-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathplotcrustes_Food_v_MB_current.R
238 lines (202 loc) · 10.9 KB
/
plotcrustes_Food_v_MB_current.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
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
# Generate a more logical plot for procrustes comparisons
### By Robin Shields-Cutler
### Feburary 2018
library(ape)
library(vegan)
library(ggplot2)
# Read in beta diversity tables
unw_B <- read.delim('../IMP/data/unweighted_unifrac_dm.txt',
header=1, row.names = 1, check.names = F)
unw_A <- read.delim('../IMP/data/uwuf_food_dm.txt',
header=1, row.names = 1, check.names = F)
# Metadata needs three columns -
# 1. sample IDs (e.g. "P21_pre_stool")
# 2. the unifying participant/unit ID (e.g."P21")
# 3. the binary metadata group (e.g. "pre")
meta <- read.delim('../IMP/data/SampleIDs-CS.txt',
header=F, check.names = F)
colnames(meta) <- 'participant'
meta_food <- meta
meta$data_type <- 'microbiome'
meta$sampleID <- unlist(lapply(X=meta$participant, FUN = function(xx) paste0(xx, '_microbiome')))
meta_food$data_type <- 'food'
meta_food$sampleID <- unlist(lapply(X=meta_food$participant, FUN = function(xx) paste0(xx, '_food')))
meta <- rbind(meta, meta_food)
meta <- tibble::column_to_rownames(meta, var = 'sampleID')
thing <- 'participant'
group <- 'data_type'
groups <- c('microbiome','food') # Names of the two distance matrices/categories
metaA <- meta[meta[,group] == groups[2],] # Split the metadata into the two groups
metaB <- meta[meta[,group] == groups[1],]
metaA <- metaA[order(metaA[,thing]),] # Sort the metadata by unifying ID
metaB <- metaB[order(metaB[,thing]),]
# CRITICAL:
# Ensure that the original distance matrices are in the same order by participant
# Uses the order generated from the metadata dataframe
rownames(unw_B) <- unlist(lapply(X=rownames(unw_B), FUN = function(xx) paste0(xx, '_microbiome')))
colnames(unw_B) <- unlist(lapply(X=colnames(unw_B), FUN = function(xx) paste0(xx, '_microbiome')))
rownames(unw_A) <- unlist(lapply(X=rownames(unw_A), FUN = function(xx) paste0(xx, '_food')))
colnames(unw_A) <- unlist(lapply(X=colnames(unw_A), FUN = function(xx) paste0(xx, '_food')))
unw_A <- unw_A[rownames(metaA),rownames(metaA)]
unw_B <- unw_B[rownames(metaB),rownames(metaB)]
# Get the principal coordinates
pcoa_A <- pcoa(unw_A)$vectors
for(c in 1:ncol(pcoa_A)){
colnames(pcoa_A)[c] <- paste0("PC",c)
}
pcoa_B <- pcoa(unw_B)$vectors
for(c in 1:ncol(pcoa_B)){
colnames(pcoa_B)[c] <- paste0("PC",c)
}
set.seed(11)
crusty <- procrustes(pcoa_A, pcoa_B, symmetric = T) # Run Procrustes
set.seed(11)
crust_test_p <- protest(pcoa_A, pcoa_B, permutations = how(nperm = 999))$signif
A_crust <- data.frame(crusty$X) # Recover the first group's coordinates
B_crust <- data.frame(crusty$Yrot) # Recover the second group's coordinates
colnames(B_crust) <- colnames(A_crust)
ncoords = as.numeric(ncol(A_crust))
A_crust <- merge(A_crust, metaA, by=0)
B_crust <- merge(B_crust, metaB, by=0)
rownames(A_crust) <- A_crust[,1]; A_crust[,1] <- NULL # Merge makes rownames column
rownames(B_crust) <- B_crust[,1]; B_crust[,1] <- NULL
sample_ids <- A_crust[,thing] # Get all the unifying participant IDs
