Customizing oncoplots
Anand Mayakonda
2020-04-13
#path to TCGA LAML MAF file
laml.maf = system.file('extdata', 'tcga_laml.maf.gz', package = 'maftools')
#clinical information containing survival information and histology. This is optional
laml.clin = system.file('extdata', 'tcga_laml_annot.tsv', package = 'maftools')
laml = read.maf(maf = laml.maf,
clinicalData = laml.clin,
verbose = FALSE)
0.2 Changing colors for variant classifications
#One can use any colors, here in this example color palette from RColorBrewer package is used
vc_cols = RColorBrewer::brewer.pal(n = 8, name = 'Paired')
names(vc_cols) = c(
'Frame_Shift_Del',
'Missense_Mutation',
'Nonsense_Mutation',
'Multi_Hit',
'Frame_Shift_Ins',
'In_Frame_Ins',
'Splice_Site',
'In_Frame_Del'
)
print(vc_cols)
#> Frame_Shift_Del Missense_Mutation Nonsense_Mutation Multi_Hit
#> "#A6CEE3" "#1F78B4" "#B2DF8A" "#33A02C"
#> Frame_Shift_Ins In_Frame_Ins Splice_Site In_Frame_Del
#> "#FB9A99" "#E31A1C" "#FDBF6F" "#FF7F00"
oncoplot(maf = laml, colors = vc_cols, top = 10)
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.
#GISTIC results LAML
all.lesions =
system.file("extdata", "all_lesions.conf_99.txt", package = "maftools")
amp.genes =
system.file("extdata", "amp_genes.conf_99.txt", package = "maftools")
del.genes =
system.file("extdata", "del_genes.conf_99.txt", package = "maftools")
scores.gis =
system.file("extdata", "scores.gistic", package = "maftools")
#Read GISTIC results along with MAF
laml.plus.gistic = read.maf(
maf = laml.maf,
gisticAllLesionsFile = all.lesions,
gisticAmpGenesFile = amp.genes,
gisticDelGenesFile = del.genes,
gisticScoresFile = scores.gis,
isTCGA = TRUE,
verbose = FALSE,
clinicalData = laml.clin
)
This plot shows frequent deletions in TP53 gene which is located on one of the significantly deleted locus 17p13.2.
0.3.2 Custom copy-number table
In case there is no GISTIC results available, one can generate a table containing CN status for known genes in known samples. This can be easily created and read along with MAF file.
For example lets create a dummy CN alterations for DNMT3A in random 20 samples.
set.seed(seed = 1024)
barcodes = as.character(getSampleSummary(x = laml)[,Tumor_Sample_Barcode])
#Random 20 samples
dummy.samples = sample(x = barcodes,
size = 20,
replace = FALSE)
#Genarate random CN status for above samples
cn.status = sample(
x = c('Amp', 'Del'),
size = length(dummy.samples),
replace = TRUE
)
custom.cn.data = data.frame(
Gene = "DNMT3A",
Sample_name = dummy.samples,
CN = cn.status,
stringsAsFactors = FALSE
)
head(custom.cn.data)
#> Gene Sample_name CN
#> 1 DNMT3A TCGA-AB-2898 Amp
#> 2 DNMT3A TCGA-AB-2879 Amp
#> 3 DNMT3A TCGA-AB-2920 Del
#> 4 DNMT3A TCGA-AB-2866 Amp
#> 5 DNMT3A TCGA-AB-2892 Amp
#> 6 DNMT3A TCGA-AB-2863 Amp
laml.plus.cn = read.maf(maf = laml.maf,
cnTable = custom.cn.data,
verbose = FALSE)
oncoplot(maf = laml.plus.cn, top = 5)
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.
