In sigven/oncoEnrichR: Cancer-dedicated gene set interpretation
Tumor aberration frequencies
- The somatic mutation frequencies of the target genes in ~10,000 tumor samples have been calculated with data from The Cancer Genome Atlas (TCGA)
SNVs/InDels - oncoplots {.tabset}
- Frequency of somatic SNVs/InDels in the query set genes (top mutated) are illustrated with oncoplots
- Query set mutation frequencies are sorted by type of diagnosis (i.e. cancer subtypes)
- The frequency of transitions/transversions is also shown per sample
tcga_oncoplot_data <- onc_enrich_report[["data"]][["tcga"]][["aberration"]][["table"]][["snv_indel"]]
## oncoplot arguments
gene_mar <- 10
annotationFontSize <- 2.5
sepwd_genes <- 0.7
fontSize <- 1.5
legendFontSize <- 2.4
legend_height <- 5
anno_height <- 0.6
fheight <- 26
fwidth <- 18
for(n in names(tcga_oncoplot_data)){
tcga_oncoplot_data[[n]]$fheight <- fheight
n_genes <- NROW(tcga_oncoplot_data[[n]][['top_mutated_genes']])
if(n_genes < 40){
tcga_oncoplot_data[[n]]$fheight <- 24
}
if(n_genes < 30){
tcga_oncoplot_data[[n]]$fheight <- 21
}
if(n_genes < 20){
tcga_oncoplot_data[[n]]$fheight <- 17
}
if(n_genes < 10){
tcga_oncoplot_data[[n]]$fheight <- 14
}
}
Breast
code <- tcga_oncoplot_data[["Breast"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Breast"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Colon/Rectum
code <- tcga_oncoplot_data[["Colon/Rectum"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Colon/Rectum"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis","MSI_status"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Lung
code <- tcga_oncoplot_data[["Lung"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Lung"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T,
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Skin
code <- tcga_oncoplot_data[["Skin"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Skin"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Esophagus/Stomach
code <- tcga_oncoplot_data[["Esophagus/Stomach"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Esophagus/Stomach"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis","MSI_status"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Cervix
code <- tcga_oncoplot_data[["Cervix"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Cervix"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
draw_titv = T, bgCol = "gray93",
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Prostate
code <- tcga_oncoplot_data[["Prostate"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Prostate"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Ovary/Fallopian Tube
code <- tcga_oncoplot_data[["Ovary/Fallopian Tube"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Ovary/Fallopian Tube"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Uterus
code <- tcga_oncoplot_data[["Uterus"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Uterus"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis","MSI_status"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Pancreas
code <- tcga_oncoplot_data[["Pancreas"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Pancreas"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Soft Tissue
code <- tcga_oncoplot_data[["Soft Tissue"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Soft Tissue"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Myeloid
code <- tcga_oncoplot_data[["Myeloid"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Myeloid"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
CNS/Brain
code <- tcga_oncoplot_data[["CNS/Brain"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["CNS/Brain"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Liver
code <- tcga_oncoplot_data[["Liver"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Liver"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
draw_titv = T, bgCol = "gray93",
