Leucegene Validation

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Last updated: 2021-05-28

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Knit directory: MINTIE-paper-analysis/

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File	Version	Author	Date	Message
Rmd	ed3d2b6	Marek Cmero	2021-05-28	Figures tweaks and reordering.
html	c60c3b4	Marek Cmero	2021-05-18	Build site.
Rmd	c46b2c5	Marek Cmero	2021-05-18	Added summary figure of variants found in Leucegene cohort
html	4206f12	Marek Cmero	2021-04-30	Build site.
Rmd	dde9f5b	Marek Cmero	2021-04-30	wflow_publish(files = list.files(pattern = "*Rmd"))
Rmd	9595530	Marek Cmero	2021-04-30	Updated analyses
html	4b8113e	Marek Cmero	2020-07-03	Build site.
Rmd	42ce21b	Marek Cmero	2020-07-03	Added CICERO results
html	42ce21b	Marek Cmero	2020-07-03	Added CICERO results
html	379b944	Marek Cmero	2020-06-26	Build site.
Rmd	5448658	Marek Cmero	2020-06-26	Update with missing leucegene sample
html	e9e4917	Marek Cmero	2020-06-24	Build site.
Rmd	9434bfe	Marek Cmero	2020-06-24	Updated results with latest MINTIE run. Fixed bug with KMT2A PTD checking in different controls. Added leucegene
html	0b21347	Marek Cmero	2020-06-11	Build site.
Rmd	fa6bf0c	Marek Cmero	2020-06-11	Updated with new results; improved tables
html	fa6bf0c	Marek Cmero	2020-06-11	Updated with new results; improved tables
html	3702862	Marek Cmero	2020-05-18	Removed MLM samples from final B-ALL results
html	a166ab8	Marek Cmero	2020-05-08	Build site.
html	a600688	Marek Cmero	2020-05-07	Build site.
Rmd	0fde0b8	Marek Cmero	2020-05-07	Added RCH B-ALL analysis
html	1c40e33	Marek Cmero	2020-05-07	Build site.
Rmd	bbc278a	Marek Cmero	2020-05-07	Refactoring
html	87b4e62	Marek Cmero	2020-05-07	Build site.
Rmd	af503f2	Marek Cmero	2020-05-07	Refactoring
html	5c045b5	Marek Cmero	2020-05-07	Build site.
Rmd	d8d5b96	Marek Cmero	2020-05-07	Added Leucegene variant validation
html	90c7fd9	Marek Cmero	2020-05-06	Build site.
Rmd	44d8c37	Marek Cmero	2020-05-06	Build leucegene validation notebook.
Rmd	ff4b1dc	Marek Cmero	2020-05-06	Leucegene results

---

# util
library(data.table)
library(dplyr)
library(here)
library(stringr)

# plotting/tables
library(ggplot2)
library(gt)

# bioinformatics
library(GenomicRanges)

options(stringsAsFactors = FALSE)

source(here("code/leucegene_helper.R"))
source(here("code/simu_helper.R"))

Leucegene Validation

Here we analyse the results of MINTIE run on a number of Leucegene samples, including the effect of controls on a cohort with KMT2A-PTD variants. We also check whether MINTIE has called known variants within the cohort.

# load SRX to patient ID lookup table
kmt2a_patient_lookup <- read.delim(here("data/leucegene/sample_info/KMT2A-PTD_samples.txt"),
                                   header = FALSE,
                                   col.names = c("sample", "patient"))

kmt2a_results_dir <- here("data/leucegene/KMT2A-PTD_results")

# load KMT2A cohort comparisons against all other controls
kmt2a_results <- load_controls_comparison(kmt2a_results_dir)
kmt2a_results <- inner_join(kmt2a_results, kmt2a_patient_lookup, by = "sample")

# load other validation reults and truth table
truth <- read.delim(here("data/leucegene/sample_info/variant_validation_table.tsv"), sep = "\t")
leucegene_results_dir <- here("data/leucegene/validation_results/MINTIE/")
validation <- list.files(leucegene_results_dir, full.names = TRUE) %>%
                lapply(., read.delim) %>%
                rbindlist(fill = TRUE) %>%
                filter(logFC > 5)

KMT2A-PTD controls comparison

MINTIE paper Supplementary Figure 6. Shows the number of variant genes found in the Leucegene cohort containing KMT2A PTDs.

results_summary <- get_results_summary(mutate(kmt2a_results,
                                              sample = patient,
                                              group_var = controls),
                                       group_var_name = "controls")

