-
Notifications
You must be signed in to change notification settings - Fork 2
/
generate-figures.R
executable file
·388 lines (312 loc) · 16.9 KB
/
generate-figures.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
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
#!/usr/bin/env Rscript
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(reshape))
suppressPackageStartupMessages(library(reshape2))
suppressPackageStartupMessages(library(RColorBrewer))
suppressPackageStartupMessages(library(vegan))
suppressPackageStartupMessages(library(gtools))
suppressPackageStartupMessages(library(grid))
suppressPackageStartupMessages(library(gridBase))
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(Biostrings))
PLOT_OLIGO_PER_SITE_STACKBAR <- function(df, oligo, sites = c('BM', 'HP', 'KG', 'PT', 'ST', 'SUBP', 'SUPP', 'SV', 'TD', 'TH'), title = 'no title', labels = NA, colors = NA){
df_sub_genus <- df[df$oligo %in% oligo, ]
df_sub_genus <- df_sub_genus[df_sub_genus$site %in% sites, ]
dfx <- data.frame(
site=character(),
oligo=character(),
abundance=numeric(),
stringsAsFactors=FALSE)
names(dfx) <- c('site', 'oligo', 'abundance')
N <- 0
for (site in sites){
site_reduced <- df_sub_genus[df_sub_genus$site == site, ]
for(oligo_in_site in oligo){
N <- N + 1
oligo_reduced <- site_reduced[site_reduced$oligo == oligo_in_site, ]
oligo_in_site_abundance <- sum(oligo_reduced$abundance)
dfx[N, ] <- c(site=site, oligo = oligo_in_site, abundance = oligo_in_site_abundance)
}
}
# replace oligos with labels in legend
if(!is.na(labels)){
dfx$oligo <- factor(dfx$oligo, levels = oligo, labels = labels)
}
dfx$abundance <- as.numeric(as.character(dfx$abundance))
dfx$oligo <- factor(dfx$oligo)
p <- ggplot(dfx, aes(x=factor(site), y=abundance, group= oligo, fill = oligo))
p <- p + geom_bar(position="fill", stat = "identity", width=0.90, colour = 'black')
p <- p + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), axis.ticks.y = element_blank(), legend.position = 'right', panel.grid.major = element_blank(), panel.grid.minor = element_blank())
p <- p + labs(title=title)
# color manually:
if(is.na(colors)){
colors <- sample(rainbow(length(levels(dfx$oligo)), s = 0.7, v = 0.9))
}
p <- p + scale_fill_manual(limits = levels(dfx$oligo), values = colors)
print(p)
return(colors)
}
PLOT_OLIGO_EVERYWHERE <- function(df, oligo, cluster_site='TH', sites = c('BM', 'HP', 'KG', 'PT', 'ST', 'SUBP', 'SUPP', 'SV', 'TD', 'TH'), metric='horn', method='average', title = 'no title', labels = NA, colors = NA){
# get genus
df_sub_genus <- df[df$oligo %in% oligo, ]
df_sub_genus <- df_sub_genus[df_sub_genus$site %in% sites, ]
#†get clustering based on SITE
df_sub_genus_sub_site <- df_sub_genus[df_sub_genus$site == cluster_site, ]
sub_matrix <- acast(df_sub_genus_sub_site, sample~oligo, value.var="abundance", fill = 0)
distance <- metric #"manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "morisita", "horn", "mountford", "raup" , "binomial" or "chao"
d <- vegdist(sub_matrix, method=distance)
fit <- hclust(d, method=method) # "ward", "single", "complete", "average", "mcquitty", "median" or "centroid"
samples_order <- fit$labels[fit$order]
# set the order of samples based on clustering results:
df_sub_genus$sample <- factor(df_sub_genus$sample, levels=samples_order)
df_sub_genus <- df_sub_genus[!df_sub_genus$sample %in% c(NA), ]
# this is very cool:
df_sub_genus$oligo <- reorder(df_sub_genus$oligo, df_sub_genus$abundance, FUN=sum)
df_sub_genus$oligo <- factor(df_sub_genus$oligo, levels=levels(df_sub_genus$oligo))
df_sub_genus <- df_sub_genus[order(df_sub_genus$oligo), ]
# replace oligos with labels in legend
if(!is.na(labels)){
df_sub_genus$oligo <- factor(df_sub_genus$oligo, levels = oligo, labels = labels)
}
df_sub_genus <- df_sub_genus[with(df_sub_genus, order(site, oligo)), ]
df_sub_genus$site <- factor(df_sub_genus$site)
p <- ggplot(df_sub_genus, aes(x=factor(sample), y=abundance, labels, color, fill = oligo))
p <- p + geom_bar(position="fill", stat = "identity", width=0.90)
p <- p + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), axis.ticks.y = element_blank(), legend.position = 'right', panel.grid.major = element_blank(), panel.grid.minor = element_blank())
p <- p + labs(title=title)
p <- p + labs(x='', y='')
p <- p + scale_y_continuous(breaks = NULL)
p <- p + facet_grid(site ~ .)