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Kidney
code <- tcga_oncoplot_data[["Kidney"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Kidney"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Lymphoid
code <- tcga_oncoplot_data[["Lymphoid"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Lymphoid"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Head and Neck
code <- tcga_oncoplot_data[["Head and Neck"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Head and Neck"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Biliary Tract
code <- tcga_oncoplot_data[["Biliary Tract"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Biliary Tract"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
draw_titv = T, bgCol = "gray93",
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Bladder/Urinary Tract
code <- tcga_oncoplot_data[["Bladder/Urinary Tract"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Bladder/Urinary Tract"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
draw_titv = T, bgCol = "gray93",
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Pleura
code <- tcga_oncoplot_data[["Pleura"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Pleura"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
draw_titv = T, bgCol = "gray93",
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Thyroid
code <- tcga_oncoplot_data[["Thyroid"]][['code']]
maf <- tcga_maf_datasets[[code]]
maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Thyroid"]][['top_mutated_genes']]$symbol,
drawRowBar = F, drawColBar = F,
sortByAnnotation = T,
draw_titv = T, bgCol = "gray93",
clinicalFeatures = c("Diagnosis"), showTitle = F,
gene_mar = gene_mar,
annotationFontSize = annotationFontSize,
sepwd_genes = sepwd_genes,
fontSize = fontSize,
legendFontSize = legendFontSize,
legend_height = legend_height,
anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
SNVs/InDels - recurrent variants
- Browse recurrent, protein-coding somatic SNVs/InDels from TCGA in the query set
- Variants are listed as one record per tissue/site, in effect making the same variant occurring in multiple rows
- Only variants with a site-specific frequency >= 2 are shown
- Variants can be filtered based on various properties, e.g. Site/tissue variant recurrence, as well as overall recurrence across all tumor sites (column Pan-cancer variant recurrence)
- Notably, each variant have been annotated/classified with a
- loss-of-function status, based on the LOFTEE plugin in VEP
- Status as somatic mutation hotspots in cancer, according to cancerhotspots.org. Format: \<GENE>|\<CODON>|\<AMINO_ACID_VARIANT>|\<Q-VALUE>
- Top 2,500 recurrent variants are listed here (all variants are listed in the Excel output of oncoEnrichR)
missing_recurrent_tcga_variants <- TRUE
if(nrow(onc_enrich_report[['data']][['tcga']][['recurrent_variants']]) > 0){
missing_recurrent_tcga_variants <- FALSE
}
vars <- onc_enrich_report[['data']][['tcga']][['recurrent_variants']] |>
dplyr::mutate(SITE_RECURRENCE = as.integer(SITE_RECURRENCE))
mutation_hotspot_levels <- levels(as.factor(
vars$MUTATION_HOTSPOT))
vars <- vars |>
dplyr::arrange(
dplyr::desc(SITE_RECURRENCE),
dplyr::desc(LOSS_OF_FUNCTION),
PROTEIN_CHANGE) |>
dplyr::mutate(CONSEQUENCE = stringr::str_replace_all(
CONSEQUENCE, "&", ", "
)) |>
head(2500)
if(length(mutation_hotspot_levels) > 0){
vars <- vars |>
dplyr::arrange(MUTATION_HOTSPOT, dplyr::desc(SITE_RECURRENCE))
}
recurrent_vars_tcga <- crosstalk::SharedData$new(
vars)
crosstalk::bscols(
list(
crosstalk::filter_select("SYMBOL","Gene symbol",recurrent_vars_tcga, ~SYMBOL),
crosstalk::filter_select("CONSEQUENCE", "Variant consequence", recurrent_vars_tcga, ~CONSEQUENCE),
crosstalk::filter_select("PRIMARY_SITE", "Primary site/tissue", recurrent_vars_tcga, ~PRIMARY_SITE),
crosstalk::filter_select("MUTATION_HOTSPOT",
"Cancer mutation hotspot",
recurrent_vars_tcga, ~MUTATION_HOTSPOT)
),
list(
