Simulation Benchmarking
Last updated: 2020-05-27
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Knit directory: MINTIE-paper-analysis/
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# util
library(data.table)
library(dplyr)
library(here)
# plotting
library(ggplot2)
# bioinformatics helpers
library(GenomicRanges)
library(Rsamtools)source(here("code/simu_helper.R"))
source(here("code/plot.R"))options(stringsAsFactors = FALSE)Simulation Benchmarking
Here we analyse the results on a simulated data set of 1,500 variants containing fusions, transcribed structural variants and novel splice variants, across 8 methods: MINTIE, TAP, Barnacle, SQUID, JAFFA, Arriba, StringTie and KisSplice.
The plots show the number of variants detected from each category and the number of false positives. The number of background genes detected in the false positives in displayed as printed output.
fus_truth <- read.delim(here("data/simu/truth/allvars_fusions_simulated.tsv"))
tsv_nsv_truth <- read.delim(here("data/simu/truth/allvars_tsvs_splice_simulated.tsv"))
all_gene_locs <- read.delim(gzfile(here("data/ref/all_gene_locs.tsv.gz")))
bg_gene_ref <- read.delim(here("data/simu/truth/bg_gene_ref.tsv"))
# extract truth and background gene names
var_genes_truth <- unique(c(fus_truth$gene1, fus_truth$gene2, tsv_nsv_truth$gene))
var_genes_truth <- var_genes_truth[var_genes_truth != ""]
bg_genes <- bg_gene_ref$gene
# make Genomic Ranges objects from data
fus_grx <- get_granges(fus_truth)
tsv_nsv_grx <- get_granges(tsv_nsv_truth)
bgenes_grx <- get_granges(bg_gene_ref, convert_chrom = TRUE, add_chr = FALSE)
all_gene_grx <- get_granges(all_gene_locs, convert_chrom = TRUE, add_chr = FALSE)
# create results object
nsv_names <- c("Extended exon",
"Novel exon",
"Retained intron",
"Truncated exon",
"Unknown splice")
tsv_names <- c("Deletion",
"Insertion",
"Internal tandem duplication",
"Partial tandem duplication",
"Inversion")
fus_names <- c("Fusion (canonical)",
"Fusion (EE)",
"Fusion (NE)",
"Fusion (insertion)",
"Fusion (unpartnered)")
vartypes <- c("False positive", nsv_names, tsv_names, fus_names)
classes <- c("False positive",
"Novel splice variant",
"Transcribed structural variant",
"Fusion")
results <- data.frame(vartype = factor(vartypes, levels = vartypes),
class = factor(c(classes[1], rep(classes[2:4], each=5)),
levels = classes[4:1]),
row.names = c("FP", "EE", "NE", "RI", "NEJ", "US",
"DEL", "INS", "ITD", "PTD", "INV",
"canonical_fusion", "EE_fusion",
"NE_fusion", "INS_fusion", "unpartnered_fusion"))
cols <- c("#87649aff", "#bdd888ff", "#e7d992ff", "#636363")
names(cols) <- c("Fusion", "Transcribed structural variant", "Novel splice variant", "False positive")MINTIE
# load data
mintie_results <- read.delim(here("data/simu/results/MINTIE/allvars-case_results.tsv"))
mintie_results <- mintie_results[mintie_results$logFC > 5,]
# extract genes
mintie_vargenes <- sapply(mintie_results$overlapping_genes, function(x){strsplit(x, "\\||:")[[1]]})
mvg <- unlist(mintie_vargenes)
# count found fusions, TSVs and NSVs
found_fus <- table(fus_truth$fusion_type[fus_truth$gene1 %in% mvg | fus_truth$gene2 %in% mvg])
found_tsv <- table(tsv_nsv_truth$vartype[tsv_nsv_truth$gene %in% mvg])
# count false positives genes
fp <- sapply(mintie_vargenes, function(x){!any(x %in% var_genes_truth)})
fp_genes <- unlist(sapply(mintie_results$overlapping_genes[fp], function(x){strsplit(x, "\\||:")}))
fp_genes <- fp_genes[fp_genes != ""]
n_fp <- length(unique(fp_genes))
