rfitness {OncoSimulR} | R Documentation |
Generate random fitness landscapes under a House of Cards, Rough Mount Fuji, additive model, and Kauffman's NK model.
rfitness(g, c = 0.5, sd = 1, mu = 1, reference = "random", scale = NULL, wt_is_1 = c("subtract", "divide", "force", "no"), log = FALSE, min_accessible_genotypes = NULL, accessible_th = 0, truncate_at_0 = TRUE, K = 1, r = TRUE, model = c("RMF", "NK"))
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 two-element vector. If a
two-element vector, fitness is re-scaled between |
wt_is_1 |
If "divide" 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" (the default) 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, log-transform 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 (1e-9). 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. |
K |
K for NK model; K is the number of loci with which each locus interacts, and the larger the K the larger the ruggedness of the landscape. |
r |
For the NK model, whether interacting loci are chosen at random
( |
model |
One of "RMF" (default), for Rough Mount Fuji, or "NK", for Kauffman's NK model. |
When using model = "RMF"
, 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.
When using model = "NK"
, the model used is Kauffman's NK model
(see details in Ferretti et al., or Brouillet et al., below), as
implemented in MAGELLAN
(http://wwwabi.snv.jussieu.fr/public/Magellan/). This fitness
landscape is generated by directly calling the fl_generate
function of MAGELLAN. Fitness is drawn from a uniform (0, 1)
distribution.
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/not-mutated. (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.
MAGELLAN uses its own random number generating functions; using
set.seed
does not allow to obtain the same fitness landscape
repeatedly.
Ramon Diaz-Uriarte for the RMF and general wrapping code. S. Brouillet, G. Achaz, S. Matuszewski, H. Annoni, and L. Ferreti for the MAGELLAN code.
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.
Brouillet, S. et al. (2015). MAGELLAN: a tool to explore small fitness landscapes. bioRxiv, 31583. http://doi.org/10.1101/031583
Ferretti, L., Schmiegelt, B., Weinreich, D., Yamauchi, A., Kobayashi, Y., Tajima, F., & Achaz, G. (2016). Measuring epistasis in fitness landscapes: The correlation of fitness effects of mutations. Journal of Theoretical Biology\/, 396, 132–143. https://doi.org/10.1016/j.jtbi.2016.01.037
MAGELLAN web site: http://wwwabi.snv.jussieu.fr/public/Magellan/
oncoSimulIndiv
,
plot.genotype_fitness_matrix
,
evalAllGenotypes
allFitnessEffects
plotFitnessLandscape
## Random fitness for four genes-genotypes, ## plotting and simulating an oncogenetic trajectory r1 <- rfitness(4) plot(r1) oncoSimulIndiv(allFitnessEffects(genotFitness = r1)) ## NK model rnk <- rfitness(5, K = 3, model = "NK") plot(rnk) oncoSimulIndiv(allFitnessEffects(genotFitness = rnk))