RCH B-ALL
Last updated: 2020-06-11
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Knit directory: MINTIE-paper-analysis/
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These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3702862 | Marek Cmero | 2020-05-12 | Removed MLM samples from final B-ALL results |
| html | 3702862 | Marek Cmero | 2020-05-12 | 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 |
| 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)
library(gt)
# 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)$V1Variant genes called
Supplementary Figure 4 in the MINTIE paper. Shows the overall number of variant genes called by MINTIE in the RCH B-ALL cohort.
results_by_gene <- get_results_by_gene(rch_ball_results)
results_summary <- results_by_gene[, length(unique(gene)), by = "sample"]
results_summary <- results_summary %>% arrange(desc(V1))
results_summary$sample <- factor(results_summary$sample,
levels = results_summary$sample)
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 |
| 20 | 52 | 554 |
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))
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 329 variants across 138 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))
# 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 25 variants across 10 samples in ETV6
We found 10 variants across 2 samples in IKZF2
We found 9 variants across 8 samples in PAX5
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_3.3.1 gt_0.2.1 stringr_1.4.0 here_0.1
[5] dplyr_1.0.0 data.table_1.12.6
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 pillar_1.4.4 compiler_3.6.1 git2r_0.26.1
[5] workflowr_1.4.0 tools_3.6.1 digest_0.6.21 evaluate_0.14
[9] lifecycle_0.2.0 tibble_3.0.1 gtable_0.3.0 checkmate_2.0.0
[13] pkgconfig_2.0.3 rlang_0.4.6 commonmark_1.7 yaml_2.2.0
[17] xfun_0.10 withr_2.1.2 knitr_1.25 generics_0.0.2
[21] fs_1.4.1 vctrs_0.3.1 sass_0.2.0 rprojroot_1.3-2
[25] grid_3.6.1 tidyselect_1.1.0 glue_1.4.1 R6_2.4.0
[29] rmarkdown_1.16 farver_2.0.3 purrr_0.3.2 magrittr_1.5
[33] whisker_0.4 backports_1.1.4 scales_1.1.1 ellipsis_0.3.0
[37] htmltools_0.4.0 colorspace_1.4-1 labeling_0.3 stringi_1.4.3
[41] munsell_0.5.0 crayon_1.3.4