Description Usage Arguments Details Value Author(s) References See Also Examples
Generate random fitness landscapes under a House of Cards, Rough Mount Fuji, or additive model.
1 2 3 4 
g 
Number of genes. 
c 
The decrease in fitness of a genotype per each unit increase
in Hamming distance from the reference genotype (see 
sd 
The standard deviation of the random component (a normal
distribution of mean 
mu 
The mean of the random component (a normal distribution of
mean 
reference 
The reference genotype: for the deterministic, additive
part, this is the genotype with maximal fitness, and all other
genotypes decrease their fitness by 
scale 
Either NULL (nothing is done) or a twoelement vector. If a
twoelement vector, fitness is rescaled between 
wt_is_1 
If "divide" (the default) the fitness of all genotypes is
divided by the fitness of the wildtype (after possibly adding a value
to ensure no negative fitness) so that the wildtype (the genotype with
no mutations) has fitness 1. This is a case of scaling, and it is
applied after If "subtract" we shift all the fitness values (subtracting fitness of
the wildtype and adding 1) so that the wildtype ends up with a fitness
of 1. This is also applied after If "force" we simply set the fitness of the wildtype to 1, without any
divisions. This means that the If "none", the fitness of the wildtype is not touched. 
log 
If TRUE, logtransform fitness. 
min_accessible_genotypes 
If not NULL, the minimum number of
accessible genotypes in the fitness landscape. A genotype is
considered accessible if you can reach if from the wildtype by going
through at least one path where all changes in fitness are larger or
equal to If the condition is not satisfied, we continue generating random fitness landscapes with the specified parameters until the condition is satisfied. (Why check against NULL and not against zero? Because this allows you to count accessible genotypes even if you do not want to ensure a minimum number of accessible genotypes.) 
accessible_th 
The threshold for the minimal change in fitness at
each mutation step (i.e., between successive genotypes) that allows a
genotype to be regarded as accessible. This only applies if

truncate_at_0 
If TRUE (the default) any fitness <= 0 is substituted by a small positive constant (1e9). Why? Because MAGELLAN and some plotting routines can have trouble (specially if you log) with values <=0. Or we might have trouble if we want to log the fitness. 
The model used here follows the Rough Mount Fuji model in Szendro et al., 2013 or Franke et al., 2011. Fitness is given as
f(i) = c d(i, reference) + x_i
where d(i, j) is the Hamming distance between genotypes i
and j (the number of positions that differ) and x_i is a
random variable (in this case, a normal deviate of mean mu
and standard deviation sd
).
Setting c = 0 we obtain a House of Cards model. Setting sd = 0 fitness is given by the distance from the reference and if the reference is the genotype with all positions mutated, then we have a fully additive model (fitness increases linearly with the number of positions mutated).
For OncoSimulR, we often want the wildtype to have a mean of
1. Reasonable settings are mu = 1
and wt_is_1 =
'subtract'
so that we simulate from a distribution centered in 1, and
we make sure afterwards (via a simple shift) that the wildtype is
actuall 1. The sd
controls the standard deviation, with the
usual working and meaning as in a normal distribution, unless c
is different from zero. In this case, with c
large, the range
of the data can be large, specially if g
(the number of genes)
is large.
An matrix with g + 1
columns. Each column corresponds to a
gene, except the last one that corresponds to fitness. 1/0 in a gene
column denotes gene mutated/notmutated. (For ease of use in other
functions, this matrix has class "genotype_fitness_matrix".)
If you have specified min_accessible_genotypes > 0
, the return
object has added attributes accessible_genotypes
and
accessible_th
that show the number of accessible
genotypes under the specified threshold.
Ramon DiazUriarte
Szendro I.~G. et al. (2013). Quantitative analyses of empirical fitness landscapes. Journal of Statistical Mehcanics: Theory and Experiment\/, 01, P01005.
Franke, J. et al. (2011). Evolutionary accessibility of mutational pathways. PLoS Computational Biology\/, 7(8), 1–9.
oncoSimulIndiv
,
plot.genotype_fitness_matrix
,
evalAllGenotypes
allFitnessEffects
plotFitnessLandscape
1 2 3 4 5 6  ## Random fitness for four genesgenotypes,
## plotting and simulating an oncogenetic trajectory
r1 < rfitness(4)
plot(r1)
oncoSimulIndiv(allFitnessEffects(genotFitness = r1))

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.