ClonEvol

Inferring and visualizing clonal evolution in multi-sample cancer sequencing

##Installation

###Requirements:

• R 3.0.2 or later

###Install ClonEvol

install.packages("devtools")
library(devtools)
install_github("hdng/clonevol")

###Install dependencies

install.packages("ggplot2")
install.packages("igraph")

##Run ClonEvol

ClonEvol infers clonal evolution models in single sample or multiple samples using the clusters of variants identified previously using other methods such as sciClone or PyClone.

###Prepare input file An input file typically has the following columns (* indicates mandatory):

1. cluster*: the cluster identity of the variant (make sure do not name cluster as “-1”. This value is reserved for ClonEvol internal use.)
2. sample1.VAF*: VAF of the variant in sample1
3. sample1.Depth: depth of the variant in sample1
4. sample2.VAF: VAF of the variant in sample2
5. sample2.Depth: depth of the variant in sample2
6. Additinal samples' VAF and depth columns
7. Additional variant annotation columns

Example input file:

cluster	prim.vaf	met1.vaf	met2.vaf
1	51	44	52
1	45	56	47
1	55	50	49
2	31	47	0
2	28	38	0
2	31	45	0
2	30	47	0
2	31	53	0
2	38	48	0
...

###Run ClonEvol

You can read your data into a data frame (eg. using read.table). Here let's use AML1 data (Ding et al., 2012) included in ClonEvol.

Load AML1 data

library(clonevol)
data(aml1)
vaf.col.names <- grep(".vaf", colnames(aml1), value=TRUE)

Infer clonal evolution models

x <- infer.clonal.models(variants=aml1,
            cluster.col.name="cluster",
            vaf.col.names=vaf.col.names,
            subclonal.test="bootstrap",
            subclonal.test.model="non-parametric",
            cluster.center="mean",
            num.boots=1000,
            founding.cluster=1,
            min.cluster.vaf=0.01,
            p.value.cutoff=0.01,
            alpha=0.1,
            random.seed=63108)

Plot clonal evolution models

plot.clonal.models(x$models,
                   matched=x$matched,
                   variants=aml1,
                   box.plot=TRUE,
                   out.format="pdf",
                   overwrite.output=TRUE,
                   scale.monoclonal.cell.frac=TRUE,
                   cell.frac.ci=TRUE,
                   tree.node.shape="circle",
                   tree.node.size=40,
                   tree.node.text.size=0.65,
                   width=8, height=5,
                   out.dir="output")

Plot clonal evolution models (with variant highlight in polygon plots)

var.to.highlight = aml1[aml1$is.cancer.gene, c("cluster", "gene")]
colnames(var.to.highlight) = c("cluster", "variant.name")
plot.clonal.models(x$models,
                   matched=x$matched,
                   variants=aml1,
                   box.plot=TRUE,
                   out.format="pdf",
                   overwrite.output=TRUE,
                   scale.monoclonal.cell.frac=TRUE,
                   cell.frac.ci=TRUE,
                   variants.to.highlight=var.to.highlight,
                   variant.color="blue",
                   variant.angle=60,
                   tree.node.shape="circle",
                   tree.node.size=40,
                   tree.node.text.size=0.65,
                   width=8, height=5,
                   out.dir="output")

Output should look like this:

Plot box/violin/jitter of VAFs with cancer gene variants highlighted

num.clusters <- length(unique(aml1$cluster))
pdf("variants.jitter.pdf", width=5, height=5, useDingbats=FALSE)
pp = variant.box.plot(aml1,
                 vaf.col.names=vaf.col.names,
                 variant.class.col.name=NULL,
                 cluster.axis.name="",
                 vaf.limits=70,
                 violin=FALSE,
                 box=FALSE,
                 order.by.total.vaf=FALSE,
                 jitter=TRUE,
                 jitter.center.method="mean",
                 jitter.center.size=0.5,
                 jitter.center.color="darkgray",
                 jitter.shape=1,
                 jitter.color=get.clonevol.colors(num.clusters),
                 jitter.size=2,
                 jitter.alpha=1,
                 highlight="is.cancer.gene",
                 highlight.note.col.name="gene",
                 highlight.shape=19,
                 display.plot=TRUE)
dev.off()

Output figure should look like this:

Plot pairwise VAFs across samples

plot.pairwise(aml1, col.names=vaf.col.names,
                  out.prefix="variants.pairwise.plot",
                  colors=get.clonevol.colors(num.clusters))

Plot mean/median of clusters across samples (cluster flow)

plot.cluster.flow(aml1, vaf.col.names=vaf.col.names,
                      sample.names=c("Primary", "Relapse"),
                      out.file="flow.pdf",
                      colors=get.clonevol.colors(num.clusters))

##References

• Ding, Li, et al. "Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing." Nature 481.7382 (2012): 506-510.

##Contact Ha X. Dang @ haxdang (at) gmail (dot) com