This vignette was created to showcase additional cancers and also to highlight additional, less-known features of the package. Additional examples: TCGA-BLCA TARGET-AML TARGET-SKCM TCGA-PRAD

#Acute Myeloid Leukemia (AML) There are fewer TARGET datasets available than TCGA. We’ll do AML first. We’ve named our downloaded archive gdc_download_aml.tar.gz.

if(!dir.exists("extracted_aml_data")){dir.create("extracted_aml_data")}
untar("gdc_download_aml.tar.gz",exdir = "./extracted_aml_data")
target_files_aml<-list.files(path = "extracted_aml_data",pattern=glob2rx("*NormalVsPrimary.tsv"),recursive=T,full.names = T)
print(target_files_aml)
sample_aggregated_segvals_aml<-formSampleMatrixFromRawGDCData(tcga_files = target_files_aml,format = "TARGET")
saveRDS(sample_aggregated_segvals_aml,"aml_sample_matched_input_matrix.rds")

Now that we’ve created and saved the AML matrix, let’s visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations.

Reference: Tarlock et al.; Recurrent Copy Number Variants Are Highly Prevalent in Acute Myeloid Leukemia. Blood 2017; 130 (Supplement 1): 3800.).

Some of the bins are invariant and correlation requires that the standard deviation be a value other than zero (otherwise correlation cannot be calculated). We will remove them in a couple steps and transpose the matrix, making the columns the bins and the samples the rows.

sample_aggregated_segvals_aml<-readRDS("aml_sample_matched_input_matrix.rds")
invariant_bins<-which((sample_aggregated_segvals_aml[stringr::str_detect(rownames(sample_aggregated_segvals_aml),"chr7"),] %>% t() %>% as.data.frame() %>% sapply(sd))==0)
chr_7_mat<-sample_aggregated_segvals_aml[(stringr::str_detect(rownames(sample_aggregated_segvals_aml),"chr7") & rownames(sample_aggregated_segvals_aml) %in% setdiff(rownames(sample_aggregated_segvals_aml),names(invariant_bins))),] %>% t()

Now we’ll perform correlation on the plot.

chr_7_mat %>%  cor(use="pairwise.complete.obs",method="pearson") %>% 
  CNVScope::signedRescale(max_cap=1) %>%
  reshape2::melt()  %>%
  ggplot(aes(x=reshape2::colsplit(Var1,"_",c("chr","start","end"))$start,
             y=reshape2::colsplit(Var2,"_",c("chr","start","end"))$start,
             fill=value)) + geom_raster() +
  theme(axis.text.x = element_blank(),axis.text.y=element_blank(),axis.title = element_blank()) +
  ggplot2::scale_fill_gradient2(low = "blue", high = "red", midpoint = 0.5, limits = c(0, 1))
## Warning: Raster pixels are placed at uneven horizontal intervals and will be
## shifted. Consider using geom_tile() instead.
## Warning: Raster pixels are placed at uneven vertical intervals and will be
## shifted. Consider using geom_tile() instead.

We could then utilize our domain finding function to find the borders of domains in the matrix. There’s an obvious distruption near the left center of the matrix (see the blue streaks of anticorrelation?).

if((Sys.info()['sysname'] == "Linux" |
Sys.info()['sysname'] == "Windows")&requireNamespace("HiCseg",quietly = T)){
colnames(chr_7_mat)[CNVScope::getAsymmetricBlockIndices(cor(chr_7_mat,use="pairwise.complete.obs"))]
breakpoints<-colnames(chr_7_mat)[CNVScope::getAsymmetricBlockIndices(cor(chr_7_mat,use="pairwise.complete.obs"))] %>% stringr::str_split_fixed(string = .,pattern="_",n=3) %>% as.matrix() %>% .[,2] %>% as.numeric()
breakpoint_labels <- colnames(chr_7_mat)[CNVScope::getAsymmetricBlockIndices(cor(chr_7_mat,use="pairwise.complete.obs"))]
breakpoint_labels} else {
colnames(chr_7_mat)[CNVScope::getAsymmetricBlockIndices(cor(chr_7_mat,use="pairwise.complete.obs"),algorithm = "jointSeg",nb_change_max = round(min(dim(chr_7_mat))/5))$breakpoints_col]
breakpoints<-colnames(chr_7_mat)[CNVScope::getAsymmetricBlockIndices(cor(chr_7_mat,use="pairwise.complete.obs"),algorithm = "jointSeg",nb_change_max = round(min(dim(chr_7_mat))/5))$breakpoints_col] %>% stringr::str_split_fixed(string = .,pattern="_",n=3) %>% as.matrix() %>% .[,2] %>% as.numeric()
breakpoint_labels <- colnames(chr_7_mat)[CNVScope::getAsymmetricBlockIndices(cor(chr_7_mat,use="pairwise.complete.obs"),algorithm = "jointSeg",nb_change_max = round(min(dim(chr_7_mat))/5))$breakpoints_col]
breakpoint_labels  
}
## [1] "chr7_56000001_57000000"   "chr7_58000001_59000000"  
## [3] "chr7_62000001_63000000"   "chr7_110000001_111000000"
## [5] "chr7_159000001_159138663"

