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 c8fba75 Marek Cmero 2021-05-13 Build site.
Rmd c12cf29 Marek Cmero 2021-05-13 Call variants by unique positions in recurrent gene analysis for RCH B-ALL
html 4206f12 Marek Cmero 2021-04-30 Build site.
html 4b8113e Marek Cmero 2020-07-03 Build site.
html 9141a03 Marek Cmero 2020-06-25 Build site.
html e9e4917 Marek Cmero 2020-06-24 Build site.
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
Rmd 3702862 Marek Cmero 2020-05-18 Removed MLM samples from final B-ALL results
html 3702862 Marek Cmero 2020-05-18 Removed MLM samples from final B-ALL results
html a166ab8 Marek Cmero 2020-05-08 Build site.
Rmd f504dcb Marek Cmero 2020-05-08 Added variant found stats for RCH B-ALL analysis
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Rmd 0fde0b8 Marek Cmero 2020-05-07 Added RCH B-ALL analysis

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

# plotting/tables
library(ggplot2)
library(gt)
options(stringsAsFactors = FALSE)
source(here("code/leucegene_helper.R"))

RCH B-ALL results

Here we analyse the results of MINTIE run on the RCH B-ALL cohort.

rch_ball_results_dir <- here("data/RCH_B-ALL")
rch_ball_results <- list.files(rch_ball_results_dir, full.names = TRUE) %>%
                        lapply(., read.delim) %>%
                        rbindlist() %>%
                        filter(logFC > 5)

# rename IDs to be consistent with doi: 10.1182/bloodadvances.2019001008
rch_ball_results$sample <- rch_ball_results$sample %>% 
                            str_split("^EKL-|^EKL|^PE15R-MLM-") %>%
                            lapply(., str_c, collapse = "") %>%
                            unlist() %>%
                            str_c("B-ALL_", .)

# list of ALL-associated genes
all_genes <- read.delim(here("data/ref/ALL_associated_genes.txt"), header=FALSE)$V1

Variant genes called

Supplementary Figure 7 in the MINTIE paper. Shows the overall number of variant genes called by MINTIE in the RCH B-ALL cohort.

results_by_gene <- get_results_by_gene(rch_ball_results)
results_summary <- results_by_gene[, length(unique(gene)), by = "sample"]
results_summary <- results_summary %>% arrange(desc(V1))
results_summary$sample <- factor(results_summary$sample,
                                 levels = results_summary$sample)

results_summary %>%
    summarise(min=min(V1),
              median=median(V1),
              max=max(V1)) %>%
    gt() %>%
    tab_header(
        title = md("**Summary of variant genes called in B-ALL cohort**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    cols_label(
        min = md("**Min**"),
        median = md("**Median**"),
        max = md("**Max**")
    )
Summary of variant genes called in B-ALL cohort
Min Median Max
15 48 633
ggplot(results_summary, aes(sample, V1)) + 
    geom_bar(position=position_dodge(width=0.8), stat="identity") +
    theme_bw() + xlab("") + ylab("# variant genes") +
    scale_fill_brewer(palette = "Set2") +
    theme(legend.position = "bottom",
          axis.text.x = element_text(size = 7, angle = 90))

Version Author Date
4206f12 Marek Cmero 2021-04-30
9141a03 Marek Cmero 2020-06-25
fa6bf0c Marek Cmero 2020-06-11
3702862 Marek Cmero 2020-05-18
a600688 Marek Cmero 2020-05-07

ALL-associated genes called

Supplementary Figure 8 in the MINTIE paper. Shows recurrently called variants in ALL-associated genes in RCH B-ALL cohort.

all_gene_results <- filter(results_by_gene, gene %in% all_genes) %>%
                        collate_vartypes()

paste("We found", 
      all_gene_results$variant_id %>% unique() %>% length(),
      "variants across",
      all_gene_results$gene %>% unique() %>% length(),
      "unique genes") %>%
    print()
[1] "We found 339 variants across 131 unique genes"
# make list of recurrently mutated genes
var_fields <- c("chr1", "pos1", "strand1", "chr2", "pos2", "strand2",
                 "gene", "variant_type", "class", "sample")
all_gene_results <- all_gene_results %>% select(var_fields) %>% distinct()
recurrent_genes <- group_by(all_gene_results, gene) %>%
                        summarise(var_count = length(sample)) %>%
                        filter(var_count > 4) %>%
                        arrange(desc(var_count))

# make summary data frame
all_gene_summary <- group_by(all_gene_results, gene, class, sample) %>%
                        summarise(var_count = length(sample)) %>%
                        filter(gene %in% recurrent_genes$gene)
all_gene_summary$gene <- factor(all_gene_summary$gene,
                                levels = recurrent_genes$gene)

# define category colours and plot
cols <- c("#87649aff",
          "#bdd888ff",
          "#e7d992ff",
          "#bdbdbd")
names(cols) <- c("Fusion",
                 "Transcribed structural variant",
                 "Novel splice variant",
                 "Unknown")
ggplot(all_gene_summary, aes(gene, var_count, fill = class)) +
    geom_bar(sta = "identity") +
    theme_bw() +
    xlab("") +
    ylab("Variants") +
    scale_fill_manual(values = cols) +
    theme(legend.position = "bottom",
          axis.text.x = element_text(angle = 90))

Version Author Date
c8fba75 Marek Cmero 2021-05-13
4206f12 Marek Cmero 2021-04-30
9141a03 Marek Cmero 2020-06-25
fa6bf0c Marek Cmero 2020-06-11
3702862 Marek Cmero 2020-05-18
a166ab8 Marek Cmero 2020-05-08
a600688 Marek Cmero 2020-05-07
# print stats of top 3 gene
all_gene_summary %>%
    group_by(gene) %>%
    summarise(total_vars = sum(var_count)) %>%
    pull(gene) %>%
    as.character() %>%
    head(3) %>%
    lapply(., get_gene_stats, all_gene_summary) %>%
    unlist() %>%
    str_c("\n") %>%
    paste0(collapse = "") %>%
    cat()
We found 26 variants across 8 samples in ETV6
We found 14 variants across 7 samples in IKZF1
We found 13 variants across 3 samples in IKZF2

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] gt_0.2.2          ggplot2_3.3.3     stringr_1.4.0     here_1.0.1       
[5] dplyr_1.0.4       data.table_1.13.6 workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        highr_0.8         pillar_1.4.7      compiler_4.0.3   
 [5] later_1.1.0.1     git2r_0.28.0      tools_4.0.3       digest_0.6.27    
 [9] checkmate_2.0.0   gtable_0.3.0      evaluate_0.14     lifecycle_1.0.0  
[13] tibble_3.0.6      pkgconfig_2.0.3   rlang_0.4.10      cli_2.3.0        
[17] DBI_1.1.1         commonmark_1.7    yaml_2.2.1        xfun_0.21        
[21] withr_2.4.1       knitr_1.31        sass_0.3.1        generics_0.1.0   
[25] fs_1.5.0          vctrs_0.3.6       rprojroot_2.0.2   grid_4.0.3       
[29] tidyselect_1.1.0  glue_1.4.2        R6_2.5.0          rmarkdown_2.6    
[33] farver_2.0.3      purrr_0.3.4       magrittr_2.0.1    whisker_0.4      
[37] backports_1.2.1   scales_1.1.1      promises_1.2.0.1  ellipsis_0.3.1   
[41] htmltools_0.5.1.1 assertthat_0.2.1  colorspace_2.0-0  httpuv_1.5.5     
[45] labeling_0.4.2    stringi_1.5.3     munsell_0.5.0     crayon_1.4.1