each compound the corresponding marginal doses are modelled using a 4-parameter logistic model.

The marginal models will be estimated together using non-linear least
squares estimation procedure. Estimation of both marginal models needs
to be simultaneous since it is assumed they share a common baseline that
also needs to be estimated. The `fitMarginals`

function and
other marginal estimation routines will automatically extract marginal
data from the dose-response data frame.

Before proceeding onto the estimation, we get a rough guess of the
parameters to use as starting values in optimization and then we fit the
model. `marginalFit`

, returned by the
`fitMarginals`

routine, is an object of class
`MarginalFit`

which is essentially a list containing the main
information about the marginal models, in particular the estimated
coefficients.

The optional `names`

argument allows to specify the names
of the compounds to be shown on the plots and in the summary. If not
defined, the defaults (“Compound 1” and “Compound 2”) are used.

```
## Fitting marginal models
<- fitMarginals(data, transforms = transforms, method = "nls",
marginalFit names = c("Drug A", "Drug B"))
summary(marginalFit)
```

```
## Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
## Transformations: Yes
##
## Drug A Drug B
## Slope 2.132 1.201
## Maximal response 0.088 0.091
## log10(EC50) 1.008 0.977
##
## Common baseline at: 0.132
```

`marginalFit`

object retains the data that was supplied
and the transformation functions used in the fitting procedure. It also
has a `plot`

method which allows for a quick visualization of
the fitting results.

```
## Plotting marginal models
plot(marginalFit) + ggtitle(paste("Direct-acting antivirals - Experiment" , i))
```

Note as well that the `fitMarginals`

function allows
specifying linear constraints on parameters. This provides an easy way
for the user to impose asymptote equality, specific baseline value and
other linear constraints that might be useful. See
`help(constructFormula)`

for more details.

```
## Parameter ordering: h1, h2, b, m1, m2, e1, e2
## Constraint 1: m1 = m2. Constraint 2: b = 0.1
<- list("matrix" = rbind(c(0, 0, 0, -1, 1, 0, 0),
constraints c(0, 0, 1, 0, 0, 0, 0)),
"vector" = c(0, 0.1))
## Parameter estimates will now satisfy equality:
## constraints$matrix %*% pars == constraints$vector
fitMarginals(data, transforms = transforms,
constraints = constraints)
```

The `fitMarginals`

function allows an alternative
user-friendly way to specify one or more fixed-value constraints using a
named vector passed to the function via `fixed`

argument.

```
## Set baseline at 0.1 and maximal responses at 0.
fitMarginals(data, transforms = transforms,
fixed = c("m1" = 0, "m2" = 0, "b" = 0.1))
```

By default, no constraints are set, thus asymptotes are not shared and so a generalized Loewe model will be estimated.

We advise the user to employ the `method = "nlslm"`

argument which is set as the default in monotherapy curve estimation. It
is based on `minpack.lm::nlsLM`

function with an underlying
Levenberg-Marquardt algorithm for non-linear least squares estimation.
This algorithm is known to be more robust than
`method = "nls"`

and its Gauss-Newton algorithm. In cases
with nice sigmoid-shaped data, both methods should however lead to
similar results.

`method = "optim"`

is a simple sum-of-squared-residuals
minimization driven by a default Nelder-Mead algorithm from
`optim`

minimizer. It is typically slower than non-linear
least squares based estimation and can lead to a significant increase in
computational time for larger datasets and bootstrapped statistics. In
nice cases, Nelder-Mead algorithm and non-linear least squares can lead
to rather similar estimates but this is not always the case as these
algorithms are based on different techniques.

In general, we advise that in automated batch processing whenever
`method = "nlslm"`

does not converge fast enough and/or emits
a warning, user should implement a fallback to
`method = "optim"`

and re-do the estimation. If none of these
suggestions work, it might be useful to fiddle around and slightly
perturb starting values for the algorithms as well. By default, these
are obtained from the `initialMarginal`

function.

```
<- tryCatch({
nlslmFit fitMarginals(data, transforms = transforms,
method = "nlslm")
warning = function(w) w, error = function(e) e)
},
if (inherits(nlslmFit, c("warning", "error")))
<- tryCatch({
optimFit fitMarginals(data, transforms = transforms,
method = "optim")
})
```

Note as well that additional arguments to `fitMarginals`

passed via `...`

ellipsis argument will be passed on to the
respective solver function, i.e. `minpack.lm::nlsLM`

,
`nls`

or `optim`

.

