README.md

SimCopy

SimCopy is an R package simulating the evolution of copy number profiles along a tree. It relies on the PhyloSim package for performing the simulations by encoding the genomic regions as sites in sequences and using modified processes acting on them. Please refer to the package manual for further details.

SimCopy was brought to you by the Goldman group from EMBL-EBI.

Download an install

The released packages are available from the release directory.

Building from source

The package can be built from the source by issuing make pkg on a *nix system. The building process need the standard unix tools, Perl and R with the R.oo, phylosim packages installed.

Examples

# The following tiny examples illustrate the
# effects of individual processes:    

# Load simcopy:
library(simcopy)

tree<-rcoal(2)  # We will use this tiny tree in the examples below.
rate<-0.08      # Common rate for the small examples.

## Simulating deletions and dealing with the results:
cat("\nSimulating deletions:\n")

# Construct a SimCopy object:
sc <- SimCopy(
    root.size=40,
    deletion=list(rate=rate, mean=2)
 )
# Run simulation:
res<-Simulate(sc, tree)

# Deal with the simulation results:
print(res$aln)  # print out the simulated alignment
print(res$cnh)  # print out the simulated copy number history
print(res$fasta)# print out the fasta alignment
summary(res$phylosim) # get the details of the PhyloSim object used for simulations
summary(res$processes[[1]]) # get the details of the deletion process
plot(res$processes[[1]])    # plot the distribution of deletion lengths

## Simulate duplications and print out the resulting alignment:
cat("\nSimulating duplications:\n")

# Construct a SimCopy object:
sc <- SimCopy(
    root.size=20,
    duplication=list(rate=rate, mean=2)
 )
print( Simulate(sc, tree)$aln )

## Simulate inverted duplications and print out the resulting alignment:
cat("\nSimulating inverted duplications:\n")

# Construct a SimCopy object:
sc <- SimCopy(
    root.size=20,
    inv.duplication=list(rate=rate, mean=2)
 )
print( Simulate(sc, tree)$aln )


## Simulate inversions and print out the resulting alignment:
cat("\nSimulating inversions:\n")

# Construct a SimCopy object:
sc <- SimCopy(
    root.size=20,
    inversion=list(rate=rate, mean=2)
 )
print( Simulate(sc, tree)$aln )

## Simulate translocations and print out the resulting alignment:
cat("\nSimulating translocations:\n")

# Construct a SimCopy object:
sc <- SimCopy(
    root.size=20,
    translocation=list(rate=rate, mean=2)
 )
print( Simulate(sc, tree)$aln )

## In the following simulation we will use all the processes above 
## and we will attempt to recover the topology using simple hierarchical
## clustering of the copy number profiles.

tree<-rcoal(6)
rate<-0.05
sc <- SimCopy(
    root.size=50,
    deletion=list(rate=rate, mean=2),
    duplication=list(rate=rate, mean=2),
    inv.duplication=list(rate=rate, mean=2),
    inversion=list(rate=rate, mean=2),
    translocation=list(rate=rate, mean=2)
 )
res<-Simulate(sc, tree, anc=FALSE) # discard internal nodes

# Print out the simulate genomic region alignment through
# the underlying PhyloSim object:
plot(res$phylosim)

# Calculate distances between copy number profiles:
d<-dist(res$cnh)

# Cluster the copy number profiles:
hc<-hclust(d)

# Relabel the tips of the true tree and plot it out:
tree$tip.label<-1:length(tree$tip.label)
plot(tree)

# Plot out the results of hierarchical clustering:
plot(hc)