results <- append_results(results, "MINTIE", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", sum(unique(fp_genes) %in% bg_genes)))[1] "Background genes in FPs: 1"plot_simu_benchmarking(results, "MINTIE")
TAP
# load result data
tap_svs <- read.delim(here("data/simu/results/TAP/sv.bedpe"), sep = "\t", skip = 2)
tap_nsv <- read.delim(here("data/simu/results/TAP/novel_splicing.bedpe"), sep = "\t", skip = 2)
# extract variant genes
tap_vargenes <- Reduce(union, list(tap_svs$gene1, tap_svs$gene2, tap_nsv$gene))
# count found fusions, TSVs and NSVs
found_fus <- table(fus_truth$fusion_type[fus_truth$gene1 %in% tap_vargenes | fus_truth$gene2 %in% tap_vargenes])
found_tsv <- table(tsv_nsv_truth$vartype[tsv_nsv_truth$gene %in% tap_vargenes])
# count false positives genes
fp_svs <- tap_svs[!tap_svs$gene1 %in% var_genes_truth & !tap_svs$gene2 %in% var_genes_truth,]
fp_nsv <- tap_nsv[!tap_nsv$gene1 %in% var_genes_truth & !tap_nsv$gene2 %in% var_genes_truth,]
fp_genes <- unique(c(fp_svs$gene1, fp_svs$gene2, fp_nsv$gene1, fp_nsv$gene2))
n_fp <- length(fp_genes)
results <- append_results(results, "TAP", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", sum(fp_genes %in% bg_genes)))[1] "Background genes in FPs: 2"plot_simu_benchmarking(results, "TAP")
Barnacle
# build hg19 version of truth data
# fusions
hg19_fus_truth <- fus_truth
hg19_fus_truth$loc1 <- hg19_fus_truth$hg19_loc1
hg19_fus_truth$loc2 <- hg19_fus_truth$hg19_loc2
hg19_fus_truth$loc1[hg19_fus_truth$loc1 == ""] <- "chrZ:1-2" #dummy record for blanks
hg19_fus_truth$loc2[hg19_fus_truth$loc2 == ""] <- "chrZ:1-2"
# TSVs/NSVs
hg19_tsv_nsv_truth <- tsv_nsv_truth
hg19_tsv_nsv_truth$loc <- hg19_tsv_nsv_truth$hg19_loc
hg19_tsv_nsv_truth$loc[hg19_tsv_nsv_truth$loc == ""] <- "chrZ:1-2"
# make Genomic Ranges objects
hg19_fus_grx <- get_granges(hg19_fus_truth, convert_chrom = FALSE)
hg19_tsv_nsv_grx <- get_granges(hg19_tsv_nsv_truth, convert_chrom = FALSE)
hg19_bg_gene_ref <- read.delim(here("data/simu/truth/hg19_bg_gene_ref.tsv"))
hg19_bgenes_grx <- get_granges(bg_gene_ref, convert_chrom = FALSE)
# load barnacle results and make Genomic Ranges objects
barnacle_results <- read.delim(here("data/simu/results/Barnacle/allvars-case.barnacle.data"), header=FALSE)$V1
barnacle_results <- barnacle_results[grep("OVERLAPPING", barnacle_results)]
barnacle_grx_a <- get_barnacle_grx(barnacle_results)
barnacle_grx_b <- get_barnacle_grx(barnacle_results, side_A = FALSE)
# extract hits
hits <- get_hits(barnacle_grx_a, barnacle_grx_b,
hg19_fus_grx[[1]], hg19_fus_grx[[2]],
hg19_tsv_nsv_grx, hg19_bgenes_grx)
found_fus <- table(hg19_fus_truth[hits$fus_truth_hits,]$fusion_type)
found_tsv <- table(hg19_tsv_nsv_truth[hits$tsv_nsv_truth_hits,]$vartype)
fps <- !1:length(barnacle_grx_a) %in% unique(hits$caller_hits)
n_fp <- sum(fps)
results <- append_results(results, "Barnacle", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", sum(hits$bg_hits %in% which(fps))))[1] "Background genes in FPs: 3"plot_simu_benchmarking(results, "Barnacle")
SQUID
# load data
squid_results <- read.delim(here("data/simu/results/SQUID/results_sv.txt"))
# make GRanges objects
chr1 <- sapply(squid_results$X..chrom1, convert_chrom, add_chr=FALSE)
loc1_grx <- GRanges(seqnames = chr1,
ranges = IRanges(start = squid_results$start1, end = squid_results$end1))
chr2 <- sapply(squid_results$chrom2, convert_chrom, add_chr=FALSE)
loc2_grx <- GRanges(seqnames = chr2,
ranges = IRanges(start = squid_results$start2, end = squid_results$end2))
# get hits
hits <- get_hits(loc1_grx, loc2_grx, fus_grx[[1]], fus_grx[[2]], tsv_nsv_grx, bgenes_grx)
found_fus <- table(fus_truth[hits$fus_truth_hits,]$fusion_type)
