samplePop: Obtain a sample from a population of simulations.

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/OncoSimulR.R


Obtain a sample (a matrix of individuals/samples by genes or, equivalently, a vector of "genotypes") from an oncosimulpop object (i.e., a simulation of multiple individuals) or a single oncosimul object. Sampling schemes include whole tumor and single cell sampling, and sampling at the end of the tumor progression or during the progression of the disease.

sampledGenotypes shows the genotype frequencies from that sample; Shannon's diversity —entropy— of the genotypes is also returned. Order effects are ignored.


samplePop(x, timeSample = "last", typeSample = "whole",
          thresholdWhole = 0.5, geneNames = NULL, popSizeSample = NULL,
          propError = 0)

sampledGenotypes(y, genes = NULL)



An object of class oncosimulpop or class oncosimul2 (a single simulation).


The output from a call to samplePop.


"last" means to sample each individual in the very last time period of the simulation. "unif" (or "uniform") means sampling each individual at a time choosen uniformly from all the times recorded in the simulation between the time when the first driver appeared and the final time period. "unif" means that it is almost sure that different individuals will be sampled at different times. "last" does not guarantee that different individuals will be sampled at the same time unit, only that all will be sampled in the last time unit of their simulation.

You can, alternatively, specify the population size at which you want the sample to be taken. See argument popSizeSample.


"singleCell" (or "single") for single cell sampling, where the probability of sampling a cell (a clone) is directly proportional to its population size. "wholeTumor" (or "whole") for whole tumor sampling (i.e., this is similar to a biopsy being the entire tumor). "singleCell-noWT" or "single-nowt" is single cell sampling, but excluding the wild type.


In whole tumor sampling, whether a gene is detected as mutated depends on thresholdWhole: a gene is considered mutated if it is altered in at least thresholdWhole proportion of the cells in that individual.


An optional vector of gene names so as to label the column names of the output.


An optional vector of total population sizes at which you want the samples to be taken. If you pass this vector, timeSample has no effect. The samples will be taken at the first time at which the population size gets as large as (or larger than) the size specified in popSizeSample.

This allows you to specify arbitrary sampling schemes with respect to total population size.


The proportion of observations with error (for instance, genotyping error). If larger than 0, this proportion of entries in the sampled matrix will be flipped (i.e., 0s turned to 1s and 1s turned to 0s).


If non-NULL, use only the genes in genes to create the table of genotypes. This can be useful if you only care about the genotypes with respect to a subset of genes (say, X), and want to collapse with respect to another subset of genes (say, Y), for instance if Y is a large set of passenger genes. For example, suppose the complete set of genes is 'a', 'b', 'c', 'd', and you specify genes = c('a', 'b'); then, genotypes 'a, b, c' and genotypes 'a, b, d' will not be shown as different rows in the table of frequencies. Likewise, genotypes 'a, c' and genotypes 'a, d' will not be shown as different rows. Of course, if what are actually different genotypes are not regarded as different, this will affect the calculation of the diversity.


samplePop simply repeats the sampling process in each individual of the oncosimulpop object.

Please see oncoSimulSample for a much more efficient way of sampling when you are sure what you want to sample.

Note that if you have set onlyCancer = FALSE in the call to oncoSimulSample, you can end up trying to sample from simulations where the population size is 0. In this case, you will get a vector/matrix of NAs and a warning.

Similarly, when using timeSample = "last" you might end up with a vector of 0 (not NAs) because you are sampling from a population that contains no clones with mutated genes. This event (sampling from a population that contains no clones with mutated genes), by construction, cannot happen when timeSample = "unif" as "uniform" sampling is taken here to mean sampling at a time choosen uniformly from all the times recorded in the simulation between the time when the first driver appeared and the final time period. However, you might still get a vector of 0, with uniform sampling, if you sample from a population that contains only a few cells with any mutated genes, and most cells with no mutated genes.


A matrix. Each row is a "sample genotype", where 0 denotes no alteration and 1 alteration. When using v.2, columns are named with the gene names.

We quote "sample genotype" because when not using single cell, a row (a sample genotype) need not be, of course, any really existing genotype in a population as we are genotyping a whole tumor. Suppose there are really two genotypes present in the population, genotype A, which has gene A mutated and genotype B, which has gene B mutated. Genotype A has a frequency of 60% (so B's frequency is 40%). If you use whole tumor sampling with thresholdWhole = 0.4 you will obtain a genotype with A and B mutated.

For sampledGenotypes a data frame with two columns: genotypes and frequencies. This data frame has an additional attribute, "ShannonI", where Shannon's index of diversity (entropy) is stored. This is an object of class "sampledGenotypes" with an S3 print method.


Ramon Diaz-Uriarte


Diaz-Uriarte, R. (2015). Identifying restrictions in the order of accumulation of mutations during tumor progression: effects of passengers, evolutionary models, and sampling

See Also

oncoSimulPop, oncoSimulSample


p705 <- examplePosets[["p705"]]

## (I set mc.cores = 2 to comply with --as-cran checks, but you
##  should either use a reasonable number for your hardware or
##  leave it at its default value).

p1 <- oncoSimulPop(4, p705, mc.cores = 2)
(sp1 <- samplePop(p1))

## Sample at fixed sizes. Notice the requested size
## for the last population is larger than the any population size
## so we get NAs

(sp2 <- samplePop(p1, popSizeSample = c(1e7, 1e6, 4e5, 1e13)))

## Now single cell sampling

r1 <- oncoSimulIndiv(p705)
samplePop(r1, typeSample = "single")

sampledGenotypes(samplePop(r1, typeSample = "single"))

OncoSimulR documentation built on Nov. 8, 2020, 8:31 p.m.