While `BIGL`

package provides several routines to fit
4-parameter log-logistic dose-response models, some users may prefer to
use their own optimizers to estimate the relevant parameters. It is
rather easy to integrate this into the workflow by constructing a custom
`MarginalFit`

object. It is in practice a simple list
with

`coef`

: named vector with coefficient estimates`sigma`

: standard deviation of residuals`df`

: degrees of freedom from monotherapy curve estimates`model`

: model of the marginal estimation which allows imposing linear constraints on parameters. If no constraints are necessary, it can be left out or assigned the output of`constructFormula`

function with no inputs.`shared_asymptote`

: whether estimation is constrained to share the asymptote. During the estimation, this is deduced from`model`

object.`method`

: method used in dose-response curve estimation which will be re-used in bootstrapping`transforms`

: power and biological transformation functions (and their inverses) used in monotherapy curve estimation. This should be a list in a format described above. If`transforms`

is unspecified or`NULL`

, no transformations will be used in statistical bootstrapping unless the user asks for it explicitly via one of the arguments to`fitSurface`

.

Other elements in the `MarginalFit`

are currently unused
for evaluating synergy and can be disregarded. These elements, however,
might be necessary to ensure proper working of available methods for the
`MarginalFit`

object.

As an example, the following code generates a custom
`MarginalFit`

object that can be passed further to estimate a
response surface under the null hypothesis.

```
<- list("coef" = c("h1" = 1, "h2" = 2, "b" = 0,
marginalFit "m1" = 1.2, "m2" = 1, "e1" = 0.5, "e2" = 0.5),
"sigma" = 0.1,
"df" = 123,
"model" = constructFormula(),
"shared_asymptote" = FALSE,
"method" = "nlslm",
"transforms" = transforms)
class(marginalFit) <- append(class(marginalFit), "MarginalFit")
```

Note that during bootstrapping this would use
`minpack.lm::nlsLM`

function to re-estimate parameters from
data following the null. A custom optimizer for bootstrapping is
currently not implemented.

Five types of null models are available for calculating expected response surfaces.

- Generalized Loewe model is used if maximal responses are not
constrained to be equal, i.e.
`shared_asymptote = FALSE`

, in the marginal fitting procedure and`null_model = "loewe"`

in response calculation. - Classical Loewe model is used if constraints are such that
`shared_asymptote = TRUE`

in the marginal fitting procedure and`null_model = "loewe"`

in response calculation. - Highest Single Agent is used if
`null_model = "hsa"`

irrespective of the value of`shared_asymptote`

. - Bliss independence model is used when
`null_model = "bliss"`

. In the situations when maximal responses are constrained to be equal, the classical Bliss independence approach is used, when they are not equal, the Bliss independence calculation is performed on responses rescaled to the maximum range (i.e. absolute difference between baseline and maximal response).

- Alternative Loewe Generalization is used when
`null_model = "loewe2"`

. If the asymptotes are constrained to be equal, this reduces to the classical Loewe. Note that if`shared_asymptote = TRUE`

constraints are used, this also reduces to classical Loewe model.

If transformation functions were estimated using
`fitMarginals`

, these will be automatically recycled from the
`marginalFit`

object when doing calculations for the response
surface fit. Alternatively, transformation functions can be passed by a
separate argument. Since the `marginalFit`

object was
estimated without the shared asymptote constraint, the following will
compute the response surface based on the generalized Loewe model.

```
<- fitSurface(data, marginalFit,
rs null_model = "loewe",
B.CP = 50, statistic = "none", parallel = FALSE)
summary(rs)
```

```
Null model: Generalized Loewe Additivity
Variance assumption used: "equal"
Mean occupancy rate: 0.6732745
Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
Transformations: Yes
Drug A Drug B
Slope 2.132 1.201
Maximal response 0.088 0.091
log10(EC50) 1.008 0.977
Common baseline at: 0.132
No test statistics were computed.
CONFIDENCE INTERVALS
Overall effect
Estimated mean departure from null response surface with 95% confidence interval:
-0.0668 [-0.113, -0.0203]
Evidence for effects in data: Syn
Significant pointwise effects
estimate lower upper call
2_7.8 -0.2162 -0.4239 -0.0085 Syn
7.8_31 -0.2770 -0.4768 -0.0773 Syn
7.8_7.8 -0.5354 -0.7488 -0.3220 Syn
Pointwise 95% confidence intervals summary:
Syn Ant Total
3 0 49
```

