Last updated: 2020-07-07

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Knit directory: MINTIE-paper-analysis/

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File Version Author Date Message
html 4b8113e Marek Cmero 2020-07-03 Build site.
html e9e4917 Marek Cmero 2020-06-24 Build site.
Rmd 9434bfe Marek Cmero 2020-06-24 Updated results with latest MINTIE run. Fixed bug with KMT2A PTD checking in different controls. Added leucegene
html b5825d3 Marek Cmero 2020-06-11 Build site.
Rmd c2c1c58 Marek Cmero 2020-06-11 Fixed several tables to reflect paper more closely
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
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
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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/tables
library(ggplot2)
library(gt)
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(fill = TRUE) %>%
                        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.

normals_results %>%
    group_by(sample) %>%
    summarise(variants = length(unique(variant_id))) %>%
    summarise(min = min(variants),
              median = median(variants),
              max = max(variants)) %>%
    gt() %>%
    tab_header(
        title = md("**Variants called by sample summary**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    cols_label(
        min = md("**Min**"),
        median = md("**Median**"),
        max = md("**Max**")
    )
Variants called by sample summary
Min Median Max
49 102 1152
collate_vartypes(normals_results) %>%
    group_by(class) %>%
    summarise(variants = length(unique(variant_id))) %>%
    mutate(fraction = variants / sum(variants)) %>%
    gt() %>%
    fmt_number(columns = vars(fraction), decimals = 3) %>%
    tab_header(
        title = md("**Variants called summary by class**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    cols_label(
        variants = md("**Variants**"),
        fraction = md("**Fraction**")
    )
Variants called summary by class
class Variants Fraction
Fusion 93 0.019
Novel splice variant 2251 0.466
Transcribed structural variant 1243 0.257
Unknown 1245 0.258

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")

results_summary %>%
    summarise(min = min(V1),
              median = median(V1),
              max = max(V1),
              total = sum(V1)) %>%
    gt() %>%
    tab_header(
        title = md("**Variant genes called by sample summary**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    cols_label(
        min = md("**Min**"),
        median = md("**Median**"),
        max = md("**Max**"),
        total = md("**Total**")
    )
Variant genes called by sample summary
Min Median Max Total
49 102 464 3045
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")

Version Author Date
e9e4917 Marek Cmero 2020-06-24
fa6bf0c Marek Cmero 2020-06-11
1c40e33 Marek Cmero 2020-05-07
4a5d6ae Marek Cmero 2020-05-01

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: 0.165101354822576"
ggplot(results_summary, aes(libsize, V1, colour = cell_type)) +
    geom_point() +
    theme_bw() +
    ylab("Genes with variants")

Version Author Date
e9e4917 Marek Cmero 2020-06-24
fa6bf0c Marek Cmero 2020-06-11
1c40e33 Marek Cmero 2020-05-07
358aa53 Marek Cmero 2020-05-04

Protein Coding Genes

Proportion of protein coding genes observed in the MINTIE results.

# load CHESS gene reference containing gene types
chess_genes <- get_chess_genes(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 = "gene") %>%
                group_by(Gene_Type) %>%
                summarise(n_genes = length(unique(gene))) %>%
                data.table()

n_protein_coding <- gene_count[gene_count$Gene_Type == "protein_coding", "n_genes"]
n_var_genes <- sum(gene_count$n_genes)
paste("proportion of protein coding genes =",
    (n_protein_coding / n_var_genes) %>% round(4),
    paste0("(", n_protein_coding, "/", n_var_genes, ")")) %>%
    print()
[1] "proportion of protein coding genes = 0.8347 (1747/2093)"

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, ., fill = TRUE)
results_summary <- get_results_summary(mutate(controls_comp,
                                              group_var = controls),
                                       group_var_name = "controls")

results_summary %>%
    group_by(controls) %>%
    summarise(tcount = sum(V1)) %>%
    gt() %>%
    tab_header(
        title = md("**Total variant genes called using different controls**")
    ) %>%
    tab_options(
        table.font.size = 12
    ) %>%
    cols_label(
        controls = md("**Controls**"),
        tcount = md("**Variant genes**")
    )
Total variant genes called using different controls
Controls Variant genes
twbc 340
mono 466
gran 506
bc 1052
tc 1073
ggplot(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"))

Version Author Date
e9e4917 Marek Cmero 2020-06-24
fa6bf0c Marek Cmero 2020-06-11
87b4e62 Marek Cmero 2020-05-07
358aa53 Marek Cmero 2020-05-04

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] gt_0.2.1          ggplot2_3.3.1     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         RColorBrewer_1.1-2 pillar_1.4.4      
 [4] compiler_3.6.1     git2r_0.26.1       workflowr_1.4.0   
 [7] tools_3.6.1        digest_0.6.21      evaluate_0.14     
[10] lifecycle_0.2.0    tibble_3.0.1       gtable_0.3.0      
[13] checkmate_2.0.0    pkgconfig_2.0.3    rlang_0.4.6       
[16] commonmark_1.7     yaml_2.2.0         xfun_0.10         
[19] withr_2.1.2        knitr_1.25         generics_0.0.2    
[22] fs_1.4.1           vctrs_0.3.1        sass_0.2.0        
[25] rprojroot_1.3-2    grid_3.6.1         tidyselect_1.1.0  
[28] glue_1.4.1         R6_2.4.0           rmarkdown_1.16    
[31] farver_2.0.3       purrr_0.3.2        magrittr_1.5      
[34] whisker_0.4        backports_1.1.4    scales_1.1.1      
[37] ellipsis_0.3.0     htmltools_0.4.0    colorspace_1.4-1  
[40] labeling_0.3       stringi_1.4.3      munsell_0.5.0     
[43] crayon_1.3.4