# Clear the current R environment
rm(list=ls())
## Load required R packages
library(ComplexHeatmap)
## 载入需要的程序包：grid
## ========================================
## ComplexHeatmap version 2.22.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
##
## The new InteractiveComplexHeatmap package can directly export static
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================
library(shinipsum)
## Warning: 程序包'shinipsum'是用R版本4.4.3 来建造的
library(ggplot2)
## Warning: 程序包'ggplot2'是用R版本4.5.1 来建造的
library(ggstatsplot)
## Warning: 程序包'ggstatsplot'是用R版本4.4.3 来建造的
## You can cite this package as:
## Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
## Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167
library(patchwork)
## Warning: 程序包'patchwork'是用R版本4.5.1 来建造的
library(reshape2)
library(stringr)
library(limma)
library(tidyverse)
## Warning: 程序包'forcats'是用R版本4.5.1 来建造的
## -- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
## v dplyr 1.1.4 v readr 2.1.5
## v forcats 1.0.0 v tibble 3.2.1
## v lubridate 1.9.4 v tidyr 1.3.1
## v purrr 1.0.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Check and set timeout for downloading (if needed)
getOption('timeout')
## [1] 60
options(timeout=10000)
# Import expression data, skipping the first 3 rows (likely metadata or header info)
data <- readxl::read_excel("BLOOD_BLD-2023-023644-mmc3.xlsx", skip = 3)
dim(data) # Check dimensions
## [1] 2067 11
data[1:6, 1:6] # Preview first few rows and columns
## # A tibble: 6 x 6
## gene_id cluster gene_name sgLuc_DMSO_rep1 sgLuc_DMSO_rep2 sgLuc_VTP_rep1
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 ENSG00000002~ cluste~ SNX11 9.41 9.28 9.78
## 2 ENSG00000005~ cluste~ HOXA11 8.62 8.65 9.68
## 3 ENSG00000005~ cluste~ MPO 10.5 10.6 12.3
## 4 ENSG00000008~ cluste~ MMP25 9.90 9.96 11.4
## 5 ENSG00000011~ cluste~ TYROBP 11.2 11.1 12.0
## 6 ENSG00000013~ cluste~ MAN2B2 9.82 9.96 10.5
# Define marker genes for each cluster
marker <- list(
cluster1 = c("CTSA", "SMPD1"),
cluster2 = c("CD14", "CD86", "FGR", "TLR8", "ITGAM"),
cluster3 = c("MEIS1", "PBX3", "MEF2C"),
cluster4 = c("MYC", "LRP5", "RUNX3")
)
marker
## $cluster1
## [1] "CTSA" "SMPD1"
##
## $cluster2
## [1] "CD14" "CD86" "FGR" "TLR8" "ITGAM"
##
## $cluster3
## [1] "MEIS1" "PBX3" "MEF2C"
##
## $cluster4
## [1] "MYC" "LRP5" "RUNX3"
## Style settings for gene name annotations
tcol <- c("black", "black", "#34b3e6", "#f46951") # Text color per cluster
bg <- c("#e8f8f8", "#fdefe9", "#e9f6fb", "#fde8e9") # Background color per cluster
# Convert gene lists into styled data frames for textbox annotations
for(i in 1:length(marker)) {
marker[[i]] <- data.frame(
text = marker[[i]],
fontsize = 12,
col = tcol[i],
fontface = "italic"
)
}
marker
## $cluster1
## text fontsize col fontface
## 1 CTSA 12 black italic
## 2 SMPD1 12 black italic
##
## $cluster2
## text fontsize col fontface
## 1 CD14 12 black italic
## 2 CD86 12 black italic
## 3 FGR 12 black italic
## 4 TLR8 12 black italic
## 5 ITGAM 12 black italic
##
## $cluster3
## text fontsize col fontface
## 1 MEIS1 12 #34b3e6 italic
## 2 PBX3 12 #34b3e6 italic
## 3 MEF2C 12 #34b3e6 italic
##
## $cluster4
## text fontsize col fontface
## 1 MYC 12 #f46951 italic
## 2 LRP5 12 #f46951 italic
## 3 RUNX3 12 #f46951 italic
str(marker)