crosstalk::filter_checkbox("LOSS_OF_FUNCTION", "Loss-of-function status",
recurrent_vars_tcga, ~LOSS_OF_FUNCTION),
crosstalk::filter_slider("TOTAL_RECURRENCE", "Pancancer variant recurrence",
recurrent_vars_tcga, ~TOTAL_RECURRENCE),
crosstalk::filter_slider("SITE_RECURRENCE", "Site/tissue variant recurrence",
recurrent_vars_tcga, ~SITE_RECURRENCE)
)
)
htmltools::br()
dt <- DT::datatable(recurrent_vars_tcga, escape = F,
extensions=c("Buttons","Responsive"),
width = "100%",
options=list(buttons = c('csv','excel'),dom = 'Bfrtip')
)
if(length(mutation_hotspot_levels) > 0){
dt <- dt |>
DT::formatStyle(
"MUTATION_HOTSPOT","MUTATION_HOTSPOT",
color = "white", fontWeight = "bold",
backgroundColor = DT::styleEqual(
mutation_hotspot_levels, rep("black", length(mutation_hotspot_levels)))
)
}
dt
htmltools::br()
htmltools::br()
cat('\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>NO</b> query proteins with recurrent coding somatic variants in TCGA were found. </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
Copy number alterations
- Genes targeted by somatic copy number alterations (sCNAs) in tumor samples have been retrieved from TCGA, where copy number state have been estimated with GISTIC
- Gene aberration frequency are plotted in heatmaps across two categories of mutation types
- sCNA - amplifications
- sCNA - homozygous deletions
- The values in the heatmaps reflect the percent of all tumor samples pr. primary site with the gene amplified/lost (percent_mutated)
- Genes in the heatmap are ranked according to alteration frequency across all sites (i.e. pan-cancer), limited to the top 75 genes in the query set
- Frequencies across all subtypes per primary site are listed in an interactive table
- Only including genes that are aberrant in >= 1 percent of samples for a given tumor type/subtype
- Limited here to top 2,500 gene-subtype frequencies (all aberration frequencies are listed in the Excel output of oncoEnrichR)
suppressMessages(library(plotly))
gene_aberration_top_mat <-
onc_enrich_report[['data']][['tcga']][['aberration']][['matrix']][['cna_ampl']]
#plotly_colors <- "YlGn"
fig <- plotly::plot_ly(
colors = "YlGn",
width = 800,
height = 400 + (14.67 * NROW(onc_enrich_report$data$tcga$aberration$matrix$cna_ampl))) |>
plotly::add_heatmap(
y = rownames(gene_aberration_top_mat),
x = colnames(gene_aberration_top_mat),
z = gene_aberration_top_mat,
hovertext = "Percent mutated",
yaxis = "y") |>
plotly::layout(
title = "Amplifications",
xaxis = list(tickfont = list(size = 13, family = "Helvetica"),
tickangle = -50),
yaxis = list(tickfont = list(size = 12, family = "Helvetica")),
margin = list(l = 75, r = 20, b = 150, t = 30, pad = 4),
legend = list(orientation = 'h')
) |>
plotly::colorbar(
nticks = 10,
title = list(text = "Percent mutated",side = "bottom"),
limits = c(0, plyr::round_any(max(gene_aberration_top_mat), 10, ceiling)))
rm(gene_aberration_top_mat)
fig
cat('\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> Heat map not shown - limited number of genes in the queryset are involved in copy number amplifications (<b>< 3, as reported in TCGA cohorts</b>) </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
suppressMessages(library(plotly))
gene_aberration_top_mat <-
onc_enrich_report[['data']][['tcga']][['aberration']][['matrix']][['cna_homdel']]
#plotly_colors <- "YlOrRd"
fig <- plotly::plot_ly(
colors = "YlOrRd",
width = 800,
height = 400 + (14.67 * NROW(gene_aberration_top_mat))) |>
plotly::add_heatmap(
y = rownames(gene_aberration_top_mat),
x = colnames(gene_aberration_top_mat),
z = gene_aberration_top_mat,
hovertext = "Percent mutated",
yaxis = "y") |>
plotly::layout(
title = 'Homozygous deletions',
xaxis = list(tickfont = list(size = 13, family = "Helvetica"),
tickangle = -50),
yaxis = list(tickfont = list(size = 12, family = "Helvetica")),
margin = list(l = 75, r = 20, b = 150, t = 30, pad = 4)
) |>
plotly::colorbar(
nticks = 10,