The occupancy matrix used in the expected response calculation for
the Loewe models can be accessed with `rs$occupancy`

.

For off-axis data and a fixed dose combination, the Z-score for that dose combination is defined to be the standardized difference between the observed effect and the effect predicted by a generalized Loewe model. If the observed effect differs significantly from the prediction, it might be due to the presence of synergy or antagonism. If multiple observations refer to the same combination of doses, then a mean is taken over these multiple standardized differences.

The following plot illustrates the isobologram of the chosen null model. Coloring and contour lines within the plot should help the user distinguish areas and dose combinations that generate similar response according to the null model. Note that the isobologram is plotted by default on a logarithmically scaled grid of doses.

`isobologram(rs)`

The plot below illustrates the above considerations in a 3-dimensional setting. In this plot, points refer to the observed effects whereas the surface is the model-predicted response. The surface is colored according to the median Z-scores where blue coloring indicates possible synergistic effects (red coloring would indicate possible antagonism).

```
plot(rs, legend = FALSE, main = "")
view3d(0, -75)
rglwidget()
```

For the Highest Single Agent null model to work properly, it is
expected that both marginal curves are either decreasing or increasing.
Equivalent `summary`

and `plot`

methods are also
available for this type of null model.

```
<- fitSurface(data, marginalFit,
rsh null_model = "hsa",
B.CP = 50, statistic = "both", parallel = FALSE)
summary(rsh)
```

```
Null model: Highest Single Agent with differing maximal response
Variance assumption used: "equal"
Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
Transformations: Yes
Drug A Drug B
Slope 2.132 1.201
Maximal response 0.088 0.091
log10(EC50) 1.008 0.977
Common baseline at: 0.132
Exact meanR test (H0 = no synergy/antagonism):
F(49,117) = 17.2006 (p-value < 2e-16)
Evidence for effects in data: Syn
Points with significant deviations from the null:
d1 d2 absR p-value call
2_0.12 2.0 0.12 4.764083 0.00015 Syn
2_0.49 2.0 0.49 4.606532 0.00039 Syn
2_2 2.0 2.00 4.320753 0.00148 Syn
2_7.8 2.0 7.80 4.595625 0.00041 Syn
7.8_2 7.8 2.00 8.539806 <2e-16 Syn
7.8_31 7.8 31.00 8.173404 <2e-16 Syn
7.8_7.8 7.8 7.80 22.495627 <2e-16 Syn
Overall maxR summary:
Call Syn Ant Total
Syn 7 0 49
CONFIDENCE INTERVALS
Overall effect
Estimated mean departure from null response surface with 95% confidence interval:
-0.1219 [-0.1626, -0.0808]
Evidence for effects in data: Syn
Significant pointwise effects
estimate lower upper call
0.49_7.8 -0.2232 -0.4125 -0.0339 Syn
2_0.12 -0.2193 -0.3926 -0.0459 Syn
2_0.49 -0.2239 -0.3928 -0.0549 Syn
2_2 -0.3246 -0.5196 -0.1295 Syn
2_31 -0.2554 -0.4484 -0.0624 Syn
2_7.8 -0.4256 -0.6149 -0.2364 Syn
31_500 -0.2312 -0.4237 -0.0388 Syn
7.8_130 -0.1852 -0.3671 -0.0034 Syn
7.8_2 -0.5581 -0.7603 -0.3559 Syn
7.8_31 -0.5155 -0.7085 -0.3225 Syn
7.8_7.8 -1.2862 -1.4755 -1.0970 Syn
Pointwise 95% confidence intervals summary:
Syn Ant Total
11 0 49
```