# color manually:
if(is.na(colors)){
colors <- sample(rainbow(length(levels(factor(df_sub_genus$oligo))), s = 0.5, v = 0.7))
}
p <- p + scale_fill_manual(limits = levels(factor(df_sub_genus$oligo)), values = colors)
print(p)
return(samples_order)
}
PLOT_OLIGO_PERCENT <- function(df, oligos, samples_order, site = 'KG', title = 'no title'){
# this function plots the percentage of all reads combined in 'oligos' list to all reads
# observed in the 'site'
dfx <- data.frame(sample=character(),
site=character(),
abundance_of_oligos=numeric(),
abundance_of_all=numeric(),
p_abundance_of_oligos=numeric(),
stringsAsFactors=FALSE)
names(dfx) <- c('sample', 'site', 'abundance_of_oligos', 'abundance_of_all', 'p_abundance_of_oligos')
N <- 0
dfx_sub_site <- df[df$site == site, ]
for(sample in samples_order){
dfx_sample_reduced <- dfx_sub_site[dfx_sub_site$sample == sample, ]
dfx_oligo_reduced <- dfx_sample_reduced[dfx_sample_reduced$oligo %in% oligos, ]
abundance_of_all <- sum(dfx_sample_reduced$abundance)
abundance_of_oligos <- sum(dfx_oligo_reduced$abundance)
p_abundance_of_oligos <- abundance_of_oligos * 100.0 / abundance_of_all
N <- N + 1
dfx[N, ] <- c(sample = sample, site=site, abundance_of_oligos = abundance_of_oligos, abundance_of_all = abundance_of_all, p_abundance_of_oligos = p_abundance_of_oligos)
}
dfx$p_abundance_of_oligos <- as.numeric(as.character(dfx$p_abundance_of_oligos))
dfx$sample <- ordered(dfx$sample,levels=samples_order)
#†prepare ggplot object
q <- ggplot(dfx, aes(x=factor(sample), y=p_abundance_of_oligos))
q <- q + stat_summary(fun.y = sum, geom = "bar")
q <- q + theme(axis.text.x = element_text(angle = 90, size=10, vjust=0, face = 'bold'), legend.position = 'none', axis.ticks.x = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())
q <- q + labs(title=title)
q <- q + labs(x='', y=paste("Percentage in ", site, sep=''))
q <- q + scale_y_sqrt()
print(q)
}
DIST_BETWEEN_TWO_SEQS <- function(s1, s2){
s1 <- gsub("-", "", s1)
s2 <- gsub("-", "", s2)
alignment <- pairwiseAlignment(s1, s2, gapOpening = -2, gapExtension = -8, scoreOnly = FALSE)
s1a <- as.character(pattern(alignment))
s2a <- as.character(subject(alignment))
mismatch_map <- strsplit(c(s1a, s2a), split= '')
num_mismatches <- length(which(mismatch_map[[1]] != mismatch_map[[2]]))
return(100 - (num_mismatches * 100 / nchar(s1a)))
}
OLIGO_DIST <- function(df, oligos, labels=c(), otu_limits=TRUE){
df_subset <- df[df$OLIGO %in% oligos, ]
dfx <- data.frame(OLIGO=character(),
REP_SEQ=character(),
stringsAsFactors=FALSE)
names(dfx) <- c('OLIGO', 'REP_SEQ')
N <- 0
for(i in seq(1, length(oligos))){
oligo <- oligos[i]
if(length(labels) > 0){
label <- labels[i]
} else {
label <- oligos[i]
}
N <- N + 1
dfx[N, ] <- c(OLIGO = label, REP_SEQ = as.character(df_subset[df_subset$OLIGO == oligo, ]$REP_SEQ))
}
df_subset <- dfx
if(length(labels) > 0){
df_subset$OLIGO <- ordered(df_subset$OLIGO,levels=labels)
} else {
df_subset$OLIGO <- ordered(df_subset$OLIGO,levels=oligos)
}
df_subset$OLIGO <- factor(df_subset$OLIGO)
num_oligos <- nrow(df_subset)
dist_mat <- matrix(nrow=num_oligos, ncol=num_oligos)
colnames(dist_mat) <- df_subset$OLIGO
rownames(dist_mat) <- df_subset$OLIGO
for(i in 1:num_oligos) {
for(j in 1:num_oligos){
o1 <- df_subset$OLIGO[i]
o2 <- df_subset$OLIGO[j]
dist_mat[i,j] <- DIST_BETWEEN_TWO_SEQS(df_subset[o1, ]$REP_SEQ, df_subset[o2, ]$REP_SEQ)
}
}
updated_df <- melt(dist_mat)
names(updated_df) <- c('OLIGO1', 'OLIGO2', 'DIST')
# find the best order
d <- vegdist(dist_mat, method="canberra")
fit <- hclust(d, method="complete") #, "single", "complete", "average", "mcquitty", "median" or "centroid"
oligos_order <- fit$labels[fit$order]
# set the order of samples based on clustering results:
updated_df$OLIGO1 <- factor(updated_df$OLIGO1, levels=rev(oligos_order))
updated_df$OLIGO2 <- factor(updated_df$OLIGO2, levels=oligos_order)
