-
Notifications
You must be signed in to change notification settings - Fork 0
/
MOSCAM20_NGS.R
634 lines (520 loc) · 25.6 KB
/
MOSCAM20_NGS.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
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
necessary_packages <- c('tidyverse', 'data.table', 'scales', 'knitr', 'grid', 'magrittr', 'DT', 'here', 'gridtext',
'ggpubr', 'ggthemes', 'reshape2', 'viridis', 'pals', 'circlize',
'vegan', 'phyloseq', 'metagenomeSeq', 'ComplexHeatmap', 'decontam', 'ggplot2','DT')
#install.packages(necessary_packages) #Install necessary and do this just once
lapply(necessary_packages, library, character.only = TRUE) #Load
#' heatmap function
plot_abundance <- function (obj, n, norm = TRUE, log = TRUE, fun = sd, rowclust = "bray", colclust = "bray", ...)
{
mat <- returnAppropriateObj(obj, norm, log)
otusToKeep <- which(rowSums(mat) > 0)
otuStats <- apply(mat[otusToKeep, ], 1, fun)
otuIndices <- otusToKeep[order(otuStats, decreasing = TRUE)[1:n]]
mat2 <- mat[otuIndices, ]
mat3 <- mat2[order(row.names(mat2)),]
mat4 <- as.data.frame(t(mat3))
Heatmap(mat3, clustering_distance_columns = vegdist(mat4, method = colclust), clustering_distance_rows = vegdist(mat3, method = rowclust), ...)
}
#' ***
#' # Eukaryotic virome analysis
#' ## Prepare the data
#' ### Load the OTU table, taxonomy file and metadata into R
OTU <- read.table ("moscam2020-abundance.tsv", header=TRUE, row.names=1, sep="\t", dec=".")
#' Information on OTU table
ncol(OTU) #Number of samples
nrow(OTU) #Number of contigs
summary(rowSums(OTU))
summary(colSums(OTU))
sum(rowSums(OTU)) ## Total number of reads (abundance)
sum(colSums(OTU)) ## Total number of reads (abundance)
tax <- read.table("MOS20tax_trimmed", header=T, row.names=1, sep="\t", dec=".")
#' Information on tax table
ncol(tax)
nrow(tax)
meta <- read.table("Metadata20.csv", header=TRUE, row.names = 1, sep=";", dec=".")
#Shorten sample names
colnames(OTU) = gsub("[EBYaeud]*20_KC_","",colnames(OTU),fixed = F)
names(OTU) <- gsub(x = names(OTU), pattern = "\\.", replacement = "-")
rownames(meta) = gsub("[EBYaeud]*20_KC_","",rownames(meta),fixed = F)
meta <- cbind(Sample=rownames(meta), meta)
#+ echo=FALSE
datatable(meta)
#' ### Make a phyloseq object
OTU.UF <- otu_table(as.matrix(OTU), taxa_are_rows=T)
tax.UF <- tax_table(as.matrix(tax))
meta.UF <- sample_data(meta)
MOS20 <- phyloseq(OTU.UF, tax.UF, meta.UF)
MOS20
#' #### Subset only viruses and remove prokaryotic viruses and EVEs
EVE_phage <- c("Atrato Retro-like virus", "Gurupi chuvirus-like 1", "Aedes aegypti To virus 1",
"Aedes aegypti To virus 2", "Guato virus", "Kaiowa virus", "Atrato Chu-like virus 1",
"Chuvirus Mos8Chu0", "Chibugado virus", "Prokaryotic dsDNA virus sp.",
"Aedes aegypti virga-like virus", "Verdadero virus", "Aedes rhabdo-like virus",
"Kvarnon virus", "Aedes binegev-like virus 2", "unclassified 9", "unclassified 10")
MOS20.V1 <- subset_taxa(MOS20, !is.element(Species, EVE_phage))
MOS20.V1 <- subset_taxa(MOS20.V1, Order!="Caudovirales")
MOS20.V1 <- subset_taxa(MOS20.V1, Phylum!="Phage")
MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Verdadero virus ")
MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Aedes rhabdo-like virus ")
MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Shilevirus leptomonadis")
MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Usinis virus")
MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Drosophila-associated totivirus 3")
MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Grizzly bear anellovirus 10")
