DENDRO.simulation: Simulation DENDRO performance
In zhouzilu/DENDRO: Dna based EvolutioNary tree preDiction by scRna-seq technOlogy

DENDRO.simulation assess the DENDRO performance given an imaginary clonal tree with cell numbers, read depth etc. See argument below.

1	DENDRO.simulation(kprob = NULL, lprob = NULL, filt = 0, m = 100, n = 1000, epi = 0.001, RD = NULL, ref, k = NULL, subtype = 1, rpt = 100, plot = TRUE)

`kprob`	Distribution of cells in each subclone. The order of probability in kprob see tutorial. Default NULL, indicates uniform distribution of cells.
`lprob`	Distribution of mutations along each branch in the tree. The order of probability in lprob see tutorial. Default NULL, indicates uniform distribution of mutations.
`filt`	filt threashold indicates mutations with less than filt mutations are filtered out.
`m`	m indicates number of cells in your simulation. Default 100
`n`	n indicates number of genes in your simulation. Default 1000
`epi`	Sequencing error and rare RNA editing combined rate. Default 0.001 according to Illunima.
`RD`	RD indicates read depth of the overall sequencing. Default uses data from Deng et al. 2014, which is 45X with 10,000,000 reads per cell.
`ref`	The reference dataset, with X1 and X2 matrices, indicating two individual allele read counts.
`k`	When kprob is NULL, this will decide the total number of subclones. Combined with subtype, they will determine the clonal tree structure. Default NULL.
`subtype`	When k=4, there are two different clonal tree structure. This will determine which one is it. Default 1
`rpt`	How many round of simulation do you want to run to generate the estimation and confidence interval. Default 100.
`plot`	Whether you want to plot out the simulation example tree and results.