p <- ggplot(updated_df, aes(OLIGO1, OLIGO2, fill = DIST))
p <- p + geom_tile()
if(otu_limits){
p <- p + scale_fill_gradient2(low = "steelblue", high = "red", mid="white", midpoint=98.5, limits=c(97,100), na.value='#bbfdc6')
} else {
p <- p + scale_fill_gradient2(low = "steelblue", high = "red", mid="white")
}
p <- p + theme(axis.text.x = element_blank(), legend.position = 'right', axis.ticks.x = element_blank(), panel.grid.major = element_blank(), panel.grid.minor = element_blank())
print(p)
return(levels(updated_df$OLIGO1))
}
STANDARD_FIG <- function(df, df_seqs, oligos, sites, title, labels, colors, cluster_site = 'SUBP', metric='horn', method = 'ward'){
# wrapper for all the functions defined above.
labels_ordered <- OLIGO_DIST(df_seqs, oligos, labels)
ordering <- match(labels_ordered, labels)
labels <- labels[ordering]
oligos <- oligos[ordering]
colors <- colors[ordering]
PLOT_OLIGO_PER_SITE_STACKBAR(df, oligos, sites = sites, title = title, labels= labels, colors = colors)
samples_order <- PLOT_OLIGO_EVERYWHERE(df, oligos, cluster_site = cluster_site, sites = sites, method = method, title = title, labels = labels, colors = colors, metric = metric)
for(s in oral_sites){
PLOT_OLIGO_PERCENT(df, oligos, samples_order, site = s, title = paste(title, ' in ', s, sep=''))
}
}
########################################################################################################
# DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA
########################################################################################################
setwd("./v3v5/")
df_v3v5 <- as.data.frame(read.csv('environment.txt', header=TRUE, sep="\t"))
df_v3v5_seqs <- as.data.frame(read.csv('representative_sequences.txt', header=TRUE, sep="\t"))
setwd("../v1v3/")
df_v1v3 <- as.data.frame(read.csv('environment.txt', header=TRUE, sep="\t"))
df_v1v3_seqs <- as.data.frame(read.csv('representative_sequences.txt', header=TRUE, sep="\t"))
all_sites = c('BM', 'HP', 'KG', 'PT', 'SUBP', 'SUPP', 'SV', 'TD', 'TH', 'ST')
oral_sites = c('BM', 'HP', 'KG', 'PT', 'SUBP', 'SUPP', 'SV', 'TD', 'TH')
base_colors <- c('#FF0000', '#FFCC33', '#FFFF00', '#00FF00', '#009900', '#00FFFF', '#3399FF', '#0000CC', '#FF00FF', '#9933FF', '#009999', '#990099', '#999900', '#990000')
# where figures are going to be generated:
setwd("..")
########################################################################################################
# SAMPLE FIGURES
########################################################################################################
# MOST ABUNDANT NEISSERIA FIGURE V1V3
man_labels_v1v3 = c('Neisseria elongata 99.6%',
'Neisseria flavescens',
'Neisseria flavescens 98.8%',
'Neisseria flavescens 99.6%',
'Neisseria oralis',
'Neisseria polysaccharea / meningitidis)',
'Neisseria sicca / mucosa / flava / oralis',
'Neisseria subflava')
man_oligos_v1v3 = c('Betaproteobacteria_ATGCCGAGGCCGTTCAA',
'Betaproteobacteria_ATGCCCTGTTGACGCGA',
'Betaproteobacteria_ATGCCCTGCCGACGTGA',
'Betaproteobacteria_ATGCCCTGTCGACGCGA',
'Betaproteobacteria_ATGCCGCGTCATTCGGA',
'Betaproteobacteria_ATGCCCTGTTAGCGCGA',
'Betaproteobacteria_ATGCCGCGGCCCTTCGA',
'Betaproteobacteria_ATGCCATAGCCCTTCGA')
man_sites_v1v3 = c('KG', 'SUBP', 'PT')
man_colors_v1v3 <- c("#FF0000", "#FFCC33", "#FFFF00", "#00FF00", "#c2c2c2", "#00FFFF", "#009900", "#0000CC")
pdf('neisseria_v1v3.pdf', width=12, height=6)
STANDARD_FIG(df=df_v1v3, # use the data frame for v1v3 data
df_seq=df_v1v3_seqs, # this is where the representative sequences are
oligos= man_oligos_v1v3, # these are the oligotypes we are interested in
sites = oral_sites, # include these oral sites
title = 'most abundant Neisseria v1v3', # put this as a title
labels = man_labels_v1v3, # use these labels instead of raw oligotype names
colors = man_colors_v1v3, # colors.
