0.1 Including Transition/Transversions into oncoplot

0.3 Including copy number data into oncoplots.

There are two ways one include CN status into MAF. 1. GISTIC results 2. Custom copy number table

0.3.1 GISTIC results

Most widely used tool for copy number analysis from large scale studies is GISTIC and we can simultaneously read gistic results along with MAF. GISTIC generates numerous files but we need mainly four files all_lesions.conf_XX.txt, amp_genes.conf_XX.txt, del_genes.conf_XX.txt, scores.gistic where XX is confidence level. These files contain significantly altered genomic regions along with amplified and deleted genes respectively.

This plot shows frequent deletions in TP53 gene which is located on one of the significantly deleted locus 17p13.2.

0.4 Including significance values

This data should be a data.frame or a tsv file with two required columns titled gene and q.

For example, including mutsig q values into oncoplot.

0.7 Highlighting samples

If you prefer to highlight mutations by a specific attribute, you can use additionalFeature argument.

Example: Highlight all mutations where alt allele is C.

Note that first argument (Tumor_Seq_Allele2) must a be column in MAF file, and second argument (C) is a value in that column. If you want to know what columns are present in the MAF file, use getFields.

0.9 SessionInfo

sessionInfo()
#> R version 3.6.1 (2019-07-05)
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