title = list(text = "Percent mutated",side = "bottom"),
limits = c(0, plyr::round_any(max(gene_aberration_top_mat), 10, ceiling)))
rm(gene_aberration_top_mat)
fig
cat('\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> Heatmap not shown - limited number of genes in the queryset are involved in homozygous deletions (<b>< 3, as reported in TCGA cohorts</b>) </span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')
all_targets_vtypes <-
onc_enrich_report[['data']][['tcga']][['aberration']][['table']][['cna_homdel']] |>
dplyr::bind_rows(
onc_enrich_report[['data']][['tcga']][['aberration']][['table']][['cna_ampl']]
) |>
dplyr::arrange(dplyr::desc(percent_mutated)) |>
head(2500)
oe_targets_tcga <- crosstalk::SharedData$new(all_targets_vtypes)
crosstalk::bscols(
list(
crosstalk::filter_select("primary_site", "Primary site/tissue", oe_targets_tcga, ~primary_site),
crosstalk::filter_select("variant_type", "Aberration type", oe_targets_tcga, ~variant_type),
crosstalk::filter_slider("percent_mutated", "Percent mutated",
oe_targets_tcga, ~percent_mutated, step = 10,
min = 0, max = 100)
),
list(
crosstalk::filter_select("primary_diagnosis", "Diagnosis", oe_targets_tcga, ~primary_diagnosis),
crosstalk::filter_slider("cohort_size", "Cohort Size", oe_targets_tcga, ~cohort_size)
)
)
htmltools::br()
DT::datatable(oe_targets_tcga, escape = F,
extensions=c("Buttons","Responsive"),
width = "100%",
options=list(buttons = c('csv','excel'),dom = 'Bfrtip')
)
cat('<i>No genes with tcga aberrations were found.</i>',sep='\n')
cat('\n')
{.unlisted .unnumbered .toc-ignore}
Citation Note : If you use the output of the Tumor aberration frequencies module of oncoEnrichR in your research, please cite the following resources and tools:
- Nakken et al., Int J Cancer, 2023 - oncoEnrichR
- Weinstein et al., Nat Genet, 2013 - TCGA
sigven/oncoEnrichR documentation built on Aug. 31, 2023, 8:05 a.m.
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Tumor aberration frequencies
- The somatic mutation frequencies of the target genes in ~10,000 tumor samples have been calculated with data from The Cancer Genome Atlas (TCGA)
SNVs/InDels - oncoplots {.tabset}
- Frequency of somatic SNVs/InDels in the query set genes (top mutated) are illustrated with oncoplots
- Query set mutation frequencies are sorted by type of diagnosis (i.e. cancer subtypes)
- The frequency of transitions/transversions is also shown per sample
tcga_oncoplot_data <- onc_enrich_report[["data"]][["tcga"]][["aberration"]][["table"]][["snv_indel"]] ## oncoplot arguments gene_mar <- 10 annotationFontSize <- 2.5 sepwd_genes <- 0.7 fontSize <- 1.5 legendFontSize <- 2.4 legend_height <- 5 anno_height <- 0.6 fheight <- 26 fwidth <- 18 for(n in names(tcga_oncoplot_data)){ tcga_oncoplot_data[[n]]$fheight <- fheight n_genes <- NROW(tcga_oncoplot_data[[n]][['top_mutated_genes']]) if(n_genes < 40){ tcga_oncoplot_data[[n]]$fheight <- 24 } if(n_genes < 30){ tcga_oncoplot_data[[n]]$fheight <- 21 } if(n_genes < 20){ tcga_oncoplot_data[[n]]$fheight <- 17 } if(n_genes < 10){ tcga_oncoplot_data[[n]]$fheight <- 14 } }
Breast
code <- tcga_oncoplot_data[["Breast"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Breast"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Colon/Rectum
code <- tcga_oncoplot_data[["Colon/Rectum"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Colon/Rectum"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis","MSI_status"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Lung
code <- tcga_oncoplot_data[["Lung"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Lung"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Skin
code <- tcga_oncoplot_data[["Skin"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Skin"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Esophagus/Stomach
code <- tcga_oncoplot_data[["Esophagus/Stomach"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Esophagus/Stomach"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis","MSI_status"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Cervix