found_tsv <- table(tsv_nsv_truth[hits$tsv_nsv_truth_hits,]$vartype)
# get false positives
fps <- squid_results[!1:nrow(squid_results) %in% hits$caller_hits,]
fps <- fps[!(fps$X..chrom1 %like% "alt" | fps$chrom2 %like% "alt"),]
n_fp <- nrow(fps)
results <- append_results(results, "SQUID", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", sum(hits$bg_hits %in% as.numeric(rownames(fps)))))[1] "Background genes in FPs: 0"plot_simu_benchmarking(results, "SQUID")
JAFFA
# load data
jaffa_results <- read.delim(here("data/simu/results/JAFFA/jaffa_results.csv"), sep = ",")
# make GRanges objects
chr1 <- sapply(jaffa_results$chrom1, convert_chrom, add_chr=FALSE)
loc1_grx <- GRanges(seqnames = chr1,
ranges = IRanges(start = jaffa_results$base1, end = jaffa_results$base1))
chr2 <- sapply(jaffa_results$chrom1, convert_chrom, add_chr=FALSE)
loc2_grx <- GRanges(seqnames = chr2,
ranges = IRanges(start = jaffa_results$base2, end = jaffa_results$base2))
# get hits
hits <- get_hits(loc1_grx, loc2_grx, fus_grx[[1]], fus_grx[[2]], tsv_nsv_grx, bgenes_grx)
found_fus <- table(fus_truth[hits$fus_truth_hits,]$fusion_type)
found_tsv <- table(tsv_nsv_truth[hits$tsv_nsv_truth_hits,]$vartype)
# get false positives
fps <- jaffa_results[!rownames(jaffa_results) %in% hits$caller_hits,]
fps <- fps[!(fps$chrom1 %like% "alt" | fps$chrom2 %like% "alt"),]
fp_genes <- unlist(sapply(fps$fusion.genes, strsplit, split=":"))
n_fp <- length(unique(fp_genes))
results <- append_results(results, "JAFFA", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", sum(hits$bg_hits %in% as.numeric(rownames(fps)))))[1] "Background genes in FPs: 0"plot_simu_benchmarking(results, "JAFFA")
Arriba
# load data
arriba_results <- read.delim(here("data/simu/results/Arriba/fusions.tsv"))
# extract variasnt genes
arriba_vargenes1 <- sapply(arriba_results$X.gene1, get_arriba_genes)
arriba_vargenes2 <- sapply(arriba_results$gene2, get_arriba_genes)
avg <- unlist(list(arriba_vargenes1, arriba_vargenes2))
# tally results
found_tsv <- table(tsv_nsv_truth[tsv_nsv_truth$gene %in% avg,]$vartype)
found_fus <- table(fus_truth[fus_truth$gene1 %in% avg | fus_truth$gene2 %in% avg,]$fusion_type)
# get false positives
fp1 <- sapply(arriba_vargenes1, function(x){!any(x %in% var_genes_truth)})
fp2 <- sapply(arriba_vargenes2, function(x){!any(x %in% var_genes_truth)})
fp_genes1 <- arriba_vargenes1[fp1 & fp2]
fp_genes2 <- arriba_vargenes2[fp1 & fp2]
fp_genes <- unique(union(fp_genes1, fp_genes2))
n_fp <- length(fp_genes)
results <- append_results(results, "Arriba", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", sum(fp_genes%in% bgenes_grx$genes)))[1] "Background genes in FPs: 0"plot_simu_benchmarking(results, "Arriba")
StringTie
# load data and extract transcripts marked as novel
stringtie_results <- read.delim(here("data/simu/results/StringTie/fullsimu.gtf"), comment="#", header=F)
stringtie_results <- stringtie_results[!stringtie_results$V9 %like% "ref_gene_id",]
stringtie_results <- stringtie_results[stringtie_results$V3 == "transcript",]
chrom <- sapply(stringtie_results$V1, convert_chrom, add_chr = FALSE)
stringtie_grx <- GRanges(seqnames = chrom,
ranges = IRanges(start = stringtie_results$V4,
end = stringtie_results$V5))
hits <- get_hits_oneloc(stringtie_grx, fus_grx[[1]], fus_grx[[2]], tsv_nsv_grx, bgenes_grx)
found_fus <- table(fus_truth[hits$fus_truth_hits,]$fusion_type)
found_tsv <- table(tsv_nsv_truth[hits$tsv_nsv_truth_hits,]$vartype)
# get false positives
fp_grx <- stringtie_grx[!1:length(hits$caller_hits) %in% hits$caller_hits]
