Last updated: 2020-04-30

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Knit directory: MINTIE-paper-analysis/

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Rmd	c4c3844	Marek Cmero	2020-04-30	Added leucegene gene expression notebook

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

# plotting
library(ggplot2)
library(RColorBrewer)

# bioinformatics/stats helpers
library(tximport)
library(limma)
library(matrixStats)

source(here("code/plot.R"))

options(stringsAsFactors = FALSE)

Leucegene Gene Expression

Here we generate a PCA plot for all the Leucegene samples used in the MINTIE paper KMT2A-PTD and normal sample analyses, and perform a few small expression analyses presented in the paper.

salmon_dir <- here("data/salmon_out")

# construct list of quant.sf files 
quant_files <- list.files(salmon_dir) %>% 
                    str_c(salmon_dir, ., "quant.sf", sep = "/")

# transcript > gene reference file for CHESS
tx2gene <- read.delim(gzfile(here("data/ref/tx2gene.txt.gz")))

# import quant files
txi <- tximport(quant_files,
                type = "salmon",
                countsFromAbundance = "lengthScaledTPM",
                tx2gene = tx2gene,
                ignoreTxVersion = FALSE)

# load sample info
celltype <- read.delim(here("data/leucegene/celltypes_info.tsv"))
kmt2a_samples <- read.delim(here("data/leucegene/KMT2A-PTD_samples.txt"), header = FALSE)$V1
aml_controls <- read.delim(here("data/leucegene/selected_13_CBF_AML_controls.txt"), header = FALSE)$V1
nup_samples <- read.delim(here("data/leucegene/NUP98-NSD1_samples.txt"), header = FALSE)$V1

# reduced normal control set (used in KMT2A-PTD analysis)
s1 <- celltype$SRX_ID[celltype$cell_type == "Total white blood cells"][1]
s2 <- celltype$SRX_ID[celltype$cell_type == "Monocytes"][1]
s3 <- celltype$SRX_ID[celltype$cell_type == "Granulocytes"][1]
reduced_normal_controls <- c(s1, s2, s3)

PCA plot

PCA of gene expression derived from Salmon quantification for Leucegene KMT2A-PTD and normals. Normal samples used as controls in the reduced set of controls are circled.

# get counts matrix
counts <- txi$counts
colnames(counts) <- list.files(salmon_dir)

# variance stabilised, log2 CPM transformation
ve <- voom(counts)$E

# select 500 most variable genes
select <- order(rowVars(ve), decreasing = T)[1:500]

# perform PCA amd select first two components
pr <- prcomp(ve[select,])
pc <- data.frame(pr$rotation[,1:2])
pc$sample <- rownames(pc)

# attach labels
pc <- left_join(pc, celltype, by = c("sample" = "SRX_ID"))
pc$cell_type[pc$sample %in% kmt2a_samples] <- "KMT2A-PTD"
pc$cell_type[pc$sample %in% nup_samples] <- "NUP98-NSD1"
pc$cell_type[pc$sample %in% aml_controls] <- "CBF AML controls"
pc <- pc[!is.na(pc$cell_type),]

# make colour mappings
cols <- brewer.pal(8, "RdBu")
names(cols) <- c("KMT2A-PTD",
                 "CBF AML controls",
                 "Granulocytes",
                 "Monocytes",
                 "Total white blood cells",
                 "T-cells",
                 "B-cells")
pc$cell_type <- factor(pc$cell_type, levels = names(cols))

# plot
ggplot(pc, aes(PC1, PC2, colour = cell_type)) +
    geom_point(size = 2.5) +
    theme_bw() +
    scale_color_manual(values = cols) +
    theme(legend.title = element_blank()) +
    geom_point(data = pc[pc$sample %in% reduced_normal_controls,],
               shape = 1, size = 5, fill = NA, colour = 'darkgrey')

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] matrixStats_0.54.0 limma_3.38.3       tximport_1.10.1   
[4] RColorBrewer_1.1-2 ggplot2_3.1.0      stringr_1.4.0     
[7] here_0.1           dplyr_0.8.1        data.table_1.12.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1       compiler_3.5.1   pillar_1.3.1     later_1.0.0     
 [5] git2r_0.26.1     plyr_1.8.4       workflowr_1.6.1  tools_3.5.1     
 [9] digest_0.6.18    jsonlite_1.6     evaluate_0.13    tibble_2.1.1    
[13] gtable_0.3.0     pkgconfig_2.0.2  rlang_0.4.2      yaml_2.2.0      
[17] xfun_0.5         withr_2.1.2      knitr_1.22       hms_0.4.2       
[21] fs_1.2.7         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[25] glue_1.3.1       R6_2.4.0         rmarkdown_1.12   readr_1.3.1     
[29] purrr_0.3.2      magrittr_1.5     codetools_0.2-16 backports_1.1.3 
[33] scales_1.0.0     promises_1.1.0   htmltools_0.3.6  assertthat_0.2.1
[37] colorspace_1.4-1 httpuv_1.5.2     labeling_0.3     stringi_1.4.3   
[41] lazyeval_0.2.2   munsell_0.5.0    crayon_1.3.4