#MutSig results
laml.mutsig = system.file("extdata", "LAML_sig_genes.txt.gz", package = "maftools")
oncoplot(
maf = laml,
mutsig = laml.mutsig,
mutsigQval = 0.01,
)
0.5 Including expression (or any) values
Similar to significance values included as right bar plot, it’s also possible to include expression (or any sort of continuous) data to left side of the plot
#Dummy expression values for top 20 genes
set.seed(seed = 1024)
exprs_tbl = data.frame(genes = getGeneSummary(x = laml)[1:20, Hugo_Symbol],
exprn = rnorm(n = 10, mean = 12, sd = 5))
head(exprs_tbl)
#> genes exprn
#> 1 FLT3 8.106686
#> 2 DNMT3A 10.052618
#> 3 NPM1 1.831008
#> 4 IDH2 7.088134
#> 5 IDH1 13.239450
#> 6 TET2 1.480677
oncoplot(maf = laml, exprsTbl = exprs_tbl)
0.6 Including annotations
Annotations are usually stored in clinical.data slot of MAF.
getClinicalData(x = laml)
#> Tumor_Sample_Barcode FAB_classification days_to_last_followup
#> 1: TCGA-AB-2802 M4 365
#> 2: TCGA-AB-2803 M3 792
#> 3: TCGA-AB-2804 M3 2557
#> 4: TCGA-AB-2805 M0 577
#> 5: TCGA-AB-2806 M1 945
#> ---
#> 189: TCGA-AB-3007 M3 1581
#> 190: TCGA-AB-3008 M1 822
#> 191: TCGA-AB-3009 M4 577
#> 192: TCGA-AB-3011 M1 1885
#> 193: TCGA-AB-3012 M3 1887
#> Overall_Survival_Status
#> 1: 1
#> 2: 1
#> 3: 0
#> 4: 1
#> 5: 1
#> ---
#> 189: 0
#> 190: 1
#> 191: 1
#> 192: 0
#> 193: 0Include FAB_classification from clinical data as one of the sample annotations.
More than one annotations can be included by passing them to the argument clinicalFeatures. Above plot can be further enhanced by sorting according to annotations. Custom colors can be specified as a list of named vectors for each levels.
#Color coding for FAB classification
fabcolors = RColorBrewer::brewer.pal(n = 8,name = 'Spectral')
names(fabcolors) = c("M0", "M1", "M2", "M3", "M4", "M5", "M6", "M7")
fabcolors = list(FAB_classification = fabcolors)
print(fabcolors)
#> $FAB_classification
#> M0 M1 M2 M3 M4 M5 M6 M7
#> "#D53E4F" "#F46D43" "#FDAE61" "#FEE08B" "#E6F598" "#ABDDA4" "#66C2A5" "#3288BD"
oncoplot(
maf = laml,
clinicalFeatures = 'FAB_classification',
sortByAnnotation = TRUE,
annotationColor = fabcolors
)
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.
getFields(x = laml)
#> [1] "Hugo_Symbol" "Entrez_Gene_Id" "Center"
#> [4] "NCBI_Build" "Chromosome" "Start_Position"
#> [7] "End_Position" "Strand" "Variant_Classification"
#> [10] "Variant_Type" "Reference_Allele" "Tumor_Seq_Allele1"
#> [13] "Tumor_Seq_Allele2" "Tumor_Sample_Barcode" "Protein_Change"
#> [16] "i_TumorVAF_WU" "i_transcript_name"0.8 Group by Pathways
0.8.1 Auto grouping
Genes can be auto grouped based on their Biological processess by setting pathways = 'auto'
0.8.2 Custom pathways
A custom pathway list can also be provided in the form of a two column tsv file or a data.frame containing gene names and their corresponding pathway.