real_dist <- data.frame(matrix(nrow = length(sample_ids), ncol = 3)) # Initialize the dataframe
colnames(real_dist) <- c('sampleID_A', 'sampleID_B', 'distance')
# Loop through each participant to get the multidimensional distance between their rotated points
for (i in 1:length(sample_ids)) {
ix <- as.character(sample_ids[i])
A_ix <- A_crust[A_crust[,thing] == ix, 1:ncoords] # Keep all the PC axes
B_ix <- B_crust[B_crust[,thing] == ix, 1:ncoords]
AB_mat <- rbind(A_ix, B_ix)
AB_dist <- matrix(dist(AB_mat, method = 'euclidean')) # Calculates the distance
real_dist[i,1] <- as.character(rownames(A_ix)) # Fill in the dataframe
real_dist[i,2] <- as.character(rownames(B_ix))
real_dist[i,3] <- as.numeric(AB_dist[1])
}
A_crust$data_type <- 'food'; B_crust$data_type <- 'microbiome'
A_crust$realperm <- 'real'; B_crust$realperm <- 'real'
# The "Procrustes Distance"
reaL_pro_dist <- sqrt(sum(real_dist$distance^2))
cat(reaL_pro_dist)
### Multiple permutations ####
# Repeat the permutation "j" times
real_dist$real_perm <- 'true_distance'
dist_plot <- real_dist
PCOA_plot_many <- rbind(A_crust, B_crust) # Start with the real data, then add the permutations
procrust_pvals <- vector(mode='numeric', length=9)
procrust_dists <- vector(mode='numeric', length=9)
for (j in 1:9) {
metaBp <- metaB
set.seed(j)
metaBp[,thing] <- sample(x = metaBp[,thing], size = length(metaBp[,thing]), replace = F)
metaBp <- metaBp[order(metaBp[,thing]),]
unw_Bp <- unw_B[rownames(metaBp),rownames(metaBp)]
pcoa_Bp <- pcoa(unw_Bp)$vectors
for(c in 1:ncol(pcoa_Bp)){
colnames(pcoa_Bp)[c] <- paste0("PC",c)
}
set.seed(j)
crusty_p <- procrustes(pcoa_A, pcoa_Bp, symmetric = T)
set.seed(j)
crust_test_perm_p <- protest(pcoa_A, pcoa_Bp, permutations = how(nperm = 999))$signif
cat(paste0(crust_test_perm_p, '\n'))
procrust_pvals[j] <- crust_test_perm_p
Ap_crust <- data.frame(crusty_p$X)
Bp_crust <- data.frame(crusty_p$Yrot)
colnames(Bp_crust) <- colnames(Ap_crust)
Ap_crust <- merge(Ap_crust, metaA, by=0)
Bp_crust <- merge(Bp_crust, metaBp, by=0)
rownames(Ap_crust) <- Ap_crust[,1]; Ap_crust[,1] <- NULL
rownames(Bp_crust) <- Bp_crust[,1]; Bp_crust[,1] <- NULL
sample_ids <- Ap_crust[,thing]
perm_dist <- data.frame(matrix(nrow = length(sample_ids), ncol = 3))
colnames(perm_dist) <- c('sampleID_A', 'sampleID_B', 'distance')
for (i in 1:length(sample_ids)) {
ix <- as.character(sample_ids[i])
# ixp <- as.character(sample_id_perm[i])
Ap_ix <- Ap_crust[Ap_crust[,thing] == ix, 1:ncoords]
Bp_ix <- Bp_crust[Bp_crust[,thing] == ix, 1:ncoords]
ABp_mat <- rbind(Ap_ix, Bp_ix)
ABp_dist <- matrix(dist(ABp_mat, method = 'euclidean'))
perm_dist[i,1] <- as.character(rownames(Ap_ix))
perm_dist[i,2] <- as.character(rownames(Bp_ix))
perm_dist[i,3] <- as.numeric(ABp_dist[1])
}
perm_dist_plot_2 <- perm_dist
perm_dist_plot_2$real_perm <- paste0('permuted',j) # Keep track of the permuted data
dist_plot <- rbind(dist_plot, perm_dist_plot_2)
# Add the permuted data to the existing runs
Ap_crust$data_type <- 'food'; Bp_crust$data_type <- 'microbiome'
Ap_crust$realperm <- paste0('permuted',j); Bp_crust$realperm <- paste0('permuted',j)
PCOA_plot_many <- rbind(PCOA_plot_many, Ap_crust, Bp_crust)
# Save the procrustes distance of the overall dataset
procrust_dist_perm <- sqrt(sum(perm_dist$distance^2))