Also for the Bliss independence null model to work properly, it is
expected that both marginal curves are either decreasing or increasing.
Equivalent `summary`

and `plot`

methods are also
available for this type of null model.

```
<- fitSurface(data, marginalFit,
rsb null_model = "bliss",
B.CP = 50, statistic = "both", parallel = FALSE)
summary(rsb)
```

```
Null model: Bliss independence with differing maximal response
Variance assumption used: "equal"
Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
Transformations: Yes
Drug A Drug B
Slope 2.132 1.201
Maximal response 0.088 0.091
log10(EC50) 1.008 0.977
Common baseline at: 0.132
Exact meanR test (H0 = no synergy/antagonism):
F(49,117) = 4.4493 (p-value = 0)
Evidence for effects in data: Syn
Points with significant deviations from the null:
d1 d2 absR p-value call
2_7.8 2.0 7.8 4.496210 0.00089 Syn
7.8_7.8 7.8 7.8 9.325958 <2e-16 Syn
Overall maxR summary:
Call Syn Ant Total
Syn 2 0 49
CONFIDENCE INTERVALS
Overall effect
Estimated mean departure from null response surface with 95% confidence interval:
-0.0688 [-0.1013, -0.0232]
Evidence for effects in data: Syn
Significant pointwise effects
estimate lower upper call
0.49_7.8 -0.2203 -0.4272 -0.0135 Syn
2_0.12 -0.2044 -0.3861 -0.0228 Syn
2_2 -0.2417 -0.4505 -0.0330 Syn
2_31 -0.2320 -0.4476 -0.0163 Syn
2_7.8 -0.3700 -0.5824 -0.1576 Syn
7.8_2 -0.3088 -0.5369 -0.0806 Syn
7.8_31 -0.2347 -0.4306 -0.0388 Syn
7.8_7.8 -0.6198 -0.8267 -0.4130 Syn
Pointwise 95% confidence intervals summary:
Syn Ant Total
8 0 49
```

Also for the Alternative Loewe Generalization null model to work
properly, it is expected that both marginal curves are either decreasing
or increasing. Equivalent `summary`

and `plot`

methods are also available for this type of null model.

```
<- fitSurface(data, marginalFit,
rsl2 null_model = "loewe2",
B.CP = 50, statistic = "both", parallel = FALSE)
summary(rsl2)
```

```
Null model: Alternative generalization of Loewe Additivity
Variance assumption used: "equal"
Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
Transformations: Yes
Drug A Drug B
Slope 2.132 1.201
Maximal response 0.088 0.091
log10(EC50) 1.008 0.977
Common baseline at: 0.132
Exact meanR test (H0 = no synergy/antagonism):
F(49,117) = 4.6653 (p-value = 0)
Evidence for effects in data: Syn
Points with significant deviations from the null:
d1 d2 absR p-value call
2_0.12 2.0 0.12 3.845699 0.01033 Syn
7.8_31 7.8 31.00 4.458567 0.00091 Syn
7.8_7.8 7.8 7.80 9.123321 <2e-16 Syn
Overall maxR summary:
Call Syn Ant Total
Syn 3 0 49
CONFIDENCE INTERVALS
Overall effect
Estimated mean departure from null response surface with 95% confidence interval:
-0.0706 [-0.1061, -0.0387]
Evidence for effects in data: Syn
Significant pointwise effects
estimate lower upper call
7.8_31 -0.2998 -0.5403 -0.0594 Syn
7.8_7.8 -0.5565 -0.8128 -0.3003 Syn
Pointwise 95% confidence intervals summary:
Syn Ant Total
2 0 49
```

Presence of synergistic or antagonistic effects can be formalized by means of statistical tests. Two types of tests are considered here and are discussed in more details in the methodology vignette as well as the accompanying paper.

`meanR`

test evaluates how the predicted response surface based on a specified null model differs from the observed one. If the null hypothesis is rejected, this test suggests that at least some dose combinations may exhibit synergistic or antagonistic behaviour. The`meanR`

test is not designed to pinpoint which combinations produce these effects nor what type of deviating effect is present.`maxR`

test allows to evaluate presence of synergistic/antagonistic effects for each dose combination and as such provides a point-by-point classification.