# # #MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Anelloviridae sp.")
# MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Hubei mosquito virus 4")
MOS20.V1 <- subset_taxa(MOS20.V1, Species!="Formosus virus")
#' #### Subset only viruses and remove prokaryotic viruses and EVEs
MOS20.V2 <- subset_taxa(MOS20.V1, Kingdom=="Viruses")
MOS20.V2
#' **Info on sample variables and level of taxonomic ranks:**
sample_variables(MOS20.V2)
rank_names(MOS20.V2)
#' #### Agglomerate taxa on Viral_species level
MOS20_species <- MOS20.V2 %>%
tax_glom(taxrank = "Species")
MOS20_species
#' **Only keep taxa with more than 0 reads:**
MOS20_species <- prune_taxa(taxa_sums(MOS20_species) > 0, MOS20_species)
MOS20_species
#' **Only keep samples with more than 0 viral reads:**
MOS20_final <- prune_samples(sample_sums(MOS20_species) > 0, MOS20_species)
MOS20_final
#Sum info
rowSums(otu_table(MOS20_final))
colSums(otu_table(MOS20_final))
sum(otu_table(MOS20))
#' **Convert phyloseq object to metagenomeseq object to make heatmap:**
MOS20_metaseq <- phyloseq_to_metagenomeSeq(MOS20_final)
#' **Aggregate by species:**
MOS20_metaseq_species <- aggregateByTaxonomy(MOS20_metaseq, lvl = "Species", norm = F,
aggfun = colSums, out = "MRexperiment", alternate = T)
#' **Count number of unique Viral_species:**
n_species <- length(unique(featureData(MOS20_metaseq_species)$Species))
n_species
#Count how many mosquito species there are and assign colors
unique(sample_data(MOS20_final)$Species)
length(unique(meta$Species))
#myColors <- c(viridis::plasma(7,0.8, begin=0, end = 1, direction = 1))
myColors <- c("#338068", "#814C8A")
#myColors <- c("#814C8A", "#EBC71B", "#E181AC", "#009E73", "#824CDE", "#EF3924", "#338068", "#4A67B1", "#5D1F0B" )
names(myColors) <- levels(as.factor(meta$Species))
myColors
pal.bands(myColors, main="Mosquito species colors")
#Assign colors to location
length(unique(meta$Location))
locColors <- c(viridis::viridis(8,1, begin=0, end = 1, direction = 1))
locColors <- c("#F68B68", "#78ADBE", "#CDABD1","#5CBA47")
names(locColors) <- levels(as.factor(meta$Location))
locColors
pal.bands(locColors, main="location colors")
#' **Set heatmap colors:**
heatmapCols <- colorRampPalette(brewer.pal(9, "YlOrRd"))(20)
#' **Assign mosquito species for each sample:**
mosquito_species <- pData(MOS20_metaseq_species)$Species
#' **Assign colors to location:**
location <- pData(MOS20_metaseq_species)$Location
#' **Calculate average BLASTx values:**
blastx <- read.table("mean_blastx.tsv", header=T, row.names=1, dec=".", sep="\t")
blastx.UF <- otu_table(as.matrix(blastx), taxa_are_rows=T)
blastx.ps <- phyloseq(blastx.UF, tax_table(prune_taxa(taxa_sums(MOS20_final) >= 1, MOS20_final)))
blastx_metaseq <- phyloseq_to_metagenomeSeq(blastx.ps)
blastx_metaseq
#Aggregate by species
blastx_mean = aggregateByTaxonomy(blastx_metaseq, lvl = "Species", norm = F, aggfun = mean, out = "MRexperiment", alternate = T)
blastx_mean
#' **Store average BLASTx identities in dataframe:**
rowanno <- as.data.frame(returnAppropriateObj(blastx_mean, log=F, norm=F))
colnames(rowanno)[1] <- "blastx"
#' **Create color function for BLASTx values:**
col_fun=colorRamp2(c(0,100), c("white","deepskyblue4"))
#+ echo=FALSE, fig.width=7, fig.height=4
pal.bands(col_fun(0:100), main="Blastx % identity colors")
#' ## Eukaryotic virome heatmap
left_ra=rowAnnotation("Blastx"= rowanno$blastx,
col=list("Blastx"=col_fun),
show_annotation_name=T,
annotation_name_side = "top",
annotation_name_gp = gpar(fontsize = 8, fontface = "bold"),
annotation_name_rot = 270,
annotation_legend_param=list("Blastx" = list(title ="Blastx % Identity",
