Fixes for documentations build under linux

R/plot_muller.R

@@ -49,7 +49,7 @@ collapse_loops <- function(df_edges) {
 #' sim$add_genotype(name = "A",
 #'                  epigenetic_rates = c("+-" = 0.01, "-+" = 0.01),
 #'                  growth_rates = c("+" = 0.2, "-" = 0.08),
-#'                  death_rates = c("+" = 0.1, "-" = 0.01))
+#'                  death_rates = c("+" = 0.02, "-" = 0.01))
 #' sim$history_delta = 1
 #' sim$place_cell("A+", 500, 500)
 #' sim$run_up_to_time(60)

src/drivers.cpp

@@ -831,11 +831,11 @@ List Simulation::get_cells() const
 //' sim$add_genotype(genotype = "A",
 //'                  epigenetic_rates = c("+-" = 0.01, "-+" = 0.01),
 //'                  growth_rates = c("+" = 0.2, "-" = 0.08),
-//'                  death_rates = c("+" = 0.1, "-" = 0.01))
+//'                  death_rates = c("+" = 0.02, "-" = 0.01))
 //' sim$add_genotype(genotype = "B",
 //'                  epigenetic_rates = c("+-" = 0.02, "-+" = 0.01),
 //'                  growth_rates = c("+" = 0.3, "-" = 0.1),
-//'                  death_rates = c("+" = 0.1, "-" = 0.01))
+//'                  death_rates = c("+" = 0.02, "-" = 0.01))
 //' sim$schedule_genotype_mutation(src = "A", dst = "B", time = 50)
 //' sim$place_cell("A+", 500, 500)
 //' sim$run_up_to_time(70)
@@ -1672,7 +1672,7 @@ void Simulation::mutate_progeny(const Races::Drivers::Simulation::AxisPosition&
 //' sim$add_genotype(genotype = "A",
 //'                  epigenetic_rates = c("+-" = 0.01, "-+" = 0.01),
 //'                  growth_rates = c("+" = 0.2, "-" = 0.08),
-//'                  death_rates = c("+" = 0.1, "-" = 0.01))
+//'                  death_rates = c("+" = 0.01, "-" = 0.01))
 //' sim$place_cell("A+", 500, 500)
 //' sim$run_up_to_time(70)
 //'

vignettes/a01_tissue_simulation.Rmd

@@ -59,6 +59,10 @@ unlink("Test", recursive = TRUE)
 sim <- new(Simulation, "Test")
 ```
 
+```{r echo=FALSE, results='hide', message=FALSE, warning=FALSE}
+sim$death_activation_level <- 50
+```
+
 Class `Simulation` exports a `show` method to get information on the
 current object.
 
@@ -199,7 +203,7 @@ thousands of cells.
 
 ```{r,  fig.height=4, fig.width=3}
 # Piechar for counts
-plot_state(sim) 
+plot_state(sim)
 
 # Spatial distribution for the whole tissue (hexagonal bins)
 plot_tissue(sim)
@@ -279,7 +283,7 @@ sim$get_clock()
 plot_tissue(sim)
 ```
 
-##### **Getting cells (advanced)**
+##### **Getting cells (Advanced)**
 
 At this point, if we query the simulation we will find more cells (we
 use `dplyr` to process query results). For convenience, the getters
@@ -337,7 +341,7 @@ therefore new species `B+` and `B-` - as descending from `A,`we need to:
     genotype `B`.
 
 ```{r}
-# We locate a random cell 
+# We locate a random cell
 cell <- sim$choose_cell_in("A")
 cell
 
@@ -374,7 +378,7 @@ plot_tissue(sim, num_of_bins = 250)
 # Facet on species via ggplot
 library(ggplot2)
 
-plot_tissue(sim) + facet_wrap(~species)
+plot_tissue(sim, num_of_bins = 250) + facet_wrap(~species)
 ```
 
 If, at this point in the simulation, we generate a new genotype `C` from
@@ -395,7 +399,7 @@ sim$run_up_to_time(sim$get_clock() + 10)
 
 ```{r,  fig.height=4, fig.width=3}
 plot_state(sim)
-plot_tissue(sim)
+plot_tissue(sim, num_of_bins = 250)
 ```
 
 ### Other operations
@@ -472,12 +476,14 @@ the state (by default, `history_delta` is set to $0$).
 
 We show this by re-simulating a tumour with two subclones.
 
-```{r}
+```{r echo=FALSE, results='hide', message=FALSE, warning=FALSE}
 # Cancel old data
 unlink(sim$get_name(), recursive = TRUE)
+```
 
+```{r}
 # Example time-series on a new simulation, with coarse-grained time-series
-sim <- new(Simulation)
+sim <- new(Simulation, "Finer Time Series")
 
 sim$history_delta <- 1
 sim$death_activation_level <- 100
@@ -517,7 +523,7 @@ sim$add_genotype(name = "D",
 
 sim$mutate_progeny(sim$choose_cell_in("A"), "D")
 
-sim$run_up_to_size("D+", 4000)
+sim$run_up_to_size("D+", 400)
 ```
 
 The time-series can be plot using `plot_timeseries`.
@@ -551,12 +557,12 @@ still, it help understand trends.
 
 ```{r,  fig.height=4, fig.width=6, message=FALSE, warning=FALSE}
 # Custom Mullers
-clock = sim$get_clock()
+clock <- sim$get_clock()
 
 plot_muller(sim) + ggplot2::xlim(clock * 3/4, clock)
 
-plot_muller(sim) + 
-  ggplot2::xlim(clock * 3/4, clock) + 
+plot_muller(sim) +
+  ggplot2::xlim(clock * 3/4, clock) +
   ggplot2::scale_y_log10()
 ```
 
