RCH B-ALL
Last updated: 2021-05-13
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Knit directory: MINTIE-paper-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 |
| 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/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)$V1Variant genes called
Supplementary Figure 4 in the MINTIE paper. Shows the overall number of variant genes called by MINTIE in the RCH B-ALL cohort.
results_by_gene <- get_results_by_gene(rch_ball_results)
results_summary <- results_by_gene[, length(unique(gene)), by = "sample"]
results_summary <- results_summary %>% arrange(desc(V1))
results_summary$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 |
| 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))
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 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))
# 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