Now, we’ll make another plot, only labeling the breakpoints.

chr_7_mat %>%  cor(use="pairwise.complete.obs",method="pearson") %>% 
    CNVScope::signedRescale(max_cap=1) %>%
    reshape2::melt()  %>%
    ggplot(aes(x=reshape2::colsplit(Var1,"_",c("chr","start","end"))$start,
               y=reshape2::colsplit(Var2,"_",c("chr","start","end"))$start,
               fill=value)) + geom_raster() +
    theme(axis.text.x = element_text(angle=90, hjust=1),axis.text.y=element_blank(),axis.title = element_blank()) +
    scale_x_continuous(breaks=breakpoints,labels=breakpoint_labels) +
    ggplot2::scale_fill_gradient2(low = "blue", high = "red", midpoint = 0.5, limits = c(0, 1))
## Warning: Raster pixels are placed at uneven horizontal intervals and will be
## shifted. Consider using geom_tile() instead.
## Warning: Raster pixels are placed at uneven vertical intervals and will be
## shifted. Consider using geom_tile() instead.

Finally, let’s try our probability weighting function for the matrix and see if we can find clearer regions of association. We’ll also try another segmentation algorithm with the jointseg package. In most cases, you can achieve a definite speed increase using the parallel=T option. We have disabled it to build the vignette without warnings.

if(requireNamespace("smoothie",quietly=T)){
chr_7_probdist <- CNVScope::calcCNVKernelProbDist(cor(chr_7_mat,use="pairwise.complete.obs"),parallel=F)$percentile_matrix
js_breakpoints<-jointseg::jointSeg(chr_7_probdist,K=20)$bestBkp
js_breakpoint_labels<-colnames(chr_7_mat)[js_breakpoints]
} else{
  print("Please install smoothie in order to run this example.")
}

We’ll notice that using this combination of techniques, we’ve managed to pick up an AML driver between domain endpoints, in a large region where the association is less than expected (as determined by the calcCNVprobdist function). Within the region of 115 and 120Mb lies the MET gene, where an LOH was detected in the paper. It’s a pretty obvious signature, perhaps the most obvious in the whole plot.

Further, we suggest that there is an minor signal off the diagonal in the region of this and the location of BRAF (chr7:140419127-140624564, another recurrent CNV in the paper), associated with the border of a region near 16-24Mb.Within this area are the PMS(a tumor suppressor), RAC1 (oncogene), and ETV1 (oncogene). PMS2 alteration has been implicated in AML previously (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734905/).

We don’t suggest that this tool is perfect and will rapidly make clear all cancer drivers associated with CNVs, but we suggest that it can, with other tools, add to your investigative toolbox to substantiate known drivers and to elucidate new ones.

if(requireNamespace("smoothie",quietly=T)){
chr_7_probdist %>%  
#  CNVScope::signedRescale(max_cap=1) %>%
  reshape2::melt()  %>%
  ggplot(aes(x=Var1,
             y=Var2,
             fill=value)) + geom_tile() +
#  theme(axis.title = element_blank()) + #axis.text.x = element_blank(),axis.text.y=element_blank(),
    theme(axis.text.x = element_text(angle=90, hjust=1),
          axis.text.y = element_text(angle=0, hjust=1)
          ,axis.title = element_blank()) +
#    scale_x_continuous(breaks=js_breakpoints,labels=js_breakpoint_labels) +
#      scale_y_continuous(breaks=js_breakpoints,labels=js_breakpoint_labels) +
      scale_x_continuous(breaks=js_breakpoints,labels=js_breakpoint_labels) +
      scale_y_continuous(breaks=js_breakpoints,labels=js_breakpoint_labels) +

  ggplot2::scale_fill_gradient2(low = "blue", high = "red", midpoint = 0.5, limits = c(0, 1))
} else{
  print("Please install smoothie in order to run this example.")
}