Leucegene Normals
Last updated: 2020-05-18
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Knit directory: MINTIE-paper-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | bf478ec | Marek Cmero | 2020-05-12 | Build site. |
| html | a166ab8 | Marek Cmero | 2020-05-08 | Build site. |
| html | a600688 | Marek Cmero | 2020-05-07 | Build site. |
| 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. |
| html | 90c7fd9 | Marek Cmero | 2020-05-06 | Build site. |
| Rmd | ff4b1dc | Marek Cmero | 2020-05-06 | Leucegene results |
| html | 358aa53 | Marek Cmero | 2020-05-04 | Build site. |
| Rmd | 453d754 | Marek Cmero | 2020-05-04 | Added controls comparison in normals analysis. Added variant class collation function. Added variant summary for |
| html | 4a5d6ae | Marek Cmero | 2020-05-01 | Build site. |
| Rmd | 9556ebb | Marek Cmero | 2020-05-01 | Added leucegene normals analysis. Added expressed genes analysis to leucegene gene expression analysis. |
# util
library(data.table)
library(dplyr)
library(here)
library(stringr)
# plotting
library(ggplot2)options(stringsAsFactors = FALSE)source(here("code/leucegene_helper.R"))Leucegene Normals
Here we generate the results presented in the MINTIE paper, of the method run on a set of non-cancer samples obtained from Leucegene.
# load MINTIE results from leucegene normals
normals_results_dir <- here("data/leucegene/normals_results")
normals_results <- list.files(normals_results_dir, full.names = TRUE) %>%
lapply(., read.delim) %>%
rbindlist() %>%
filter(logFC > 5)
# load cell type info and add to results
celltype <- read.delim(here("data/leucegene/sample_info/celltypes_info.tsv"))
normals_results <- inner_join(normals_results, celltype,
by = c("sample" = "SRX_ID"))Variant Summary
Summary results for variants called by MINTIE on Leucegene normals.
summary_by_sample <- normals_results %>%
group_by(sample) %>%
summarise(variants = length(unique(variant_id)))
summary_by_class <- collate_vartypes(normals_results) %>%
group_by(class) %>%
summarise(variants = length(unique(variant_id))) %>%
mutate(fraction = variants / sum(variants))
print(summary_by_sample)# A tibble: 23 x 2
sample variants
<chr> <int>
1 SRX372044 181
2 SRX372045 137
3 SRX372046 213
4 SRX372047 639
5 SRX372048 1119
6 SRX372049 143
7 SRX372050 497
8 SRX372051 449
9 SRX372052 235
10 SRX372053 75
# … with 13 more rowsprint(summary_by_class)# A tibble: 4 x 3
class variants fraction
<chr> <int> <dbl>
1 Fusion 69 0.0117
2 Novel splice variant 1992 0.338
3 Transcribed structural variant 1234 0.210
4 Unknown 2593 0.440 Variant Genes
MINTIE paper Figure 4 showing the number of variant genes called across the Leucegene normal samples.
results_summary <- get_results_summary(mutate(normals_results, group_var = cell_type),
group_var_name = "cell_type")
ggplot(results_summary, aes(cell_type, V1, group=sample)) +
geom_bar(position = position_dodge2(width = 0.9, preserve = "single"), stat = "identity") +
theme_bw() +
xlab("") +
ylab("Genes with variants")
Library Size and Variant Number Correlation
Perform correlation calculation on the library size and number of variant genes found per sample.
Leucegene Gene Expression notebook must be run before this chunk to generate the expression counts matrix.
# load counts data, calculate library sizes and add to results summary
counts <- fread(here("output/Leucegene_gene_counts.tsv"))
libsizes <- apply(counts, 2, sum) %>% data.frame()
colnames(libsizes) <- "libsize"
libsizes$sample <- factor(rownames(libsizes),
levels = results_summary$sample)
results_summary <- left_join(results_summary, libsizes, by ="sample", "sample")
lib_var_cor <- cor(results_summary$libsize, results_summary$V1, method = "spearman")
print(paste("Spearman correlation between library size and variant genes called:", lib_var_cor))[1] "Spearman correlation between library size and variant genes called: 0.140845074722306"ggplot(results_summary, aes(libsize, V1, colour = cell_type)) +
geom_point() +
theme_bw() +
ylab("Genes with variants")
Protein Coding Genes
Proportion of protein coding genes observed in the MINTIE results.
# load CHESS gene reference containing gene types
chess_genes <- read.delim(gzfile(here("data/ref/chess2.2.genes.gz")))
# join gene info with results and summarise by gene type
results_by_gene <- get_results_by_gene(normals_results)
gene_count <- left_join(results_by_gene, chess_genes, by = c("gene" = "Gene_Name")) %>%
group_by(Gene_Type) %>%
summarise(n_genes = length(unique(gene))) %>%
data.table()
n_protein_coding <- gene_count[gene_count$Gene_Type == "protein_coding", "n_genes"]
print(paste("proportion of protein coding genes =", n_protein_coding / sum(gene_count$n_genes)))[1] "proportion of protein coding genes = 0.81547619047619"Controls Comparison
MINTIE Supplementary Figure 3 showing variant genes called in Leucegene Total White Blood Cell samples with different cell types as control groups.
# get TWBC results
controls_comp <- normals_results[normals_results$cell_type == "Total white blood cells",]
controls_comp$controls <- "twbc"
controls_comp$cell_type <- NULL
# load comparisons against all other controls
controls_test_dir <- here("data/leucegene/normals_controls_test_results")
controls_comp <- load_controls_comparison(controls_test_dir) %>%
rbind(controls_comp, .)
results_summary <- get_results_summary(mutate(controls_comp,
group_var = controls),
group_var_name = "controls")
print("Total variant genes called using different controls:")[1] "Total variant genes called using different controls:"print(results_summary) sample controls V1
1 SRX372045 bc 345
2 SRX372044 bc 365
3 SRX372046 bc 443
4 SRX372045 gran 130
5 SRX372046 gran 247
6 SRX372044 gran 279
7 SRX372045 mono 138
8 SRX372044 mono 187
9 SRX372046 mono 192
10 SRX372044 tc 329
11 SRX372045 tc 378
12 SRX372046 tc 419
13 SRX372045 twbc 156
14 SRX372044 twbc 193
15 SRX372046 twbc 233ggplot(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") +
scale_fill_brewer(palette = "RdYlBu",
labels = c("mono" = "Monocytes",
"twbc" = "Total white blood cells",
"gran" = "Granulocytes",
"tc" = "T-Cells",
"bc" = "B-Cells"))
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 RColorBrewer_1.1-2 pillar_1.4.2
[4] compiler_3.6.1 git2r_0.26.1 workflowr_1.4.0
[7] tools_3.6.1 zeallot_0.1.0 digest_0.6.21
[10] evaluate_0.14 tibble_2.1.3 gtable_0.3.0
[13] pkgconfig_2.0.3 rlang_0.4.0 cli_1.1.0
[16] yaml_2.2.0 xfun_0.10 withr_2.1.2
[19] knitr_1.25 fs_1.3.1 vctrs_0.2.0
[22] rprojroot_1.3-2 grid_3.6.1 tidyselect_0.2.5
[25] glue_1.3.1 R6_2.4.0 fansi_0.4.0
[28] rmarkdown_1.16 purrr_0.3.2 magrittr_1.5
[31] whisker_0.4 backports_1.1.4 scales_1.0.0
[34] htmltools_0.3.6 assertthat_0.2.1 colorspace_1.4-1
[37] labeling_0.3 utf8_1.1.4 stringi_1.4.3
[40] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4