## List of 4
## $ cluster1:'data.frame': 2 obs. of 4 variables:
## ..$ text : chr [1:2] "CTSA" "SMPD1"
## ..$ fontsize: num [1:2] 12 12
## ..$ col : chr [1:2] "black" "black"
## ..$ fontface: chr [1:2] "italic" "italic"
## $ cluster2:'data.frame': 5 obs. of 4 variables:
## ..$ text : chr [1:5] "CD14" "CD86" "FGR" "TLR8" ...
## ..$ fontsize: num [1:5] 12 12 12 12 12
## ..$ col : chr [1:5] "black" "black" "black" "black" ...
## ..$ fontface: chr [1:5] "italic" "italic" "italic" "italic" ...
## $ cluster3:'data.frame': 3 obs. of 4 variables:
## ..$ text : chr [1:3] "MEIS1" "PBX3" "MEF2C"
## ..$ fontsize: num [1:3] 12 12 12
## ..$ col : chr [1:3] "#34b3e6" "#34b3e6" "#34b3e6"
## ..$ fontface: chr [1:3] "italic" "italic" "italic"
## $ cluster4:'data.frame': 3 obs. of 4 variables:
## ..$ text : chr [1:3] "MYC" "LRP5" "RUNX3"
## ..$ fontsize: num [1:3] 12 12 12
## ..$ col : chr [1:3] "#f46951" "#f46951" "#f46951"
## ..$ fontface: chr [1:3] "italic" "italic" "italic"
# Pathway or functional annotations for each cluster
annotation <- list(
cluster1 = c("Lysosome"),
cluster2 = c("CD14 Positive Monocyte", "CD33 Positive Myeloid", "Regulation of Inflammatory Response",
"Hematopoietic cell lineage", "Increased Monocyte Cell number"),
cluster3 = c("KMT2A Fusion Target Genes", "MLL-AF6-Spreading Target Genes"),
cluster4 = c("Ribosome Biogenesis", "Myc Targets V2")
)
# Convert annotation list into styled data frames
for(i in 1:length(annotation)) {
annotation[[i]] <- data.frame(
text = annotation[[i]],
fontsize = 11.3,
col = "black"
)
}
annotation
## $cluster1
## text fontsize col
## 1 Lysosome 11.3 black
##
## $cluster2
## text fontsize col
## 1 CD14 Positive Monocyte 11.3 black
## 2 CD33 Positive Myeloid 11.3 black
## 3 Regulation of Inflammatory Response 11.3 black
## 4 Hematopoietic cell lineage 11.3 black
## 5 Increased Monocyte Cell number 11.3 black
##
## $cluster3
## text fontsize col
## 1 KMT2A Fusion Target Genes 11.3 black
## 2 MLL-AF6-Spreading Target Genes 11.3 black
##
## $cluster4
## text fontsize col
## 1 Ribosome Biogenesis 11.3 black
## 2 Myc Targets V2 11.3 black
# Adjusted p-values for each pathway annotation
adj_p_values <- list(
cluster1 = c(1.862e-9),
cluster2 = c(6.094e-55, 9.956e-47, 2.308e-12, 3.595e-8, 5.908e-5),
cluster3 = c(4.557e-11, 1.715e-7),
cluster4 = c(1.030e-16, 2.745e-14)
)
# Format p-values as text annotation
for(i in 1:length(adj_p_values)) {
adj_p_values[[i]] <- data.frame(
text = as.character(adj_p_values[[i]]),
fontsize = 11.3,
col = "black"
)
}
adj_p_values
## $cluster1
## text fontsize col
## 1 1.862e-09 11.3 black
##
## $cluster2
## text fontsize col
## 1 6.094e-55 11.3 black
## 2 9.956e-47 11.3 black
## 3 2.308e-12 11.3 black
## 4 3.595e-08 11.3 black
## 5 5.908e-05 11.3 black
##
## $cluster3
## text fontsize col
## 1 4.557e-11 11.3 black
## 2 1.715e-07 11.3 black
##
## $cluster4
## text fontsize col
## 1 1.03e-16 11.3 black
## 2 2.745e-14 11.3 black
## Heatmap construction
# Extract numeric matrix and set gene symbols as row names
mat <- as.matrix(data[,-c(1:3)])
rownames(mat) <- data$gene_id
dim(mat)
## [1] 2067 8
head(mat)