# build table
results_summary %>%
    group_by(controls) %>%
    summarise(min = min(V1), median = median(V1), max = max(V1)) %>%
    data.frame() %>%
    gt() %>%
     tab_header(
        title = md("**Total variant genes called using different controls**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    cols_label(
        controls = md("**Controls**"),
        min = md("**Min**"),
        median = md("**Median**"),
        max = md("**Max**")
    ) 

Total variant genes called using different controls
Controls	Min	Median	Max
AML_controls	130	211.5	1852
normal_controls	264	645.5	2265
normal_controls_reduced	490	913.0	2552
no_controls	4750	9255.5	11796

ggplot(results_summary, aes(sample, V1, fill = controls)) +
    geom_bar(position = position_dodge2(width = 0.9, preserve = "single"), stat = "identity") +
    theme_bw() +
    xlab("") +
    ylab("Genes with variants") +
    coord_flip() +
    theme(legend.position = "bottom") +
    scale_fill_brewer(palette="Dark2",
                      labels =  c("AML_controls" = "13 AMLs",
                                  "normal_controls" = "13 normals",
                                  "normal_controls_reduced" = "3 normals",
                                  "no_controls" = "No controls"))

Past versions of KMT2A-PTD_controls_comparison-1.png

Version	Author	Date
4206f12	Marek Cmero	2021-04-30
e9e4917	Marek Cmero	2020-06-24
fa6bf0c	Marek Cmero	2020-06-11
3702862	Marek Cmero	2020-05-18
87b4e62	Marek Cmero	2020-05-07
90c7fd9	Marek Cmero	2020-05-06

KMT2A variants found in cohort

MINTIE paper Supplementary Table 1. Shows whether MINITE found a KMT2A SV in each sample for the given control group. Coverage obtained from Audemard et al. spreadsheet containing the Leucegene results must be manually added to data/leucegene/sample_info to run the code.

# load results from km paper for coverage of KMT2A PTDs
kmt2a_lgene_km_results <- read.csv(here("data/leucegene/sample_info/KMT2A-PTD_8-2.fa.xls"), sep="\t") %>%
                            mutate(patient = Sample) %>%
                            group_by(patient) %>%
                            summarise(coverage = max(Min.coverage))

# check whether MINTIE found a KMT2A SV in each control set
found_using_cancon <- get_samples_with_kmt2a_sv(kmt2a_results, "AML_controls")
found_using_norcon <- get_samples_with_kmt2a_sv(kmt2a_results, "normal_controls")
found_using_redcon <- get_samples_with_kmt2a_sv(kmt2a_results, "normal_controls_reduced")
found_using_nocon  <- get_samples_with_kmt2a_sv(kmt2a_results, "no_controls")

# make the table
kmt2a_control_comp <- inner_join(kmt2a_patient_lookup, kmt2a_lgene_km_results, by = "patient") %>%
                        arrange(desc(coverage))
kmt2a_control_comp$`13_AMLs` <- kmt2a_control_comp$sample %in% found_using_cancon
kmt2a_control_comp$`13_normals` <- kmt2a_control_comp$sample %in% found_using_norcon
kmt2a_control_comp$`3_normals` <- kmt2a_control_comp$sample %in% found_using_redcon
kmt2a_control_comp$`no_controls` <- kmt2a_control_comp$sample %in% found_using_nocon

# build output table
kmt2a_control_comp %>%
    gt() %>% 
    cols_label(
        sample = md("**Sample**"),
        patient = md("**Patient**"),
        coverage = md("**Coverage**"),
        `13_AMLs` = md("**13 AMLs**"),
        `13_normals` = md("**13 Normals**"),
        `3_normals` = md("**3 Normals**"),
        `no_controls` = md("**No Controls**")
    ) %>%
    tab_header(
        title = md("**KMT2A PTDs found in Leucegene cohort**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(`13_AMLs`),
            rows = `13_AMLs`)
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(`13_normals`),
            rows = `13_normals`)
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(`3_normals`),
            rows = `3_normals`)
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(`no_controls`),
            rows = `no_controls`)
    )