my_path = data.table::data.table(Gene = c("TP53", "WT1", "DNMT3A", "IDH1", "IDH2", "RUNX1", "CEBPA"), Pathway = c("Tumor Suppressors", "DNA methylation", 'DNA methylation', 'DNA methylation', 'DNA methylation', 'Myeloid TF', 'Myeloid TF'))
print(my_path)
#> Gene Pathway
#> 1: TP53 Tumor Suppressors
#> 2: WT1 DNA methylation
#> 3: DNMT3A DNA methylation
#> 4: IDH1 DNA methylation
#> 5: IDH2 DNA methylation
#> 6: RUNX1 Myeloid TF
#> 7: CEBPA Myeloid TF
oncoplot(maf = laml, top = 20, pathways = my_path, gene_mar = 6, fontSize = 0.6)
0.9 Combining everything
oncoplot(
maf = laml.plus.gistic,
draw_titv = TRUE,
clinicalFeatures = c('FAB_classification', 'Overall_Survival_Status'),
additionalFeature = c("Tumor_Seq_Allele2", "C"),
sortByAnnotation = TRUE,
mutsig = laml.mutsig,
exprsTbl = exprs_tbl,
logColBar = TRUE
)
0.10 SessionInfo
sessionInfo()
#> R version 4.0.0 alpha (2020-03-31 r78116)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server 2012 R2 x64 (build 9600)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] stats4 parallel stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] pheatmap_1.0.12 doParallel_1.0.15
#> [3] iterators_1.0.12 foreach_1.5.0
#> [5] NMF_0.22.0 Biobase_2.47.3
#> [7] cluster_2.1.0 rngtools_1.5
#> [9] pkgmaker_0.31.1 registry_0.5-1
#> [11] BSgenome.Hsapiens.UCSC.hg19_1.4.3 BSgenome_1.55.4
#> [13] rtracklayer_1.47.0 Biostrings_2.55.7
#> [15] XVector_0.27.2 GenomicRanges_1.39.3
#> [17] GenomeInfoDb_1.23.16 IRanges_2.21.8
#> [19] S4Vectors_0.25.15 BiocGenerics_0.33.3
#> [21] maftools_2.3.30
#>
#> loaded via a namespace (and not attached):
#> [1] splines_4.0.0 R.utils_2.9.2
#> [3] assertthat_0.2.1 GenomeInfoDbData_1.2.2
#> [5] Rsamtools_2.3.7 yaml_2.2.1
#> [7] pillar_1.4.3 lattice_0.20-41
#> [9] glue_1.4.0 digest_0.6.25
#> [11] RColorBrewer_1.1-2 colorspace_1.4-1
#> [13] plyr_1.8.6 htmltools_0.4.0
#> [15] Matrix_1.2-18 R.oo_1.23.0
#> [17] pkgconfig_2.0.3 XML_3.99-0.3
#> [19] bibtex_0.4.2.2 zlibbioc_1.33.1
#> [21] purrr_0.3.3 xtable_1.8-4
#> [23] scales_1.1.0 BiocParallel_1.21.2
#> [25] tibble_3.0.0 ggplot2_3.3.0
#> [27] ellipsis_0.3.0 withr_2.1.2
#> [29] SummarizedExperiment_1.17.5 cli_2.0.2
#> [31] survival_3.1-12 magrittr_1.5
#> [33] crayon_1.3.4 mclust_5.4.6
#> [35] evaluate_0.14 R.methodsS3_1.8.0
#> [37] fansi_0.4.1 tools_4.0.0
#> [39] data.table_1.12.8 gridBase_0.4-7
#> [41] lifecycle_0.2.0 matrixStats_0.56.0
#> [43] stringr_1.4.0 munsell_0.5.0
#> [45] DelayedArray_0.13.12 compiler_4.0.0
#> [47] berryFunctions_1.18.2 rlang_0.4.5
#> [49] grid_4.0.0 RCurl_1.98-1.1
#> [51] bitops_1.0-6 rmarkdown_2.1
#> [53] gtable_0.3.0 codetools_0.2-16
#> [55] abind_1.4-5 reshape2_1.4.4
#> [57] R6_2.4.1 GenomicAlignments_1.23.2
#> [59] dplyr_0.8.5 knitr_1.28
#> [61] stringi_1.4.6 Rcpp_1.0.4.6
#> [63] vctrs_0.2.4 tidyselect_1.0.0
#> [65] wordcloud_2.6 xfun_0.12