## sgLuc_DMSO_rep1 sgLuc_DMSO_rep2 sgLuc_VTP_rep1 sgLuc_VTP_rep2
## ENSG00000002919 9.408104 9.282241 9.777550 9.773726
## ENSG00000005073 8.615269 8.647978 9.678552 9.699707
## ENSG00000005381 10.534899 10.625492 12.303269 12.287824
## ENSG00000008516 9.897188 9.964641 11.437336 11.438829
## ENSG00000011600 11.168022 11.145130 12.036846 11.966498
## ENSG00000013288 9.819726 9.957936 10.501483 10.472438
## sgPCGF1_DMSO_rep1 sgPCGF1_DMSO_rep2 sgPCGF1_VTP_rep1
## ENSG00000002919 9.521928 9.665968 9.644823
## ENSG00000005073 9.152883 9.107757 9.240887
## ENSG00000005381 10.199645 10.312349 12.854775
## ENSG00000008516 10.568840 10.616849 11.108043
## ENSG00000011600 11.384284 11.590741 11.650581
## ENSG00000013288 10.748916 10.663170 10.832866
## sgPCGF1_VTP_rep2
## ENSG00000002919 9.614058
## ENSG00000005073 9.254842
## ENSG00000005381 13.106643
## ENSG00000008516 11.034186
## ENSG00000011600 11.572871
## ENSG00000013288 10.974700
# Normalize gene expression (Z-score by row/gene)
mat <- t(scale(t(mat)))
# Clean column names (remove "_rep")
colnames(mat) <- str_split(colnames(mat), "_rep", simplify = TRUE)[,1]
# Row cluster group labels
split <- data$cluster
split
## [1] "cluster1" "cluster1" "cluster1" "cluster1" "cluster1" "cluster1"
## [7] "cluster1" "cluster1" "cluster1" "cluster1" "cluster1" "cluster1"
## [13] "cluster1" "cluster1" "cluster1" "cluster1" "cluster1" "cluster1"
## [19] "cluster1" "cluster1" "cluster1" "cluster1" "cluster1" "cluster1"
## [25] "cluster1" "cluster1" "cluster1" "cluster1" "cluster1" "cluster1"
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## [37] "cluster1" "cluster1" "cluster1" "cluster1" "cluster1" "cluster1"
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# Create textbox annotation for genes (left panel)
ha <- rowAnnotation(
textbox1 = anno_textbox(
split, marker,
add_new_line = TRUE,
word_wrap = TRUE,
line_space = unit(3, "mm"),
background_gp = gpar(fill = bg, col = NA),
by = "anno_block"
)
)
# Create textbox annotation for functional/pathway descriptions
ha1 <- rowAnnotation(
textbox2 = anno_textbox(
split, annotation,
add_new_line = TRUE,
word_wrap = TRUE,
line_space = unit(3, "mm"),
background_gp = gpar(fill = bg, col = NA),
by = "anno_block"
)
)
# Create textbox annotation for adjusted p-values
ha2 <- rowAnnotation(
textbox3 = anno_textbox(
split, adj_p_values,
add_new_line = TRUE,
word_wrap = TRUE,
line_space = unit(3, "mm"),
background_gp = gpar(fill = bg, col = NA),
by = "anno_block"
)
)
# Start plotting to PDF
pdf(file = "Fig4.pdf", height = 7, width = 11)
# Create heatmap and combine with annotations
ht_list = Heatmap(
mat, name = "mat",
cluster_rows = FALSE,
row_split = split,
show_row_names = FALSE,
cluster_columns = FALSE,
row_title_rot = 0,
row_title_gp = gpar(fontsize = 12),
heatmap_legend_param = list(direction = "horizontal", nrow = 1),
column_names_side = "top",
right_annotation = ha
) + ha1 + ha2
# Draw heatmap with legend placed at the bottom
draw(ht_list, heatmap_legend_side = "bottom")
# Add titles to each annotation textbox
decorate_annotation("textbox1", {
grid.text("Genes", y = unit(1, "npc") + unit(2, "mm"), just = "bottom")
})
decorate_annotation("textbox2", {
grid.text("Annotation", y = unit(1, "npc") + unit(2, "mm"), just = c("right", "bottom"))
})
decorate_annotation("textbox3", {
grid.text("Adj.Pvalue", y = unit(1, "npc") + unit(2, "mm"), just = "bottom")
})
# Return heatmap object (optional)
ht_list
# Close PDF output
dev.off()
## png
## 2