dot-synthesis: Internal function for generating synthetic single-cell data...
In RScelestial: Scelestial: Steiner Tree Based Single-Cell Lineage Tree Inference

Internal function for generating synthetic single-cell data through simulation of tumor growth and evolution.

.synthesis(
  sample,
  site,
  evolutionSteps,
  mutationRate = 0.01,
  advantageIncreaseRatio = 1,
  advantageDecreaseRatio = 10,
  advantageKeepRatio = 100,
  advantageIncreaseStep = 0.01,
  advantageDecreaseStep = 0.01,
  mvRate = 0.5,
  fpRate = 0.2,
  fnRate = 0.1,
  seed = -1L
)

`sample`	Number of samples
`site`	Number of sites
`evolutionSteps`	Number of non-root nodes in the evolutionary tree to be generated.
`mutationRate`	The rate of mutation on each evolutionary step in evolutionary tree synthesis.
`advantageIncreaseRatio, advantageDecreaseRatio, advantageKeepRatio`	A child node in the evolutionary tree is chosen for increase/decrease/keep its parent advantage with probabilities proportional to `advantage.increase.ratio`/`advantage.decrease.ratio`/`advantage.keep.ratio`.
`advantageIncreaseStep, advantageDecreaseStep`	The amount of increasing or decreasing the advantage of a cell relative to its parent.
`mvRate`	Rate of missing value to be added to the resulting sequences.
`fpRate, fnRate`	Rate of false positive (0 -> 1) and false negative (1 -> 0) in the sequences.
`seed`	The seed for randomization.

The function returns a list. The list consists of

sequence: A data frame representing result of sequencing. The data frame has a row for each locus and a column for each sample.
true.sequence: The actual sequence for the sample before adding errors and missing values.
true.clone: A list that stores index of sampled cells for each node in the evolutionary tree.
true.tree: The evolutionary tree that the samples are sampled from. It is a data frame with src, dest, and len columns representing source, destination and weight of edges of the tree, respectively.