KMT2A PTDs found in Leucegene cohort
Sample	Patient	Coverage	13 AMLs	13 Normals	3 Normals	No Controls
SRX958906	07H152	158	FALSE	TRUE	TRUE	TRUE
SRX332646	09H115	125	FALSE	TRUE	TRUE	TRUE
SRX957230	06H146	87	TRUE	TRUE	TRUE	TRUE
SRX957223	05H111	79	TRUE	TRUE	TRUE	TRUE
SRX332659	11H021	63	FALSE	TRUE	TRUE	TRUE
SRX332633	05H050	58	TRUE	TRUE	TRUE	TRUE
SRX959061	13H150	58	FALSE	TRUE	FALSE	TRUE
SRX959044	13H048	57	TRUE	TRUE	TRUE	TRUE
SRX958974	10H070	53	TRUE	TRUE	TRUE	TRUE
SRX958963	10H007	50	TRUE	TRUE	TRUE	TRUE
SRX958959	09H106	49	TRUE	TRUE	TRUE	TRUE
SRX959060	13H141	45	TRUE	TRUE	TRUE	TRUE
SRX958945	09H058	29	TRUE	TRUE	TRUE	TRUE
SRX958907	07H155	23	FALSE	TRUE	TRUE	TRUE
SRX381854	08H112	22	TRUE	TRUE	TRUE	TRUE
SRX332645	09H113	17	TRUE	TRUE	TRUE	TRUE
SRX959001	11H183	16	FALSE	FALSE	FALSE	FALSE
SRX381852	08H012	15	FALSE	FALSE	FALSE	FALSE
SRX958932	08H138	15	FALSE	FALSE	FALSE	TRUE
SRX381865	11H008	13	FALSE	FALSE	FALSE	TRUE
SRX958873	06H048	10	FALSE	FALSE	FALSE	TRUE
SRX958922	08H063	6	FALSE	FALSE	FALSE	FALSE
SRX958961	10H001	6	FALSE	FALSE	FALSE	TRUE
SRX958844	04H111	3	FALSE	FALSE	FALSE	FALSE

Leucegene variants found by MINTIE

# add KMT2A results against AML controls as validation
validation <- filter(kmt2a_results, controls == "normal_controls") %>%
                select(-c(controls, patient)) %>%
                select(colnames(validation)) %>%
                rbind(., validation)

get_results_by_gene(validation) %>%
    group_by(sample) %>%
    summarise(vargenes = length(unique(gene))) %>%
    summarise(min = min(vargenes),
              median = median(vargenes),
              max = max(vargenes)) %>%
    data.frame() %>%
    gt() %>%
    tab_header(
        title = md("**Total MINTIE variant genes called by sample**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    cols_label(
        min = md("**Min**"),
        median = md("**Median**"),
        max = md("**Max**")
    )

Total MINTIE variant genes called by sample
Min	Median	Max
261	592	2265

Summary of variants found by MINTIE, Arriba, Squid and TAP

Figure 4A.

Note that the TAP results must be obtained from Supplementary Table 4 from Chiu et al. 2018.

# load other callers
arriba_results <- get_results(here("data/leucegene/validation_results/Arriba"))
squid_results <- get_results(here("data/leucegene/validation_results/Squid"))
tap_results <- read.delim(here("data/leucegene/validation_results/TAP/TAP_leucegene_results.txt"), sep = "\t")

# get variant gene locs (needed to check Squid results)
vargene_locs <- read.delim(here("data/leucegene/leucegene_vargene_locs.tsv"), sep = "\t")
vgx <- GRanges(seqnames = vargene_locs$chrom,
               ranges = IRanges(start = vargene_locs$start, end = vargene_locs$end),
               genes = vargene_locs$gene)

# make truth table
truth_table <- rowwise(truth) %>% 
    mutate(mintie_found = is_variant_in_mintie_results(Experiment, gene1, gene2, variant, validation)) %>%
    mutate(arriba_found = is_variant_in_results(Experiment, gene1, gene2, variant, "arriba", arriba_results)) %>%
    mutate(tap_found = is_variant_in_results(patient_ID, gene1, gene2, variant, "tap", tap_results)) %>%
    mutate(squid_found = is_variant_in_squid_results(Experiment, gene1, gene2, vgx, squid_results)) %>%
    data.frame()

gt(truth_table) %>%
     tab_header(
        title = md("**Variants found in Leucegene cohort**")
    ) %>%
    cols_label(
        patient_ID = md("**Patient**"),
        Experiment = md("**Experiment**"),
        gene1 = md("**Gene 1**"),
        gene2 = md("**Gene 2**"),
        variant = md("**Variant**"),
        cohort = md("**Cohort**"),
        mintie_found = md("**MINTIE Found**"),
        arriba_found = md("**Arriba Found**"),
        squid_found = md("**Squid Found**"),
        tap_found = md("**TAP Found**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(mintie_found),
            rows = mintie_found)
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(arriba_found),
            rows = arriba_found)
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(squid_found),
            rows = squid_found)
    ) %>%
    tab_style(
        style = cell_fill(color = "lightgreen"),
        locations = cells_body(
            columns = vars(tap_found),
            rows = tap_found)
    )

