-
Notifications
You must be signed in to change notification settings - Fork 1
/
EUCLIDEAN_distances_boxplots.R
181 lines (133 loc) · 6.24 KB
/
EUCLIDEAN_distances_boxplots.R
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
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
setwd("data/")
library(gplots)
library(pvclust)
ultra_lals <- c("03", "04", "07", "08", "13", "15", "16", "20", "25", "29", "33", "45", "46", "47", "49", "58")
ultra_lals <- paste0("LAL.00", ultra_lals)
total_ultra_lals <- 32
#### SNPs ####
genome_studio_output <- read.table("LAL_Omni5_CNV_16p.csv", sep = "\t", stringsAsFactors = F, header = T)
# last line is empty
genome_studio_output <- genome_studio_output[1:(nrow(genome_studio_output)-1), ]
snps <- genome_studio_output[, grepl(pattern = "GType", colnames(genome_studio_output))]
colnames(snps) <- gsub(pattern = ".GType", replacement = "", colnames(snps))
snps <- t(snps)
snps[snps == "NC"] <- NA
snps[snps == "AA"] <- 0
snps[snps == "AB"] <- 1
snps[snps == "BB"] <- 2
snps_numeric <- c()
for(i in 1:nrow(snps)) {
snps_numeric <- rbind(snps_numeric, as.integer(snps[i, ]))
message(i)
}
rownames(snps_numeric) <- rownames(snps)
dist_snps <- dist(snps_numeric, method = "euclidean")
save(dist_snps, file = "dist_snps.RData")
#### CNVs ####
cnvs <- genome_studio_output[, grepl("CNV.Value", colnames(genome_studio_output))]
colnames(cnvs) <- gsub(pattern = ".CNV.Value", replacement = "", colnames(cnvs))
cnvs <- t(cnvs)
dist_cnvs <- dist(cnvs, method = "euclidean")
save(dist_cnvs, file = "dist_cnvs.RData")
#### Methylation
bvalues <- read.csv("b_values.csv", sep = ",", stringsAsFactors = F, row.names = 1)
bvalues <- as.matrix(bvalues)
dist_bvals_all <- dist(t(bvalues),
method = "euclidean")
save(dist_bvals_all, file = "dist_bvals_all.RData")
#### Microbiome ####
# Qualitative
qualit_meta <- read.table("table_diff_alpha_metag.csv", sep = ",", stringsAsFactors = F, header = T, row.names = 1)
qualit_meta <- as.matrix(t(qualit_meta))
dist_qualit <- dist(qualit_meta, method = "euclidean")
save(dist_qualit, file = "dist_qualit.RData")
# Quantitative
quant_meta <- read.table("table_metag16p_proportions.csv", sep = ",", stringsAsFactors = F, header = T, row.names = 1)
quant_meta <- as.matrix(quant_meta)
dist_quanti <- dist(t(quant_meta), method = "euclidean")
save(dist_quanti, file = "dist_quanti.RData")
# Questionnaire (normalized to 1 numeric data)
# quest <- read.table("~/_projects/Look_alike_multiomics/SAMPLE_INFORMATION_LOOKALIKE/norm_to_1_curated_questionaire_all_numeric_with_Age_Height_Weight_ULTRALALs_only.csv",
# sep = ",", stringsAsFactors = F, header = T)
#
# quest <- read.table("OUTPUT_lookalike_form_numeric.csv",
# sep = ",", stringsAsFactors = F, header = T)
load("lookalike_form_normalized_to_1.RData")
norm1_df <- norm1_df[order(norm1_df$ID_PEBC), ]
rownames(norm1_df) <- gsub(norm1_df$ID_PEBC, pattern = "-", replacement = ".")
norm1_df$pair_id <- gsub(norm1_df$pair_id, pattern = "-", replacement = ".")
