Last updated: 2020-05-18

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Knit directory: MINTIE-paper-analysis/

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Ignored files:
    Ignored:    .Rhistory
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    Ignored:    data/RCH_B-ALL/
    Ignored:    data/leucegene/salmon_out/
    Ignored:    data/leucegene/sample_info/KMT2A-PTD_8-2.fa.xls
    Ignored:    output/Leucegene_gene_counts.tsv
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File Version Author Date Message
html 3aee926 Marek Cmero 2020-05-18 Build site.
html bf478ec Marek Cmero 2020-05-12 Build site.
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
html a600688 Marek Cmero 2020-05-07 Build site.
Rmd 0fde0b8 Marek Cmero 2020-05-07 Added RCH B-ALL analysis

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

# plotting
library(ggplot2)
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 4 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$sample)
print("Variant genes called summary:")
[1] "Variant genes called summary:"
summary(results_summary$V1)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   5.00   37.00   49.00   64.97   73.00  507.00 
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
bf478ec Marek Cmero 2020-05-12
a600688 Marek Cmero 2020-05-07

ALL-associated genes called

Supplementary Figure 5 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 517 variants across 192 unique genes"
# make list of recurrently mutated genes
recurrent_genes <- group_by(all_gene_results, gene) %>%
                        summarise(var_count = length(unique(variant_id))) %>%
                        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(unique(variant_id))) %>%
                        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
bf478ec Marek Cmero 2020-05-12
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 11 samples in ETV6
We found 19 variants across 2 samples in ZFP36L2
We found 17 variants across 6 samples in ACTB

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /config/RStudio/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /config/RStudio/R/3.6.1/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_3.2.1     stringr_1.4.0     here_0.1          dplyr_0.8.3      
[5] data.table_1.12.6

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       knitr_1.25       whisker_0.4      magrittr_1.5    
 [5] workflowr_1.4.0  munsell_0.5.0    tidyselect_0.2.5 colorspace_1.4-1
 [9] R6_2.4.0         rlang_0.4.0      tools_3.6.1      grid_3.6.1      
[13] gtable_0.3.0     xfun_0.10        withr_2.1.2      git2r_0.26.1    
[17] htmltools_0.3.6  lazyeval_0.2.2   yaml_2.2.0       rprojroot_1.3-2 
[21] digest_0.6.21    assertthat_0.2.1 tibble_2.1.3     crayon_1.3.4    
[25] purrr_0.3.2      fs_1.3.1         glue_1.3.1       evaluate_0.14   
[29] rmarkdown_1.16   labeling_0.3     stringi_1.4.3    compiler_3.6.1  
[33] pillar_1.4.2     scales_1.0.0     backports_1.1.4  pkgconfig_2.0.3