fp_gene_hits <- all_gene_grx[subjectHits(findOverlaps(fp_grx, all_gene_grx))]
n_fp <- length(unique(fp_gene_hits$genes))
results <- append_results(results, "StringTie", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", sum(unique(fp_gene_hits$genes) %in% bgenes_grx$genes)))[1] "Background genes in FPs: 1"plot_simu_benchmarking(results, "StringTie")
KisSplice
# regenerate gene ranges objects "chr" prefix
# (this is required as KisSplice"s bam was aligned
# to a genome with chr prefixes)
fus_grx <- get_granges(fus_truth, convert_chrom = TRUE)
tsv_nsv_grx <- get_granges(tsv_nsv_truth, convert_chrom = TRUE)
bgenes_grx <- get_granges(bg_gene_ref, convert_chrom = FALSE)
# extract hits im fusion and TSV/NSV gene regions
bam <- here("data/simu/results/KisSplice/results.bam")
fus1_hits <- get_kissplice_hits(bam, fus_grx[[1]])
fus2_hits <- get_kissplice_hits(bam, fus_grx[[2]])
tsv_nsv_hits <- get_kissplice_hits(bam, tsv_nsv_grx)
# remame row names to match locations from results
rownames(fus_truth) <- sapply(fus_truth$loc1, convert_chrom)
rownames(tsv_nsv_truth) <- sapply(tsv_nsv_truth$loc, convert_chrom)
# tally results
found_fus <- table(fus_truth[names(fus1_hits)[fus1_hits | fus2_hits],]$fusion_type)
found_tsv <- table(tsv_nsv_truth[names(tsv_nsv_hits[tsv_nsv_hits]),]$vartype)
# count number of FP hits in background genes
# first, get all reads that we counted as "hits"
param <- ScanBamParam(which = c(fus_grx[[1]], fus_grx[[2]], tsv_nsv_grx),
what = c("pos"))
all_hits <- scanBam(bam, param = param)
all_hits <- unique(unlist(all_hits))
# now get all reads in the results and count all
# reads that were not counted as hits
param <- ScanBamParam(what = "pos")
all_results <- unique(scanBam(bam, param = param)[[1]]$pos)
fps <- all_results[!all_results %in% all_hits]
n_fp <- length(fps)
# count the numner of FPs in background genes
param <- ScanBamParam(which = bgenes_grx, what = c("pos"))
bg_hits <- scanBam(bam, param = param)
bg_hits <- bg_hits[as.numeric(bg_hits) %in% fps]
results <- append_results(results, "KisSplice", found_fus, found_tsv, n_fp)
print(paste("Background genes in FPs:", length(bg_hits)))[1] "Background genes in FPs: 0"plot_simu_benchmarking(results, "KisSplice")
All results
MINTIE paper Figure 3 containing results from all benchmarked methods.
mr <- melt(results, id.vars = c("vartype", "class"), variable.name = "method")
mr$method <- factor(mr$method,
levels=c("MINTIE", "TAP", "Barnacle", "SQUID", "JAFFA", "Arriba", "KisSplice", "StringTie"))
plot_all_simu_benchmarks(mr)
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] Rsamtools_1.34.1 Biostrings_2.50.2 XVector_0.22.0
[4] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2 IRanges_2.16.0
[7] S4Vectors_0.20.1 BiocGenerics_0.28.0 ggplot2_3.1.0
[10] here_0.1 dplyr_0.8.1 data.table_1.12.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.5 purrr_0.3.2
[4] reshape2_1.4.3 colorspace_1.4-1 htmltools_0.3.6
[7] yaml_2.2.0 rlang_0.4.2 later_1.0.0
[10] pillar_1.3.1 glue_1.3.1 withr_2.1.2
[13] BiocParallel_1.14.2 GenomeInfoDbData_1.2.0 plyr_1.8.4
[16] stringr_1.4.0 zlibbioc_1.28.0 munsell_0.5.0
[19] gtable_0.3.0 workflowr_1.6.1 evaluate_0.13
[22] labeling_0.3 knitr_1.22 httpuv_1.5.2
[25] Rcpp_1.0.1 promises_1.1.0 scales_1.0.0
[28] backports_1.1.3 fs_1.2.7 digest_0.6.18
[31] stringi_1.4.3 grid_3.5.1 rprojroot_1.3-2
[34] tools_3.5.1 bitops_1.0-6 magrittr_1.5
[37] lazyeval_0.2.2 RCurl_1.95-4.12 tibble_2.1.1
[40] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[43] assertthat_0.2.1 rmarkdown_1.12 R6_2.4.0
[46] git2r_0.26.1 compiler_3.5.1