@@ -573,24 +579,24 @@ and `B-`.
 # Current rates
 sim
 
-# Raise the death rate to very high levels
+# Raise the death rate levels
 sim$update_rates("B+", c(death = 3))
 sim$update_rates("B-", c(death = 3))
 sim$update_rates("C+", c(death = 3))
 sim$update_rates("C-", c(death = 3))
 
 # Now D will become larger
-sim$run_up_to_size("D+", 60000)
+sim$run_up_to_size("D+", 6000)
 
 # Current state
 sim
 
 # This now show the change in rates
-clock = sim$get_clock()
+clock <- sim$get_clock()
 plot_muller(sim) + ggplot2::xlim(clock * 3/4, clock)
 
-plot_muller(sim) + 
-  ggplot2::xlim(clock * 3/4, clock) + 
+plot_muller(sim) +
+  ggplot2::xlim(clock * 3/4, clock) +
   ggplot2::scale_y_log10()
 ```
 

vignettes/a02_sampling_simulation.Rmd

@@ -179,7 +179,6 @@ In general, these plots can be annotated with extra information, such as the sam
 # Full plot
 plot_forest(forest) %>%
   annotate_forest(forest)
-
 # S_1_1 plot
 plot_forest(S_1_1_forest) %>%
   annotate_forest(S_1_1_forest)
@@ -194,8 +193,7 @@ unlink(sim$get_name(), recursive = TRUE)
 
 
 ```{r}
-# Monoclonal model, no epigenotypes
-sim <- new(Simulation, "Monoclonal")
+sim <- new(Simulation, "Randomised")
 
 sim$add_genotype(name = "A", growth_rates = 0.1, death_rates = 0.01)
 
@@ -241,7 +239,9 @@ current +
   geom_rect(xmin = bbox$p[1], xmax = bbox$q[1],
             ymin = bbox$p[2], ymax = bbox$q[2],
             fill = NA, color = "black")
+```
 
+```{r}
 sim$sample_cells("S_2_1", bbox$p, bbox$q)
 ```
 
@@ -254,7 +254,9 @@ current +
   geom_rect(xmin = bbox$p[1], xmax = bbox$q[1],
             ymin = bbox$p[2], ymax = bbox$q[2],
             fill = NA, color = "black")
+```
 
+```{r}
 sim$sample_cells("S_2_2", bbox$p, bbox$q)
 ```
 
@@ -277,13 +279,19 @@ plot_forest(forest) %>%
 unlink(sim$get_name(), recursive = TRUE)
 ```
 
-## Example with two populations
+## Two populations with epigenetic state
+
+We are now ready to simulate a model with epigenetic
+switches and subclonal expansions.
 
-We can now simulate a rather more complex model, with epigenetic
-switches and subclonal expansions
+```{r echo=FALSE, results='hide', message=FALSE, warning=FALSE}
+unlink("Two Populations", recursive = TRUE)
+```
 
 ```{r,  fig.height=4, fig.width=3}
-sim <- new(Simulation, "TwoClones")
+sim <- new(Simulation, "Two Populations")
+
+sim$death_activation_level <- 20
 
 # First clone
 sim$add_genotype(name = "A",
@@ -310,7 +318,9 @@ sim$sample_cells("B1",
                  top_right = c(500 + bbox_width, 500 + bbox_width))
 
 plot_tissue(sim, num_of_bins = 500)
+```
 
+```{r,  fig.height=4, fig.width=3}
 # Let it grow a bit more
 sim$run_up_to_time(sim$get_clock() + 15)
 
@@ -368,11 +378,14 @@ visualisation.
 We build a tumour with three populations, branching.
 
 ```{r echo=FALSE, results='hide', message=FALSE, warning=FALSE}
-unlink("bbox_sampler_test", recursive = TRUE)
+unlink("Three Populations", recursive = TRUE)
 ```
 
-```{r}
-sim <- new(Simulation, "bbox_sampler_test", 1234)
+```{r, fig.height=4, fig.width=3}
+sim <- new(Simulation, "Three Populations")
+
+sim$death_activation_level <- 20
+
 sim$update_tissue(250, 250)
 
 sim$add_genotype(name = "A", growth_rates = 0.08, death_rates = 0.01)
@@ -393,10 +406,13 @@ n_w <- n_h <- 7
 bbox <- bbox_sampler(sim, "A", ncells, n_w, n_h)
 sim$sample_cells("A1", bbox$p, bbox$q)
 
-bbox <- bbox_sampler(sim, "A", ncells, n_w, n_h)
-sim$sample_cells("B1", bbox$p, bbox$q)
+bbox2 <- bbox_sampler(sim, "A", ncells, n_w, n_h)
+sim$sample_cells("B1", bbox2$p, bbox2$q)
 
 plot_tissue(sim)
+```
+
+```{r,  fig.height=4, fig.width=3, eval=FALSE}
 plot_muller(sim)
 ```
 
@@ -429,7 +445,7 @@ Finally, a third clone branching from `A`.
 
 ```{r,  fig.height=4, fig.width=3}
 # New subclone
-sim$add_genotype(name = "C", growth_rates = 0.24, death_rates = 0.01)
+sim$add_genotype(name = "C", growth_rates = 0.6, death_rates = 0.01)
 sim$mutate_progeny(sim$choose_cell_in("A"), "C")
 
 sim$run_up_to_size("C", 500)