Both of the above test statistics have a well specified null distribution under a set of assumptions, namely normality of Z-scores. If this assumption is not satisfied, distribution of these statistics can be estimated using bootstrap. Normal approximation is significantly faster whereas bootstrapped distribution of critical values is likely to be more accurate in many practical cases.

Here we will use the previously computed `CP`

covariance
matrix to speed up the process.

- normal errors

```
<- fitSurface(data, marginalFit,
meanR_N statistic = "meanR", CP = rs$CP, B.B = NULL,
parallel = FALSE)
```

- non-normal errors

The previous piece of code assumes normal errors. If we drop this
assumption, we can use bootstrap methods to resample from the observed
errors. Other parameters for bootstrapping, such as additional
distribution for errors, wild bootstrapping to account for
heteroskedasticity, are also available. See
`help(fitSurface)`

.

```
<- fitSurface(data, marginalFit,
meanR_B statistic = "meanR", CP = rs$CP, B.B = 20,
parallel = FALSE)
```

Both tests use the same calculated F-statistic but compare it to different null distributions. In this particular case, both tests lead to identical results.

F-statistic | p-value | |
---|---|---|

Normal errors | 4.353656 | 0 |

Bootstrapped errors | 4.353656 | 0 |

The `meanR`

statistic can be complemented by the
`maxR`

statistic for each of available dose combinations. We
will do this once again by assuming both normal and non-normal errors
similar to the computation of the `meanR`

statistic.

```
<- fitSurface(data, marginalFit,
maxR_N statistic = "maxR", CP = rs$CP, B.B = NULL,
parallel = FALSE)
<- fitSurface(data, marginalFit,
maxR_B statistic = "maxR", CP = rs$CP, B.B = 20,
parallel = FALSE)
<- rbind(summary(maxR_N$maxR)$totals,
maxR_both summary(maxR_B$maxR)$totals)
```

Here is the summary of `maxR`

statistics. It lists the
total number of dose combinations listed as synergistic or antagonistic
for Experiment 1 given the above calculations.

Call | Syn | Ant | Total | |
---|---|---|---|---|

Normal errors | Syn | 3 | 0 | 49 |

Bootstrapped errors | Syn | 2 | 0 | 49 |

By using the `outsidePoints`

function, we can obtain a
quick summary indicating which dose combinations in Experiment 1 appear
to deviate significantly from the null model according to the
`maxR`

statistic.

```
<- outsidePoints(maxR_B$maxR$Ymean)
outPts kable(outPts, caption = paste0("Non-additive points for Experiment ", i))
```

d1 | d2 | absR | p-value | call | |
---|---|---|---|---|---|

7.8_31 | 7.8 | 31.0 | 4.176872 | 0.06 | Syn |

7.8_7.8 | 7.8 | 7.8 | 8.905776 | 0.00 | Syn |

Synergistic effects of drug combinations can be depicted in a
bi-dimensional contour plot where the `x-axis`

and
`y-axis`

represent doses of `Compound 1`

and
`Compound 2`

respectively and each point is colored based on
the *p*-value and sign of the respective `maxR`

statistic.

```
contour(maxR_B,
## colorPalette = c("blue", "black", "black", "red"),
main = paste0(" Experiment ", i, " contour plot for maxR"),
scientific = TRUE, digits = 3, cutoff = cutoff)
```

Previously, we had colored the 3-dimensional predicted response
surface plot based on its Z-score, i.e. deviation of the predicted
versus the observed effect. We can also easily color it based on the
computed `maxR`

statistic to account for additional
statistical variation.

```
plot(maxR_B, color = "maxR", legend = FALSE, main = "")
view3d(0, -75)
rglwidget()
```

The BIGL package also yields effect sizes and corresponding
confidence intervals with respect to any response surface. The overall
effect size and confidence interval is output in the summary of the
`ResponseSurface`

, but can also be called directly:

`summary(maxR_B$confInt)`

```
## Overall effect
## Estimated mean departure from null response surface with 95% confidence interval:
## -0.0668 [-0.099, -0.0242]
## Evidence for effects in data: Syn
##
## Significant pointwise effects
## estimate lower upper call
## 7.8_31 -0.2770 -0.4858 -0.0683 Syn
## 7.8_7.8 -0.5354 -0.7584 -0.3124 Syn
##
## Pointwise 95% confidence intervals summary:
## Syn Ant Total
## 2 0 49
```

In addition, a contour plot can be made with pointwise confidence intervals. Contour plot colouring can be defined according to the effect sizes or according to maxR results.