Variants found in Leucegene cohort
Patient	Experiment	Gene 1	Gene 2	Variant	Cohort	MINTIE Found	Arriba Found	TAP Found	Squid Found
03H065	SRX729615	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
03H083	SRX729616	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
03H095	SRX729602	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
03H109	SRX729580	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
03H112	SRX729581	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
03H112	SRX729581	FLT3		ITD	CBF	TRUE	FALSE	TRUE	FALSE
04H030	SRX729603	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
04H061	SRX729582	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
04H091	SRX729583	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
05H042	SRX729617	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	FALSE
05H099	SRX958862	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
05H113	SRX729604	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
05H118	SRX729618	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
05H136	SRX729605	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	FALSE
05H184	SRX729619	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
06H020	SRX729606	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
06H035	SRX729620	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
06H115	SRX729607	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
07H099	SRX381851	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
07H137	SRX729621	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
07H144	SRX729585	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
08H034	SRX729622	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
08H042	SRX729623	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
08H072	SRX729624	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	FALSE
08H072	SRX729624	FLT3		ITD	CBF	TRUE	TRUE	TRUE	FALSE
08H081	SRX729586	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
08H099	SRX729608	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
09H016	SRX729587	CBFB	MYH11	fusion	CBF	FALSE	TRUE	TRUE	TRUE
09H040	SRX729625	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
09H066	SRX729588	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
10H008	SRX729609	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
10H030	SRX729626	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
10H119	SRX729627	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
11H022	SRX729610	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
11H022	SRX729610	FLT3		ITD	CBF	FALSE	FALSE	TRUE	FALSE
11H104	SRX729589	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
11H107	SRX729628	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
11H179	SRX729611	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
12H042	SRX729590	CBFB	MYH11	fusion	CBF	FALSE	TRUE	TRUE	TRUE
12H044	SRX729591	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
12H045	SRX729629	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	FALSE
12H098	SRX729630	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
12H165	SRX729592	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
12H166	SRX729631	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
12H180	SRX729632	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
12H183	SRX729633	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
13H066	SRX729612	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
13H120	SRX959058	CBFB	MYH11	fusion	CBF	TRUE	TRUE	TRUE	TRUE
13H169	SRX959064	RUNX1	RUNX1T1	fusion	CBF	TRUE	TRUE	TRUE	TRUE
04H111	SRX958844	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	TRUE
05H050	SRX332633	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
05H111	SRX957223	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
06H048	SRX958873	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	FALSE
06H146	SRX957230	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
07H152	SRX958906	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
07H155	SRX958907	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
08H012	SRX381852	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	TRUE
08H063	SRX958922	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	FALSE
08H112	SRX381854	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	FALSE
08H138	SRX958932	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	TRUE
09H058	SRX958945	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
09H106	SRX958959	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	TRUE	TRUE
09H113	SRX332645	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
09H115	SRX332646	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	FALSE
10H001	SRX958961	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	FALSE
10H007	SRX958963	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
10H070	SRX958974	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
11H008	SRX381865	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	FALSE
11H021	SRX332659	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	TRUE
11H183	SRX959001	KMT2A		PTD	KMT2A-PTD	FALSE	TRUE	FALSE	TRUE
13H048	SRX959044	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	FALSE
13H141	SRX959060	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	FALSE
13H150	SRX959061	KMT2A		PTD	KMT2A-PTD	TRUE	TRUE	FALSE	FALSE
03H041	SRX332627	NUP98	NSD1	fusion	NUP98-NSD1	FALSE	TRUE	TRUE	TRUE
03H041	SRX332627	FLT3		ITD	NUP98-NSD1	TRUE	TRUE	TRUE	FALSE
05H034	SRX958856	NUP98	NSD1	fusion	NUP98-NSD1	TRUE	TRUE	TRUE	TRUE
05H163	SRX332635	NUP98	NSD1	fusion	NUP98-NSD1	TRUE	TRUE	TRUE	TRUE
08H049	SRX958915	NUP98	NSD1	fusion	NUP98-NSD1	TRUE	TRUE	TRUE	TRUE
08H049	SRX958915	FLT3		ITD	NUP98-NSD1	TRUE	FALSE	TRUE	FALSE
10H038	SRX381861	NUP98	NSD1	fusion	NUP98-NSD1	TRUE	TRUE	TRUE	TRUE
11H027	SRX958987	NUP98	NSD1	fusion	NUP98-NSD1	TRUE	TRUE	TRUE	TRUE
11H027	SRX958987	FLT3		ITD	NUP98-NSD1	TRUE	TRUE	TRUE	FALSE
11H160	SRX332667	NUP98	NSD1	fusion	NUP98-NSD1	TRUE	TRUE	TRUE	TRUE
11H160	SRX332667	FLT3		ITD	NUP98-NSD1	TRUE	FALSE	TRUE	FALSE