cluster_site = 'TD', # cluster all patients based on this site
metric = 'horn', # distance metric for clustering
method = 'ward') # clustering algorithm
dev.off()
# STREP OLIGOS FIGURE V3V5
strep_oligos_v3v5 = c('Firmicutes_GGCTCATTACTTGGTTTCCCTTCTACGT',
'Firmicutes_GGCTCATTACTTGGTTTCTCTTCTACGT',
'Firmicutes_GGTTCGTTATTTGATTCCCCTTCTACGT',
'Firmicutes_GGTTCATTGTTTGACTCCCCTTCTACGT',
'Firmicutes_GGTTCATTGCTTGGCTTCCCTTCTACGT',
'Firmicutes_GGTTCGTTGCTTGGCTTCCCTTCTACGT',
'Firmicutes_GGTTCGTTGTTTGACTTCCCTTCTACGT',
'Firmicutes_GGTTCGTTGCTTGGCTTCGCTACTACGT')
strep_labels_v3v5 = c('S.peroris (728) / S.oralis (707) / S. mitis (677, 398) / S. sp. (431, 423, 071)',
'S. infantis (638) / S. sp. (486, 074, 061, 058)',
'S. salivarius (755) / S. vestibularis (021)',
'S. sinensis (767) / S. parasanguinis I (721) / S. parasanguinis II (411) / S. cristatus (578) / S. australis(073) / S. sp. (069, 067)',
'S. sanguinis (758) / S. agalactiae (537)',
'S. gordonii (622) / S. sp. (056)', 'S. intermedius (644) / S. constellatus (576)',
'S. mutans (686)')
strep_colors_v3v5 <- base_colors[1:length(strep_oligos_v3v5)]
pdf('strep_oligos_v3v5.pdf', width=12, height=6)
STANDARD_FIG(df=df_v3v5,
df_seq=df_v3v5_seqs,
oligos= strep_oligos_v3v5,
sites = oral_sites,
title = 'STREP SITE v3v5',
labels = strep_labels_v3v5,
colors = strep_colors_v3v5,
cluster_site = 'SUBP',
metric = 'horn',
method = 'ward')
dev.off()
# STREP OLIGOS FIGURE V1V3
strep_oligos_v1v3 = c('Firmicutes_-CTCAGTGTTGCGAGTGGTAGTTCACACTTCG-',
'Firmicutes_-CTCAATGTTGCGAGTGGTAGTTCACACTTCG-',
'Firmicutes_-CTCTGTCCTCCGAGTGGTAGTTCACACGTCG-',
'Firmicutes_-CTCAGTGTTGCGAGTGGTAGTTCACACATCG-',
'Firmicutes_-CTCAGTCCTGCGAGTGGTAGTTCACACATCG-',
'Firmicutes_-CTCAGTGCTGCGGGTGGTAGTTCACACTTCG-',
'Firmicutes_-CTCTGTATTGCGGGTGGTAGTTCACACTTCG-',
'Firmicutes_-CTCTGTGCTGCGAGTGGTAGTTCATACCACG-',
'Firmicutes_-GTTAGTATTCCGTGTGGTAGTTCACACGTCG-',
'Firmicutes_-CTCAGTCCTGCGAGTGGTAGTTCACGCTTCG-',
'Firmicutes_-CTCAATCCTGCGAGTGGTAGTTCACACATCG-',
'Firmicutes_-CTCTGTCCTGCGAGTGGTAGTTCACACTTCG-')
strep_labels_v1v3 = c('S. mitis I / mitis II / australis / pneumoniae',
'S. oralis / infantis / mitis bv II',
'S. salivarius / S. vestibularis',
'S. parasanguinis I',
'S. parasanguinis II',
'S. sanguinis',
'S. gordonii',
'S. intermedius',
'S. mutans',
'S. peroris',
'S. sp. (HOT-065)',
'S. sp. (HOT-056)')
strep_colors_v1v3 <- base_colors[1:length(strep_oligos_v1v3)]
pdf('strep_oligos_v1v3.pdf', width=12, height=6)
STANDARD_FIG(df=df_v1v3,
df_seq=df_v1v3_seqs,
oligos= strep_oligos_v1v3,
sites = oral_sites,
title = 'STREP SITE v1v3',
labels = strep_labels_v1v3,
colors = strep_colors_v1v3,
cluster_site = 'SUBP',
metric = 'horn',
method = 'ward')
dev.off()