Leucegene Gene Expression
Last updated: 2021-04-30
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Knit directory: MINTIE-paper-analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 41431cc | Marek Cmero | 2020-07-07 | Build site. |
| Rmd | f16b530 | Marek Cmero | 2020-07-07 | Added 5 missing leucegene AML controls to gene expression analysis |
| html | 4b8113e | Marek Cmero | 2020-07-03 | Build site. |
| html | e9e4917 | Marek Cmero | 2020-06-24 | Build site. |
| 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 | 3702862 | Marek Cmero | 2020-05-18 | Removed MLM samples from final B-ALL results |
| html | a166ab8 | Marek Cmero | 2020-05-08 | Build site. |
| html | a600688 | Marek Cmero | 2020-05-07 | Build site. |
| 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. |
| html | 784838b | Marek Cmero | 2020-04-30 | Build site. |
| Rmd | c4c3844 | Marek Cmero | 2020-04-30 | Added leucegene gene expression notebook |
# util
library(data.table)
library(dplyr)
library(here)
library(stringr)
# plotting/tables
library(ggplot2)
library(RColorBrewer)
library(gt)
# bioinformatics/stats helpers
library(tximport)
library(limma)
library(edgeR)
library(matrixStats)options(stringsAsFactors = FALSE)source(here("code/leucegene_helper.R"))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 output is not provided in the repository and must be generated by the user.
salmon_dir <- here("data/leucegene/salmon_out")
# construct list of quant.sf files
quant_files <- list.files(salmon_dir,
full.names = TRUE,
recursive = TRUE,
pattern = "quant.sf")
# 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/sample_info/celltypes_info.tsv"))
kmt2a_samples <- read.delim(here("data/leucegene/sample_info/KMT2A-PTD_samples.txt"), header = FALSE)$V1
aml_controls <- read.delim(here("data/leucegene/sample_info/selected_13_CBF_AML_controls.txt"), header = FALSE)$V1
nup_samples <- read.delim(here("data/leucegene/sample_info/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$cell_type == "Granulocytes"][1]
reduced_normal_controls <- c(s1, s2, s3)PCA plot
MINTIE paper Supplememtary Figure 1. PCA of gene expression derived from Salmon quantification for Leucegene KMT2A-PTD and normals. Normal samples used as controls in the reduced set of controls are circled.
# get counts matrix
counts <- txi$counts
colnames(counts) <- list.files(salmon_dir)
write.table(counts, file = here("output/Leucegene_gene_counts.tsv"), sep = "\t", quote = FALSE, row.names = FALSE)
# 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$sample %in% reduced_normal_controls,],
shape = 1, size = 5, fill = NA, colour = 'darkgrey')
Expressed genes
Number of expressed genes found in Leucegene normals and percentage of those genes that are protein coding.
# load CHESS gene reference containing gene types
chess_genes <- get_chess_genes(gzfile(here("data/ref/chess2.2.genes.gz")))
# construct a normal counts matrix of "expressed" counts
# (>1 CPM in at least one sample)
normal_counts <- counts[,colnames(counts) %in% celltype$SRX_ID]
keep <- rowSums(cpm(normal_counts) > 1) >= 1
normal_counts <- normal_counts[keep,]
# tally gene types
expressed_genes <- rownames(normal_counts)
gene_types <- filter(chess_genes, gene %in% expressed_genes) %>%
group_by(Gene_Type) %>%
summarise(gene_count = length(unique(GFF_ID)))
gene_types <- rbind(gene_types, c("Total", sum(gene_types$gene_count)))
# results table
data.frame(gene_types) %>%
gt() %>%
tab_header(
title = md("**Gene classifications**")
) %>%
tab_options(
table.font.size = 12
) %>%
cols_label(
Gene_Type = md("**Gene type**"),
gene_count = md("**Count**")
)| Gene classifications | |
|---|---|
| Gene type | Count |
| antisense_RNA | 146 |
| lncRNA | 4116 |
| misc_RNA | 601 |
| protein_coding | 15030 |
| Total | 19893 |
n_protein_coding <- filter(gene_types, Gene_Type == "protein_coding") %>% select(gene_count)
print(paste("Proportion of expressed genes that are protein coding:",
(as.numeric(n_protein_coding) / length(expressed_genes)) %>% round(4)))[1] "Proportion of expressed genes that are protein coding: 0.7439"
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] matrixStats_0.58.0 edgeR_3.32.1 limma_3.46.0 tximport_1.18.0
[5] gt_0.2.2 RColorBrewer_1.1-2 ggplot2_3.3.3 stringr_1.4.0
[9] here_1.0.1 dplyr_1.0.4 data.table_1.13.6
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 locfit_1.5-9.4 xfun_0.21 purrr_0.3.4
[5] lattice_0.20-41 colorspace_2.0-0 vctrs_0.3.6 generics_0.1.0
[9] htmltools_0.5.1.1 yaml_2.2.1 rlang_0.4.10 later_1.1.0.1
[13] pillar_1.4.7 glue_1.4.2 withr_2.4.1 DBI_1.1.1
[17] lifecycle_1.0.0 commonmark_1.7 munsell_0.5.0 gtable_0.3.0
[21] workflowr_1.6.2 evaluate_0.14 labeling_0.4.2 knitr_1.31
[25] httpuv_1.5.5 highr_0.8 Rcpp_1.0.6 readr_1.4.0
[29] promises_1.2.0.1 scales_1.1.1 backports_1.2.1 checkmate_2.0.0
[33] jsonlite_1.7.2 farver_2.0.3 fs_1.5.0 hms_1.0.0
[37] digest_0.6.27 stringi_1.5.3 grid_4.0.3 rprojroot_2.0.2
[41] tools_4.0.3 sass_0.3.1 magrittr_2.0.1 tibble_3.0.6
[45] crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[49] assertthat_0.2.1 rmarkdown_2.6 R6_2.5.0 git2r_0.28.0
[53] compiler_4.0.3