Rscripts/Heatmap.R at main · OmicsSuite/Rscripts · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
# Author: Benben Miao
# Email: benben.miao@outlook.com
# Rversion: 4.2.2
# Date: 2023-02-13

# -> 0. Install and Library
# install.packages(c("ggplot2", "ggsci"))
# install.packages("circlize")
# BiocManager::install("ComplexHeatmap")
library(circlize)
library(ComplexHeatmap)
library(ggplot2)
# <- 0. Install and Library

options(warn = 1)

# -> 1. File Read
file_path = "data/Heatmap/Heatmap.txt"
file_format = "txt"
# "xlsx", "xls", "txt", "csv"

if (file_format == "xlsx" | file_format == "xls") {
	data <- readxl::read_excel(path = file_path,
							   sheet = NULL,
							   col_names = TRUE,
							   na = "",
							   progress = readxl::readxl_progress()
	)
} else if (file_format == "txt" | file_format == "csv") {
	data <- read.table(file = file_path,
					   header = TRUE,
					   sep = "\t",
					   stringsAsFactors = F,
					   row.names = 1
	)
} else if (file_format == "csv") {
	data <- read.table(file = file_path,
					   header = TRUE,
					   sep = ",",
					   stringsAsFactors = F
	)
}
# <- 1. File Read

# -> 2. Data Operation
# set.seed(123)
# nr1 = 4; nr2 = 8; nr3 = 6; nr = nr1 + nr2 + nr3
# nc1 = 6; nc2 = 8; nc3 = 10; nc = nc1 + nc2 + nc3
# mat = cbind(rbind(matrix(rnorm(nr1*nc1, mean = 1,   sd = 0.5), nr = nr1),
# 				  matrix(rnorm(nr2*nc1, mean = 0,   sd = 0.5), nr = nr2),
# 				  matrix(rnorm(nr3*nc1, mean = 0,   sd = 0.5), nr = nr3)),
# 			rbind(matrix(rnorm(nr1*nc2, mean = 0,   sd = 0.5), nr = nr1),
# 				  matrix(rnorm(nr2*nc2, mean = 1,   sd = 0.5), nr = nr2),
# 				  matrix(rnorm(nr3*nc2, mean = 0,   sd = 0.5), nr = nr3)),
# 			rbind(matrix(rnorm(nr1*nc3, mean = 0.5, sd = 0.5), nr = nr1),
# 				  matrix(rnorm(nr2*nc3, mean = 0.5, sd = 0.5), nr = nr2),
# 				  matrix(rnorm(nr3*nc3, mean = 1,   sd = 0.5), nr = nr3))
# )
# mat = mat[sample(nr, nr), sample(nc, nc)] # random shuffle rows and columns
# rownames(mat) = paste0("row", seq_len(nr))
# colnames(mat) = paste0("column", seq_len(nc))
#
# small_mat = mat[1:9, 1:9]
#
# write.table(small_mat, file = "Heatmap.txt", quote = FALSE, sep = "\t", row.names = TRUE)
data <- as.matrix(data)
# <- 2. Data Operation

# -> 3. Plot Parameters
fonts <- "Times"
# ChoiceBox: "Times", "Palatino", "Bookman", "Courier", "Helvetica", "URWGothic", "NimbusMon", "NimbusSan"

lowColor <- "#008800"
	# ColorPicker

midColor <- "#ffffff"
	# ColorPicker

highColor <- "#0000ff"
	# ColorPicker

textSize <- 10
# Slider: 10, 0, 90, 1

clusterMethod <- "complete"
# ChoiceBox: "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid"

distanceMethod <- "euclidean"
# ChoiceBox: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"
# <- 3. Plot Parameters

# # -> 4. Plot
col_fun = colorRamp2(c(round(min(data)), 0, round(max(data))),
					 c(lowColor, midColor, highColor))

p1 <- Heatmap(data,
			col = col_fun,
			name = "ColorBar",
			na_col = "grey",
			color_space = "LAB",
			rect_gp = gpar(col = NA),
			border = NA,
			border_gp = gpar(col = "black"),
			cell_fun = function(j, i, x, y, width, height, fill) {
				grid.text(sprintf("%.1f", data[i, j]), x, y, gp = gpar(fontsize = textSize))
			},
			cluster_rows = TRUE,
			cluster_row_slices = TRUE,
			clustering_distance_rows = distanceMethod,
			clustering_method_rows = clusterMethod,
			row_dend_side = "left",
			row_dend_width = unit(20, "mm"),
			show_row_dend = TRUE,
			# row_dend_reorder = is.logical(cluster_rows) || is.function(cluster_rows),
			row_dend_gp = gpar(),
			cluster_columns = TRUE,
			cluster_column_slices = TRUE,
			clustering_distance_columns = distanceMethod,
			clustering_method_columns = clusterMethod,
			column_dend_side = "top",
			column_dend_height = unit(20, "mm"),
			show_column_dend = TRUE,
			column_dend_gp = gpar()
			# column_dend_reorder = is.logical(cluster_columns) || is.function(cluster_columns)
			)
# # <- 4. Plot

# -> 5. Save parameters
pdf_name <- "results.pdf"
jpeg_name <- "results.jpeg"
width <- 10.00
height <- 6.18
units <- "in"
font <- fonts
# <- 5. Save parameters

# -> 6. PDF
pdf(file = pdf_name,
	width = width,
	height = height,
	family = font
)
p <- plot(p1)
p
dev.off()
# <- 6. PDF

# -> 7. JPEG
jpeg(filename = jpeg_name,
	 width = width,
	 height = height,
	 units = units,
	 res = 300,
	 quality = 100,
	 family = font
)
p <- plot(p1)
p
dev.off()
# <- 7. JPEG