RScelestial (version 1.0.4)

synthesis: Synthesize single-cell data through tumor simulation

Description

This function simulates a evolution in a tumor through two phases: 1) simulation of evolution, 2) sampling.

Usage

synthesis(
  sample,
  site,
  evolution.step,
  mutation.rate = 1,
  advantage.increase.ratio = 1,
  advantage.decrease.ratio = 10,
  advantage.keep.ratio = 100,
  advantage.increase.step = 0.01,
  advantage.decrease.step = 0.01,
  mv.rate = 0.5,
  fp.rate = 0.2,
  fn.rate = 0.1,
  seed = -1
)

Value

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.

Arguments

sample

Number of samples.

site

number of sites (loci)

evolution.step

Number of evolutionary steps in the process of production of the evolutionary tree.

mutation.rate

The rate of mutation on each evolutionary step in evolutionary tree synthesis.

advantage.increase.ratio, advantage.decrease.ratio, advantage.keep.ratio

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.

advantage.increase.step, advantage.decrease.step

The amount of increasing or decreasing the advantage of a cell relative to its parent.

mv.rate

Rate of missing value to be added to the resulting sequences.

fp.rate, fn.rate

Rate of false positive (0 -> 1) and false negative (1 -> 0) in the sequences.

seed

The seed for randomization.

Details

The simulation of evolution starts with a single cell. Then for evolution.step steps, on each step a cell is selected for duplication. A new cell as its child is added to the evolutionary tree. To each node in the evolutionary tree an advantage is assigned representing its relative advantage in replication and in being sampled. Advantage of a node is calculated by increasing (decreasing) its parents advantage by advantage.increase.step (advantage.decrease.step) with probability proportional to advantage.increase.ratio (advantage.decrease.ratio). With a probability proportional to advantage.keep.ratio the advantage of a node is equal to its parent's advantage.

Sequences for each node is build based on its parent's sequence by adding some mutations. Mutations are added for each locus independently with rate mutation.rate.

In the sampling phase, sample cells are selected from the evolutionary tree nodes. Result of the sequencing process for a cell is determined by the sequence of the node in the evolutionary tree with addition of some random errors. Errors are result of applying some false positives with rate fp.rate, applying some false negatives with rate fn.rate, and adding some missing values with rate mv.rate.

Examples

## generating a data set with 10 samples and 5 loci through simulation of
## 20-step evolution.
synthesis(10, 5, 20, seed=7)
## The result is
# $seqeunce
#     C1 C2 C3 C4 C5
# L1   1  1  1  1  1
# L2   3  1  3  3  0
# L3   3  1  3  3  1
# L4   3  0  1  0  0
# L5   1  3  0  3  3
# L6   3  1  3  1  0
# L7   3  3  1  0  3
# L8   3  1  1  3  3
# L9   3  3  1  3  1
# L10  0  3  0  3  0
#
# $true.sequence
#     C1 C2 C3 C4 C5
# L1   0  1  1  1  1
# L2   0  1  0  0  1
# L3   0  1  0  0  1
# L4   0  1  1  1  1
# L5   1  1  0  1  0
# L6   0  1  0  1  0
# L7   0  1  0  0  1
# L8   0  1  1  1  1
# L9   0  1  1  1  1
# L10  0  0  0  0  0
#
# $true.clone
# $true.clone[[1]]
# [1] 4
#
# $true.clone[[2]]
# [1] 1
#
# $true.clone[[3]]
# [1] 6
#
# $true.clone[[4]]
# [1] 10
#
# $true.clone[[5]]
# [1] 2
#
# $true.clone[[6]]
# [1] 3
#
# $true.clone[[7]]
# [1] 8 9
#
# $true.clone[[8]]
# [1] 7
#
# $true.clone[[9]]
# [1] 5
#
#
# $true.tree
#   src dest len
# 1   1    5   3
# 2   5    7   1
# 3   5   10   2
# 4   1   11   3
# 5   1   12   2
# 6   1   13   3
# 7   7   14   2
# 8  12   19   1
# 9  10   20   1
#