# tally up detected variants into summary table
truth_summary <- truth_table %>%
    group_by(gene1, gene2, variant) %>%
    summarise(mintie_detected = sum(mintie_found),
              arriba_detected = sum(arriba_found),
              squid_detected = sum(squid_found),
              tap_detected = sum(tap_found),
              total = length(mintie_found)) %>%
    data.frame()

gt(truth_summary) %>%
    tab_header(
        title = md("**Summary of variants found in Leucegene cohort**")
    ) %>%
    cols_label(
        gene1 = md("**Gene 1**"),
        gene2 = md("**Gene 2**"),
        variant = md("**Variant**"),
        mintie_detected = md("**MINTIE Detected**"),
        arriba_detected = md("**Arriba Detected**"),
        squid_detected = md("**Squid Detected**"),
        tap_detected = md("**TAP Detected**"),
        total = md("**Total**")
    ) %>%
    tab_options(
        table.font.size = 12
    )

Summary of variants found in Leucegene cohort
Gene 1	Gene 2	Variant	MINTIE Detected	Arriba Detected	Squid Detected	TAP Detected	Total
CBFB	MYH11	fusion	24	26	25	26	26
FLT3		ITD	6	3	0	7	7
KMT2A		PTD	16	24	15	1	24
NUP98	NSD1	fusion	6	7	7	7	7
RUNX1	RUNX1T1	fusion	20	20	17	20	20

ts <- truth_summary %>%
        reshape2::melt() %>%
        group_by(variant, variable) %>%
        summarise(detected = sum(value)) %>%
        data.frame()
ts$method <- gsub("_detected", "", ts$variable)

# reorder factors
ts$method <- factor(ts$method, levels = c("mintie", "arriba", "squid", "tap", "total"))
ts$variant <- factor(paste0(ts$variant, "s"), levels=c("fusions", "PTDs", "ITDs"))

ggplot(ts[ts$method != "total",], aes(method, detected)) +
    geom_bar(stat = "identity") +
    theme_bw() +
    xlab("Detected") +
    ylab("Method") +
    scale_x_discrete(labels=c("MINTIE", "Arriba", "SQUID", "TAP")) +
    scale_y_discrete(limits=seq(0, 53, 5)) +
    geom_hline(data=ts[ts$method == "total",], aes(yintercept=detected), colour="salmon") +
    facet_grid(~variant)

Past versions of leucegene_variants_validation-1.png

Version	Author	Date
c60c3b4	Marek Cmero	2021-05-18

Session information

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2  IRanges_2.24.1      
 [4] S4Vectors_0.28.1     BiocGenerics_0.36.0  gt_0.2.2            
 [7] ggplot2_3.3.3        stringr_1.4.0        here_1.0.1          
[10] dplyr_1.0.4          data.table_1.13.6    workflowr_1.6.2     

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0       xfun_0.21              reshape2_1.4.4        
 [4] purrr_0.3.4            colorspace_2.0-0       vctrs_0.3.6           
 [7] generics_0.1.0         htmltools_0.5.1.1      yaml_2.2.1            
[10] rlang_0.4.10           later_1.1.0.1          pillar_1.4.7          
[13] glue_1.4.2             withr_2.4.1            DBI_1.1.1             
[16] RColorBrewer_1.1-2     plyr_1.8.6             GenomeInfoDbData_1.2.4
[19] lifecycle_1.0.0        zlibbioc_1.36.0        commonmark_1.7        
[22] munsell_0.5.0          gtable_0.3.0           evaluate_0.14         
[25] labeling_0.4.2         knitr_1.31             httpuv_1.5.5          
[28] highr_0.8              Rcpp_1.0.6             promises_1.2.0.1      
[31] scales_1.1.1           backports_1.2.1        checkmate_2.0.0       
[34] XVector_0.30.0         farver_2.0.3           fs_1.5.0              
[37] digest_0.6.27          stringi_1.5.3          grid_4.0.3            
[40] rprojroot_2.0.2        tools_4.0.3            bitops_1.0-6          
[43] sass_0.3.1             magrittr_2.0.1         RCurl_1.98-1.2        
[46] tibble_3.0.6           crayon_1.4.1           whisker_0.4           
[49] pkgconfig_2.0.3        ellipsis_0.3.1         assertthat_0.2.1      
[52] rmarkdown_2.6          R6_2.5.0               git2r_0.28.0          
[55] compiler_4.0.3