code <- tcga_oncoplot_data[["Cervix"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Cervix"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, draw_titv = T, bgCol = "gray93", clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Prostate
code <- tcga_oncoplot_data[["Prostate"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Prostate"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Ovary/Fallopian Tube
code <- tcga_oncoplot_data[["Ovary/Fallopian Tube"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Ovary/Fallopian Tube"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Uterus
code <- tcga_oncoplot_data[["Uterus"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Uterus"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis","MSI_status"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Pancreas
code <- tcga_oncoplot_data[["Pancreas"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Pancreas"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Soft Tissue
code <- tcga_oncoplot_data[["Soft Tissue"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Soft Tissue"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Myeloid
code <- tcga_oncoplot_data[["Myeloid"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Myeloid"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
CNS/Brain
code <- tcga_oncoplot_data[["CNS/Brain"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["CNS/Brain"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Liver
code <- tcga_oncoplot_data[["Liver"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Liver"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, draw_titv = T, bgCol = "gray93", clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Kidney
code <- tcga_oncoplot_data[["Kidney"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Kidney"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Lymphoid
code <- tcga_oncoplot_data[["Lymphoid"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Lymphoid"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Head and Neck
code <- tcga_oncoplot_data[["Head and Neck"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Head and Neck"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Biliary Tract
code <- tcga_oncoplot_data[["Biliary Tract"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Biliary Tract"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, draw_titv = T, bgCol = "gray93", clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Bladder/Urinary Tract
code <- tcga_oncoplot_data[["Bladder/Urinary Tract"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Bladder/Urinary Tract"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, draw_titv = T, bgCol = "gray93", annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Pleura
code <- tcga_oncoplot_data[["Pleura"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Pleura"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, draw_titv = T, bgCol = "gray93", clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Thyroid
code <- tcga_oncoplot_data[["Thyroid"]][['code']] maf <- tcga_maf_datasets[[code]] maftools::oncoplot(maf, genes = tcga_oncoplot_data[["Thyroid"]][['top_mutated_genes']]$symbol, drawRowBar = F, drawColBar = F, sortByAnnotation = T, draw_titv = T, bgCol = "gray93", clinicalFeatures = c("Diagnosis"), showTitle = F, gene_mar = gene_mar, annotationFontSize = annotationFontSize, sepwd_genes = sepwd_genes, fontSize = fontSize, legendFontSize = legendFontSize, legend_height = legend_height, anno_height = anno_height)
cat('<br><br>\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>Too few genes (n < 5) </b> with significant SNV/InDel mutation frequency for oncoplot </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
SNVs/InDels - recurrent variants