`plotConfInt(maxR_B, color = "effect-size")`

Starting from the package version `1.2.0`

the variance can
be estimated separately for on-axis (monotherapy) and off-axis points
using `method`

argument to `fitSurface`

. The
possible values for `method`

are:

`"equal"`

, equal variances assumed (as above, default),`"unequal"`

, variance is estimated separately for on-axis and off-axis points,`"model"`

, the variance is modelled as a function of the mean.

Please see the methodology vignette for details. Below we show an example analysis in such case. Note that transformations are not possible if variances are not assumed equal.

```
<- fitMarginals(data, transforms = NULL)
marginalFit summary(marginalFit)
```

```
## Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
## Transformations: No
##
## Compound 1 Compound 2
## Slope 1.579 1.459
## Maximal response 425.607 789.029
## log10(EC50) 0.663 0.500
##
## Common baseline at: 13200.55
```

```
<- fitSurface(data, marginalFit, method = "unequal",
resU statistic = "both", B.CP = 20, B.B = 20, parallel = FALSE)
summary(resU)
```

```
## Null model: Generalized Loewe Additivity
## Variance assumption used: "unequal"
## Mean occupancy rate: 0.7663414
##
## Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
## Transformations: No
##
## Compound 1 Compound 2
## Slope 1.579 1.459
## Maximal response 425.607 789.029
## log10(EC50) 0.663 0.500
##
## Common baseline at: 13200.55
##
## Bootstrapped meanR test (H0 = no synergy/antagonism):
## F(49,117) = 3.9581 (p-value < 2e-16)
##
## Evidence for effects in data: Syn
## Points with significant deviations from the null:
## d1 d2 absR p-value call
## 0.12_0.12 0.12 0.12 7.129077 <2e-16 Syn
## 0.49_0.12 0.49 0.12 6.520855 <2e-16 Syn
##
## Overall maxR summary:
## Call Syn Ant Total
## Syn 2 0 49
##
## CONFIDENCE INTERVALS
## Overall effect
## Estimated mean departure from null response surface with 95% confidence interval:
## -179.9317 [-777.8101, 27.9046]
## Evidence for effects in data: None
##
## Significant pointwise effects
## estimate lower upper call
## 0.12_0.12 -1325.992 -2323.329 -328.6560 Syn
## 0.49_0.12 -1195.634 -2215.949 -175.3202 Syn
##
## Pointwise 95% confidence intervals summary:
## Syn Ant Total
## 2 0 49
```

For the variance model, an exploratory plotting function is available to explore the relationship between the mean and the variance.

`plotMeanVarFit(data)`

`plotMeanVarFit(data, log = "xy") #Clearer on the log-scale`

`plotMeanVarFit(data, trans = "log") #Thresholded at maximum observed variance`

The linear fit seems fine in this case.

```
<- fitSurface(data, marginalFit, method = "model",
resM statistic = "both", B.CP = 20, B.B = 20, parallel = FALSE)
```

If the log transformation yielded a better fit, then this could be achieved by using the following option.

```
<- fitSurface(data, marginalFit, method = "model", trans = "log",
resL statistic = "both", B.CP = 20, B.B = 20, parallel = FALSE)
```

Negative variances were modelled, but variance model has the smallest observed variances as minimum so we can proceed.`

