This section contains a number of examples of real designs, and shows how they can be explored with ExploreModelMatrix. For each example, the sample information table is printed out. Next, the VisualizeDesign() function is called to generate a static plot of the fitted values, in terms of the model coefficients. This is the same plot that is displayed in the top left panel of the interactive interface generated by ExploreModelMatrix(). We also provide the code for generating and (for interactive sessions) opening the interactive application with ExploreModelMatrix().
Example 1
This example illustrates a two-factor design (genotype and treatment), where the effect of the genotype and treatment are assumed to be additive. For each genotype, two treated and two control individuals are studied. The design formula is ~ genotype + treatment, reflecting the assumption of additivity between the two predictors. The figure generated by the VisualizeDesign() function, displayed below, shows the value of the linear predictor (or, for a regular linear model, the fitted values) for observations with a given combination of predictor values, in terms of the model coefficients. This can be useful in order to set up suitable contrasts. For example, we can see that testing the null hypothesis that the genotypeB coefficient is zero would correspond to comparing observations with genotype B and those with genotype A.
(sampleData <- data.frame(genotype = rep(c("A", "B"), each = 4),
treatment = rep(c("ctrl", "trt"), 4)))
#> genotype treatment
#> 1 A ctrl
#> 2 A trt
#> 3 A ctrl
#> 4 A trt
#> 5 B ctrl
#> 6 B trt
#> 7 B ctrl
#> 8 B trt
vd <- VisualizeDesign(sampleData = sampleData,
designFormula = ~ genotype + treatment,
textSizeFitted = 4)
cowplot::plot_grid(plotlist = vd$plotlist)
app <- ExploreModelMatrix(sampleData = sampleData,
designFormula = ~ genotype + treatment)
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if (interactive()) shiny::runApp(app)
Example 2
From https://support.bioconductor.org/p/121132/. In this example we are considering a set of patients, each being either Resistant or Sensitive to a treatment, and each studied before (pre) and after (post) treatment. Patients have been renumbered within each response group, and patients with only pre- or post-measurements are removed. We use the design ~ Response + Response:ind.n + Response:Treatment. As can be seen from the visualization below, this lets us easily compare e.g. post- vs pre-treatment observations within the Sensitive group (via the ResponseSensitive.Treatmentpre coefficient).
(sampleData <- data.frame(
Response = rep(c("Resistant", "Sensitive"), c(12, 18)),
Patient = factor(rep(c(1:6, 8, 11:18), each = 2)),
Treatment = factor(rep(c("pre","post"), 15)),
ind.n = factor(rep(c(1:6, 2, 5:12), each = 2))))
#> Response Patient Treatment ind.n
#> 1 Resistant 1 pre 1
#> 2 Resistant 1 post 1
#> 3 Resistant 2 pre 2
#> 4 Resistant 2 post 2
#> 5 Resistant 3 pre 3
#> 6 Resistant 3 post 3
#> 7 Resistant 4 pre 4
#> 8 Resistant 4 post 4
#> 9 Resistant 5 pre 5
#> 10 Resistant 5 post 5
#> 11 Resistant 6 pre 6
#> 12 Resistant 6 post 6
#> 13 Sensitive 8 pre 2
#> 14 Sensitive 8 post 2
#> 15 Sensitive 11 pre 5
#> 16 Sensitive 11 post 5
#> 17 Sensitive 12 pre 6
#> 18 Sensitive 12 post 6
#> 19 Sensitive 13 pre 7
#> 20 Sensitive 13 post 7
#> 21 Sensitive 14 pre 8
#> 22 Sensitive 14 post 8
#> 23 Sensitive 15 pre 9
#> 24 Sensitive 15 post 9
#> 25 Sensitive 16 pre 10
#> 26 Sensitive 16 post 10
#> 27 Sensitive 17 pre 11
#> 28 Sensitive 17 post 11
#> 29 Sensitive 18 pre 12
#> 30 Sensitive 18 post 12
vd <- VisualizeDesign(
sampleData = sampleData,
designFormula = ~ Response + Response:ind.n + Response:Treatment,
textSizeFitted = 3
)
cowplot::plot_grid(plotlist = vd$plotlist, ncol = 1)
app <- ExploreModelMatrix(
sampleData = sampleData,
designFormula = ~ Response + Response:ind.n + Response:Treatment
)
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#> The `name` provided ('hand-o-right') does not correspond to a known icon
#> The `name` provided ('question-circle fa-1g') does not correspond to a known icon
if (interactive()) shiny::runApp(app)
The design above doesn’t allow comparison between Resistant and Sensitive patients while accounting for the patient effect, since the patient is nested within the response group. If we choose to ignore the patient effect, we can fit a factorial model with the design formula ~ Treatment + Response, as illustrated below.
vd <- VisualizeDesign(sampleData = sampleData,
designFormula = ~ Treatment + Response,
textSizeFitted = 4)
cowplot::plot_grid(plotlist = vd$plotlist, ncol = 1)
Example 3
From https://support.bioconductor.org/p/80408/. Here we are considering mice from two conditions (ctrl/ko), each measured with and without treatment with a drug (plus/minus). We use the design ~ 0 + batch + condition (where batch corresponds to the mouse ID), and drop the column corresponding to conditionko_minus to get a full-rank design matrix.
(sampleData = data.frame(
condition = factor(rep(c("ctrl_minus", "ctrl_plus",
"ko_minus", "ko_plus"), 3)),
batch = factor(rep(1:6, each = 2))))
#> condition batch
#> 1 ctrl_minus 1
#> 2 ctrl_plus 1
#> 3 ko_minus 2
#> 4 ko_plus 2
#> 5 ctrl_minus 3
#> 6 ctrl_plus 3
#> 7 ko_minus 4
#> 8 ko_plus 4
#> 9 ctrl_minus 5
#> 10 ctrl_plus 5
#> 11 ko_minus 6
#> 12 ko_plus 6
vd <- VisualizeDesign(sampleData = sampleData,
designFormula = ~ 0 + batch + condition,
textSizeFitted = 4, lineWidthFitted = 20,
dropCols = "conditionko_minus")
cowplot::plot_grid(plotlist = vd$plotlist, ncol = 1)
app <- ExploreModelMatrix(sampleData = sampleData,
designFormula = ~ batch + condition)
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if (interactive()) shiny::runApp(app)