- Browse recurrent, protein-coding somatic SNVs/InDels from TCGA in the query set
- Variants are listed as one record per tissue/site, in effect making the same variant occurring in multiple rows
- Only variants with a site-specific frequency >= 2 are shown
- Variants can be filtered based on various properties, e.g. Site/tissue variant recurrence, as well as overall recurrence across all tumor sites (column Pan-cancer variant recurrence)
- Notably, each variant have been annotated/classified with a
- loss-of-function status, based on the LOFTEE plugin in VEP
- Status as somatic mutation hotspots in cancer, according to cancerhotspots.org. Format: \<GENE>|\<CODON>|\<AMINO_ACID_VARIANT>|\<Q-VALUE>
- Top 2,500 recurrent variants are listed here (all variants are listed in the Excel output of oncoEnrichR)
missing_recurrent_tcga_variants <- TRUE if(nrow(onc_enrich_report[['data']][['tcga']][['recurrent_variants']]) > 0){ missing_recurrent_tcga_variants <- FALSE }
vars <- onc_enrich_report[['data']][['tcga']][['recurrent_variants']] |> dplyr::mutate(SITE_RECURRENCE = as.integer(SITE_RECURRENCE)) mutation_hotspot_levels <- levels(as.factor( vars$MUTATION_HOTSPOT)) vars <- vars |> dplyr::arrange( dplyr::desc(SITE_RECURRENCE), dplyr::desc(LOSS_OF_FUNCTION), PROTEIN_CHANGE) |> dplyr::mutate(CONSEQUENCE = stringr::str_replace_all( CONSEQUENCE, "&", ", " )) |> head(2500) if(length(mutation_hotspot_levels) > 0){ vars <- vars |> dplyr::arrange(MUTATION_HOTSPOT, dplyr::desc(SITE_RECURRENCE)) } recurrent_vars_tcga <- crosstalk::SharedData$new( vars) crosstalk::bscols( list( crosstalk::filter_select("SYMBOL","Gene symbol",recurrent_vars_tcga, ~SYMBOL), crosstalk::filter_select("CONSEQUENCE", "Variant consequence", recurrent_vars_tcga, ~CONSEQUENCE), crosstalk::filter_select("PRIMARY_SITE", "Primary site/tissue", recurrent_vars_tcga, ~PRIMARY_SITE), crosstalk::filter_select("MUTATION_HOTSPOT", "Cancer mutation hotspot", recurrent_vars_tcga, ~MUTATION_HOTSPOT) ), list( crosstalk::filter_checkbox("LOSS_OF_FUNCTION", "Loss-of-function status", recurrent_vars_tcga, ~LOSS_OF_FUNCTION), crosstalk::filter_slider("TOTAL_RECURRENCE", "Pancancer variant recurrence", recurrent_vars_tcga, ~TOTAL_RECURRENCE), crosstalk::filter_slider("SITE_RECURRENCE", "Site/tissue variant recurrence", recurrent_vars_tcga, ~SITE_RECURRENCE) ) ) htmltools::br() dt <- DT::datatable(recurrent_vars_tcga, escape = F, extensions=c("Buttons","Responsive"), width = "100%", options=list(buttons = c('csv','excel'),dom = 'Bfrtip') ) if(length(mutation_hotspot_levels) > 0){ dt <- dt |> DT::formatStyle( "MUTATION_HOTSPOT","MUTATION_HOTSPOT", color = "white", fontWeight = "bold", backgroundColor = DT::styleEqual( mutation_hotspot_levels, rep("black", length(mutation_hotspot_levels))) ) } dt htmltools::br() htmltools::br()
cat('\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> <b>NO</b> query proteins with recurrent coding somatic variants in TCGA were found. </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
Copy number alterations
- Genes targeted by somatic copy number alterations (sCNAs) in tumor samples have been retrieved from TCGA, where copy number state have been estimated with GISTIC
- Gene aberration frequency are plotted in heatmaps across two categories of mutation types
- sCNA - amplifications
- sCNA - homozygous deletions
- The values in the heatmaps reflect the percent of all tumor samples pr. primary site with the gene amplified/lost (percent_mutated)
- Genes in the heatmap are ranked according to alteration frequency across all sites (i.e. pan-cancer), limited to the top 75 genes in the query set
- Frequencies across all subtypes per primary site are listed in an interactive table
- Only including genes that are aberrant in >= 1 percent of samples for a given tumor type/subtype
- Limited here to top 2,500 gene-subtype frequencies (all aberration frequencies are listed in the Excel output of oncoEnrichR)
