sciclone: An R repository from dgaston

An R package for inferring the subclonal architecture of tumors

Installation instructions:

Both the 'sciClone' package and it's 'bmm' dependency can be installed by doing the following:

#install IRanges from bioconductor
source("http://bioconductor.org/biocLite.R")
biocLite("IRanges")
#install devtools if you don't have it already
install.packages("devtools")
library(devtools)
install_github("genome/bmm")
install_github("genome/sciClone")

If you prefer to build the package by hand, follow these steps:

Make sure that you have the dependencies from the CRAN and BioConductor repos: IRanges, rgl, RColorBrewer, ggplot2, grid, plotrix, methods
install the bmm package from https://github.com/genome/bmm

Download and build from source:

  git clone git@github.com:genome/sciclone.git
  cd sciclone/
  R CMD build sciClone
  R CMD INSTALL sciClone_1.0.7.tar.gz

Usage

library(sciClone)

#read in vaf data from three related tumors
#format is 5 column, tab delimited: 
#chr, pos, ref_reads, var_reads, vaf

v1 = read.table("data/vafs.tumor1.dat",header=T);
v2 = read.table("data/vafs.tumor2.dat",header=T);
v3 = read.table("data/vafs.tumor3.dat",header=T);

#read in regions to exclude (commonly LOH)
#format is 3-col bed
regions = read.table("data/exclude.loh")

#read in segmented copy number data
#4 columns - chr, start, stop, segment_mean   
cn1 = read.table("data/copy_number_tum1")
cn2 = read.table("data/copy_number_tum2")
cn3 = read.table("data/copy_number_tum3")

#set sample names
names = c("Sample1","Sample2","Sample3")


#Examples:
#------------------------------------
#1d clustering on just one sample
sc = sciClone(vafs=v1,
         copyNumberCalls=cn1,
         sampleNames=names[1],
         regionsToExclude=reg1)
#create output
writeClusterTable(sc, "results/clusters1")
sc.plot1d(sc,"results/clusters1.1d.pdf")

#------------------------------------
#2d clustering using two samples:
sc = sciClone(vafs=list(v1,v2),
              copyNumberCalls=list(cn1,cn2),
              sampleNames=names[1:2],
               regionsToExclude=regions)
#create output
writeClusterTable(sc, "results/clusters2")
sc.plot1d(sc,"results/clusters2.1d.pdf")
sc.plot2d(sc,"results/clusters2.2d.pdf")


#------------------------------------
#3d clustering using three samples:
sc = sciClone(vafs=list(v1,v2,v3),
              copyNumberCalls=list(cn1,cn2,cn3),
              sampleNames=names[1:3],
               regionsToExclude=regions)
#create output
writeClusterTable(sc, "results/clusters2")
sc.plot1d(sc,"results/clusters2.1d.pdf")
sc.plot2d(sc,"results/clusters2.2d.pdf")
sc.plot3d(sc, sc@sampleNames, size=700, outputFile="results/clusters3.3d.gif")

#This pattern generalizes up to N samples, except for plotting, which caps out at 3d for obvious reasons.

Visualization

single-tumor plot

2d comparison plot

3d comparison plot

Notes

Requires host system to have imagemagick installed before it can produce animated gif output of 3d plots.
Input formats described in more detail in the R documentation (see ?sciClone)

Accessory Scripts and Data

The sciClone-meta repo contains all data and scripts used to create the figures in the manuscript. It also contains a small suite of tests that demonstrate the capabilities of sciClone and verify that it is installed correctly.

Reference

Manuscript published at PLoS Computational Biology (doi:10.1371/journal.pcbi.1003665)

SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution

Christopher A. Miller^1*, Brian S. White^2*, Nathan D. Dees¹, John S. Welch^2,3, Malachi Griffith¹, Obi Griffith¹, Ravi Vij^2,3, Michael H. Tomasson^2,3, Timothy A. Graubert^2,3, Matthew J. Walter^2,3, William Schierding¹, Timothy J. Ley^1,2,3, John F. DiPersio^2,3, Elaine R. Mardis^1,3,4, Richard K. Wilson^1,3,4, and Li Ding^1,2,3,4

¹The Genome Institute

²Department of Medicine

³Siteman Cancer Center

⁴Department of Genetics Washington University, St. Louis, MO 63110, USA

^* These authors contributed equally to this work

dgaston/sciclone