norm1_df <- norm1_df[norm1_df$pair_id %in% ultra_lals, ]
norm1_df <- norm1_df[, 3:ncol(norm1_df)]
norm1_df <- as.matrix(norm1_df)
dist_quest <- dist(norm1_df, method = "euclidean")
save(dist_quest, file = "dist_quest.RData")
pdf("boxplots_similarity_6_data_types.pdf", width = 10)
par(mfrow = c(2,3), mar = c(4,4,3,1))
for(y in c("qualit", "quanti", "snps", "cnvs", "bvals_all", "quest")) {
load(paste0("dist_", y ,".RData"))
distances <- get(paste0("dist_", y))
plot_title <- c("qualit" = "Metagenome (qualitative)",
"quanti" = "Metagenome (quantitative)",
"snps" = "SNPs",
"cnvs" = "CNVs",
"bvals_all" = "Methylome",
"quest" = "Questionnaire")[y]
valores_por_columna <- list()
x <- 0
for(col_num in 1:(total_ultra_lals -1)) {
first_value <- x+1
last_value <- x+total_ultra_lals-col_num
valores_por_columna[[col_num]] <- c(rep(NA, (total_ultra_lals -1)-length(first_value:last_value)),
distances[first_value:last_value])
x <- x+total_ultra_lals-col_num
}
dist_matrix <- do.call("cbind", valores_por_columna)
colnames(dist_matrix) <- labels(distances)[1:(total_ultra_lals-1)]
rownames(dist_matrix) <- labels(distances)[2:total_ultra_lals]
### Are samples more similar to their than with the others?
matrix_extra <- dist_matrix
for(lal in ultra_lals) {
lal_A <- paste0(lal, ".A")
lal_B <- paste0(lal, ".B")
if(lal_A %in% rownames(matrix_extra) & lal_A %in% colnames(matrix_extra)) matrix_extra[lal_A, lal_A] <- NA
if(lal_B %in% rownames(matrix_extra) & lal_B %in% colnames(matrix_extra)) matrix_extra[lal_B, lal_B] <- NA
if(lal_A %in% rownames(matrix_extra) & lal_B %in% colnames(matrix_extra)) matrix_extra[lal_A, lal_B] <- NA
if(lal_B %in% rownames(matrix_extra) & lal_A %in% colnames(matrix_extra)) matrix_extra[lal_B, lal_A] <- NA
}
# intra and extra scores
intra_pair <- c()
for(lal in ultra_lals) {
a <- NA
b <- NA
lal_A <- paste0(lal, ".A")
lal_B <- paste0(lal, ".B")
if(lal_B %in% rownames(matrix_extra) & lal_A %in% colnames(matrix_extra)) a <- dist_matrix[lal_B, lal_A]
if(lal_A %in% rownames(matrix_extra) & lal_B %in% colnames(matrix_extra)) {
b <- dist_matrix[lal_A, lal_B]
} else { b <- NA }
# print(c(a,b))
intra_pair <- c(intra_pair, c(a,b)[!is.na(c(a,b))])
}
extra_pair <- c(matrix_extra[!is.na(matrix_extra)])
# pdf("intra_extra_pair_distances_boxplot_prueba.pdf")
lista <- list("Distance\nwithin pairs" = intra_pair, "Distance with\nother individuals" = extra_pair)
ymax <- max(boxplot(lista, plot = F)$stats)
ymin <- min(boxplot(lista, plot = F)$stats)
bp <- boxplot(lista,
main = plot_title,
ylab = "Euclidean distance",
xaxt = "n",
outline = F,
ylim = c(ymin-0.05*(ymax-ymin), ymax + 0.1*(ymax-ymin)),
lwd = 1.25, col = "gray70")
axis(side = 1, at = c(1,2), labels = c("Distance\nwithin pairs\n(intra-pair)", "Distance with\nother individuals\n(extra-pair)"),
tick = F, line = 1.5)
pval <- t.test(intra_pair, extra_pair)$p.value
if(pval < 0.05) {
arrows(1, ymax + 0.05*(ymax-ymin),2, ymax + 0.05*(ymax-ymin), code = 3, angle = 90, length = 0.07, lwd = 1.5)
label <- ifelse(pval < 0.05, yes = paste0("P = ", round(pval, 5)), no = "n.s.")
text(x = 1.5, y = ymax + 0.1*(ymax-ymin), labels = label, cex = 1.1)
}
# dev.off()
}
dev.off()