procrust_dists[j] <- procrust_dist_perm
}
# What's the median permutation?
cat(procrust_pvals)
cat(procrust_dists)
median(procrust_dists)
### Plot the results ####
cols <- c("#A300FF", "#FF0000", "#00A696", "#FE42AD", "#FBB400", "#2E1915")
names(cols) <- c("KarenThai", "HmongThai", "Karen1st", "Hmong1st", "Hmong2nd", "Control" )
# Get more metadata for coloring
meta.big <- read.delim('../IMP/data/mappingIMP.txt', header=1, sep = '\t', check.names = F)
meta.big <- meta.big[meta.big$`#SampleID` %in% as.character(sample_ids), ]
dist_plot$sampleID <- unlist(lapply(X=dist_plot$sampleID_B,
FUN=function(xx) gsub(pattern = '_microbiome', replacement = '', x = xx, fixed = T)))
dist_plot_groups <- merge(dist_plot, meta.big[,c('#SampleID','Sample.Group')], by.x = 'sampleID', by.y='#SampleID')
dist_plot_groups$Sample.Group <- factor(dist_plot_groups$Sample.Group,
levels = c("KarenThai", "HmongThai", "Karen1st", "Hmong1st", "Hmong2nd", "Control"),
ordered = T)
# Boxplot with scatter ALL PERMUTATIONS
pbox <- ggplot(dist_plot, aes(x=real_perm, y=distance, group=real_perm)) +
geom_boxplot(outlier.colour = 'white') + geom_jitter(width = 0.2, alpha=0.20) + theme_classic() +
theme(axis.text.x = element_text(angle=45, hjust=1), axis.title.x = element_blank(),
axis.text = element_text(color='black'))
# To save
ggsave(pbox, filename = 'distance_boxplots_10x.png', width = 10, height = 6, dpi = 300)
# Set of typical procrustes plots ALL PERMUTATIONS
pscat <- ggplot(PCOA_plot_many) + geom_point(aes(x=PC1, y=PC2, color=data_type), size=0.7, alpha=0.6) +
geom_line(aes(x=PC1, y=PC2, group=participant), alpha=0.1) +
theme_classic() + facet_grid(. ~ realperm) +
theme(axis.text = element_text(color='black', size = 6)) + coord_fixed(ratio = 1)
# To save
ggsave(pscat, filename = 'permute10x_groups.png', width = 15, height = 3, dpi = 300)
#### Set of two procrustes plots WITH GROUPS ####
PCOA_plot_many.groups <- merge(PCOA_plot_many, meta.big[,c('#SampleID','Sample.Group')], by.x = 'participant', by.y='#SampleID')
PCOA_plot_many.groups$Sample.Group <- factor(PCOA_plot_many.groups$Sample.Group,
levels = c("KarenThai", "HmongThai", "Karen1st", "Hmong1st", "Hmong2nd", "Control"),
ordered = T)
# Enter the permuted set that features the median procrustes distance
PCOA_plot_many.groups.sub <- PCOA_plot_many.groups[PCOA_plot_many.groups$realperm == 'permuted9' | PCOA_plot_many.groups$realperm == 'real', ]
PCOA_plot_many.groups.sub$realperm[PCOA_plot_many.groups.sub$realperm == 'permuted9'] <- 'Permuted'
PCOA_plot_many.groups.sub$realperm[PCOA_plot_many.groups.sub$realperm == 'real'] <- 'Original'
PCOA_plot_many.groups.sub$realperm <- factor(x = PCOA_plot_many.groups.sub$realperm, levels = c('Original','Permuted'), ordered = T)
pscat.groups <- ggplot(PCOA_plot_many.groups.sub) +
geom_line(aes(x=PC1, y=PC2, group=participant, color=Sample.Group), alpha=0.3) +
geom_point(aes(x=PC1, y=PC2, color=Sample.Group, shape=data_type), size=1.2, alpha=0.7) +
theme_classic() + facet_grid(. ~ realperm) +
theme(axis.text = element_text(color='black', size = 6),
axis.title = element_text(size=6, color='black')) +
coord_fixed(ratio = 1) +
scale_color_manual(values = cols) + theme(legend.title = element_blank())
# To save
ggsave(pscat.groups, filename = '../IMP/results/procrustes_vs_med_permuted_group_colored_v3.png', width = 8.5, height = 4, dpi = 300)
dist_plot_groups.sub <- dist_plot_groups[dist_plot_groups$real_perm == 'permuted9' | dist_plot_groups$real_perm == 'true_distance', ]
dist_plot_groups.sub$real_perm[dist_plot_groups.sub$real_perm == 'permuted9'] <- 'permuted'
dist_plot_groups.sub$real_perm[dist_plot_groups.sub$real_perm == 'true_distance'] <- 'original'
dist_plot_groups.sub$real_perm <- factor(x = dist_plot_groups.sub$real_perm, levels = c('original','permuted'), ordered = T)
pbox.group <- ggplot(dist_plot_groups.sub, aes(x=real_perm, y=distance, group=real_perm)) +
geom_boxplot(outlier.colour = 'white') +
geom_jitter(width = 0.28, alpha=0.45) +
theme_classic() +
scale_color_manual(values=cols) +
theme(axis.title.x = element_blank(),
axis.text = element_text(color='black', size=10),
axis.title.y = element_text(color='black',size=12))
# To save
ggsave(pbox.group, filename = '../IMP/results/distance_boxplots_vs_med_permutation_group_v2.png', width = 3.5, height = 5, dpi = 300)
shapiro.test(dist_plot_groups.sub[dist_plot_groups.sub$real_perm=='original', 'distance'])
wilcox.test(dist_plot_groups.sub$distance ~ dist_plot_groups.sub$real_perm, paired=F)$p.value