`summary(resM) `

```
## Null model: Generalized Loewe Additivity
## Variance assumption used: "model"
## Mean occupancy rate: 0.7663414
##
## Formula: effect ~ fn(h1, h2, b, m1, m2, e1, e2, d1, d2)
## Transformations: No
##
## Compound 1 Compound 2
## Slope 1.579 1.459
## Maximal response 425.607 789.029
## log10(EC50) 0.663 0.500
##
## Common baseline at: 13200.55
##
## Bootstrapped meanR test (H0 = no synergy/antagonism):
## F(49,117) = 3.7381 (p-value < 2e-16)
##
## Evidence for effects in data: Syn
## Points with significant deviations from the null:
## d1 d2 absR p-value call
## 7.8_7.8 7.8 7.8 7.951282 <2e-16 Syn
##
## Overall maxR summary:
## Call Syn Ant Total
## Syn 1 0 49
##
## CONFIDENCE INTERVALS
## Overall effect
## Estimated mean departure from null response surface with 95% confidence interval:
## -179.9317 [-509.0856, 14.7183]
## Evidence for effects in data: None
##
## Significant pointwise effects
## [1] estimate lower upper call
## <0 rows> (or 0-length row.names)
##
## Pointwise 95% confidence intervals summary:
## Syn Ant Total
## 0 0 49
```

In order to proceed with multiple experiments, we repeat the same
procedure as previously. We collect all the necessary objects for which
estimations do not have to be repeated to generate `meanR`

and `maxR`

statistics in a simple list.

```
<- list()
marginalFits <- list()
datasets <- list()
respSurfaces <- list()
maxR.summary for (i in seq_len(nExp)) {
## Select experiment
<- subsetData(directAntivirals, i)
data ## Fit joint marginal model
<- fitMarginals(data, transforms = transforms,
marginalFit method = "nlslm")
## Predict response surface based on generalized Loewe model
<- fitSurface(data, marginalFit,
respSurface statistic = "maxR", B.CP = 20,
parallel = FALSE)
<- data
datasets[[i]] <- marginalFit
marginalFits[[i]] <- respSurface
respSurfaces[[i]] <- summary(respSurface$maxR)$totals
maxR.summary[[i]] }
```

We use the `maxR`

procedure with a chosen p-value cutoff
of 0.95. If `maxR`

statistic falls outside the 95th
percentile of its distribution (either bootstrapped or not), the
respective off-axis dose combination is said to deviate significantly
from the generalized Loewe model and the algorithm determines whether it
deviates in a synergistic or antagonistic way.

Below is the summary of overall calls and number of deviating points for each experiment.

Call | Syn | Ant | Total | |
---|---|---|---|---|

Experiment 1 | Syn | 3 | 0 | 49 |

Experiment 2 | Syn | 5 | 0 | 49 |

Experiment 3 | Syn | 5 | 0 | 49 |

Experiment 4 | Syn | 13 | 2 | 49 |

Previous summarizing and visual analysis can be repeated on each of
the newly defined experiments. For example, `Experiment 4`

indicates a total of 15 combinations that were called synergistic
according to the `maxR`

test.

d1 | d2 | absR | p-value | call | |
---|---|---|---|---|---|

0.12_0.25 | 0.12 | 0.25000 | 3.369638 | 0.04933 | Ant |

2_0.004 | 2.00 | 0.00400 | 5.930847 | 0.00000 | Syn |

2_0.016 | 2.00 | 0.01600 | 7.503016 | 0.00000 | Syn |

2_0.063 | 2.00 | 0.06300 | 9.122209 | 0.00000 | Syn |

2_0.25 | 2.00 | 0.25000 | 6.078746 | 0.00000 | Syn |

2_1 | 2.00 | 1.00000 | 3.849097 | 0.00976 | Syn |

31_0.016 | 31.00 | 0.01600 | 3.480577 | 0.03389 | Syn |

31_0.063 | 31.00 | 0.06300 | 4.481778 | 0.00093 | Syn |

31_0.25 | 31.00 | 0.25000 | 5.174262 | 0.00003 | Syn |

31_1 | 31.00 | 1.00000 | 4.017347 | 0.00539 | Syn |

7.8_0.00024 | 7.80 | 0.00024 | 4.147400 | 0.00336 | Ant |

7.8_0.016 | 7.80 | 0.01600 | 8.180572 | 0.00000 | Syn |

7.8_0.063 | 7.80 | 0.06300 | 11.711154 | 0.00000 | Syn |

7.8_0.25 | 7.80 | 0.25000 | 10.980381 | 0.00000 | Syn |

7.8_1 | 7.80 | 1.00000 | 7.025138 | 0.00000 | Syn |

Consequently, above table for `Experiment 4`

can be
illustrated in a contour plot.