suppressMessages(library(plotly)) gene_aberration_top_mat <- onc_enrich_report[['data']][['tcga']][['aberration']][['matrix']][['cna_ampl']] #plotly_colors <- "YlGn" fig <- plotly::plot_ly( colors = "YlGn", width = 800, height = 400 + (14.67 * NROW(onc_enrich_report$data$tcga$aberration$matrix$cna_ampl))) |> plotly::add_heatmap( y = rownames(gene_aberration_top_mat), x = colnames(gene_aberration_top_mat), z = gene_aberration_top_mat, hovertext = "Percent mutated", yaxis = "y") |> plotly::layout( title = "Amplifications", xaxis = list(tickfont = list(size = 13, family = "Helvetica"), tickangle = -50), yaxis = list(tickfont = list(size = 12, family = "Helvetica")), margin = list(l = 75, r = 20, b = 150, t = 30, pad = 4), legend = list(orientation = 'h') ) |> plotly::colorbar( nticks = 10, title = list(text = "Percent mutated",side = "bottom"), limits = c(0, plyr::round_any(max(gene_aberration_top_mat), 10, ceiling))) rm(gene_aberration_top_mat) fig
cat('\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> Heat map not shown - limited number of genes in the queryset are involved in copy number amplifications (<b>< 3, as reported in TCGA cohorts</b>) </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
suppressMessages(library(plotly)) gene_aberration_top_mat <- onc_enrich_report[['data']][['tcga']][['aberration']][['matrix']][['cna_homdel']] #plotly_colors <- "YlOrRd" fig <- plotly::plot_ly( colors = "YlOrRd", width = 800, height = 400 + (14.67 * NROW(gene_aberration_top_mat))) |> plotly::add_heatmap( y = rownames(gene_aberration_top_mat), x = colnames(gene_aberration_top_mat), z = gene_aberration_top_mat, hovertext = "Percent mutated", yaxis = "y") |> plotly::layout( title = 'Homozygous deletions', xaxis = list(tickfont = list(size = 13, family = "Helvetica"), tickangle = -50), yaxis = list(tickfont = list(size = 12, family = "Helvetica")), margin = list(l = 75, r = 20, b = 150, t = 30, pad = 4) ) |> plotly::colorbar( nticks = 10, title = list(text = "Percent mutated",side = "bottom"), limits = c(0, plyr::round_any(max(gene_aberration_top_mat), 10, ceiling))) rm(gene_aberration_top_mat) fig
cat('\n <ul><li> <i> <span style="font-size: 105%; padding: 3px; background-color:#989898; color:white"> Heatmap not shown - limited number of genes in the queryset are involved in homozygous deletions (<b>< 3, as reported in TCGA cohorts</b>) </span></i></li></ul>',sep='\n') cat('\n') cat('<br><br>')
all_targets_vtypes <- onc_enrich_report[['data']][['tcga']][['aberration']][['table']][['cna_homdel']] |> dplyr::bind_rows( onc_enrich_report[['data']][['tcga']][['aberration']][['table']][['cna_ampl']] ) |> dplyr::arrange(dplyr::desc(percent_mutated)) |> head(2500)
oe_targets_tcga <- crosstalk::SharedData$new(all_targets_vtypes) crosstalk::bscols( list( crosstalk::filter_select("primary_site", "Primary site/tissue", oe_targets_tcga, ~primary_site), crosstalk::filter_select("variant_type", "Aberration type", oe_targets_tcga, ~variant_type), crosstalk::filter_slider("percent_mutated", "Percent mutated", oe_targets_tcga, ~percent_mutated, step = 10, min = 0, max = 100) ), list( crosstalk::filter_select("primary_diagnosis", "Diagnosis", oe_targets_tcga, ~primary_diagnosis), crosstalk::filter_slider("cohort_size", "Cohort Size", oe_targets_tcga, ~cohort_size) ) ) htmltools::br() DT::datatable(oe_targets_tcga, escape = F, extensions=c("Buttons","Responsive"), width = "100%", options=list(buttons = c('csv','excel'),dom = 'Bfrtip') )
cat('<i>No genes with tcga aberrations were found.</i>',sep='\n') cat('\n')
{.unlisted .unnumbered .toc-ignore}
Citation Note : If you use the output of the Tumor aberration frequencies module of oncoEnrichR in your research, please cite the following resources and tools:
- Nakken et al., Int J Cancer, 2023 - oncoEnrichR
- Weinstein et al., Nat Genet, 2013 - TCGA
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