direction="horizontal",
at=c(0,25,50,75,100))))
column_ha = HeatmapAnnotation(Location=location,
'Mosquito species' = mosquito_species,
show_annotation_name = T,
annotation_label = gt_render(c("Location", "Species")),
annotation_name_side = "right",
annotation_name_gp = gpar(fontsize = 8, fontface = "bold"),
annotation_name_rot = 0,
annotation_legend_param=list('Mosquito species'= list(title ='Mosquito species',
labels_gp = gpar(fontface="italic")),
'Location' = list(title='Location')),
col = list('Mosquito species'=c(myColors), Location=c(locColors)))
#n depends on the amount of taxa in 'MOS20_metaseq_species'
hm <- plot_abundance(MOS20_metaseq_species, n = n_species, log = T, norm = F, colclust = "bray",
col = heatmapCols,
name = "Log2 Read Counts",
top_annotation=column_ha,
left_annotation=left_ra,
row_names_gp = gpar(fontsize = 7),
show_column_names = T,
column_names_rot = 45,
column_names_gp=gpar(fontface=1, fontsize=7),
heatmap_legend_param = list(direction = "horizontal"),
cluster_rows = T,
row_title_gp = gpar(fontsize=7),
row_title_rot=0,
border=F)
#+ echo=TRUE, fig.width=15, fig.height=9
draw(hm, heatmap_legend_side = "left", annotation_legend_side = "left",
merge_legend = T, legend_grouping="original")
#'*Alpha diversity*
#'*Alpha diversity*
#'*Alpha diversity*
#Wilcox
#Based on mosquito species
#Based on mosquito species
mosColors <- c("#338068", "#814C8A")
names(mosColors) <- levels(as.factor(meta$Species))
mosColors
alpha_mos <- plot_richness(MOS20_final, measures=c("Observed", "Simpson"), x="Species", color = "Species")+
geom_boxplot()+
geom_jitter(width=0.05, size=3)+
stat_compare_means(aes(label= ..p.signif..), show.legend = F, size = 10) + # Set size of significance asterisks
theme_bw() +
scale_fill_manual(values = mosColors) + # Assigning colors manually
scale_color_manual(values = mosColors) + # Assigning colors manually
scale_color_manual(values = myColors, name = "Mosquito Species") +
theme(axis.text.y=element_text(angle=90,hjust=0.5, size = 22))+
theme(axis.title.y = element_text(size = 22, face = "bold")) + # Increase size of y-axis title
theme(strip.text.x = element_text(size = 22),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.text.align = 0,
legend.text = element_text(size = 22, face = "italic"),
legend.title = element_text(face = "bold",size = 22), # Change legend title
plot.title = element_text(size = 30, face = "bold")) + # Increase title size and bold
labs(title = "a")
alpha_mos
#Wilcox
#Based on location species
#Based on location species
locColors <- c("#F68B68", "#78ADBE", "#CDABD1","#5CBA47")
names(locColors) <- levels(as.factor(meta$Location))
locColors
alpha_loc <- plot_richness(MOS20_final, measures = c("Observed", "Simpson"), x = "Location", color = "Location") +
geom_boxplot() +
geom_jitter(width = 0.05, size = 3) +
theme_bw() +
scale_fill_manual(values = locColors) +
scale_color_manual(values = locColors) +
theme(axis.text.y = element_text(angle = 90, hjust = 0.5, size = 22)) +
theme(axis.title.y = element_text(size = 22, face = "bold")) +
theme(strip.text.x = element_text(size = 22),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.text.align = 0,
legend.text = element_text(size = 22),
legend.title = element_text(size = 22, face = "bold"),
plot.title = element_text(size = 30, face = "bold")) +
labs(title = element_text("b", face = "bold")) +
scale_y_continuous(limits = c(0, NA)) +
stat_compare_means(method = "wilcox.test", aes_string(x = "Climatic_zone", y = "value", group = "Climatic_zone"),
label = "p.signif", hide.ns = TRUE, geom = "text", size = 10, show.legend = FALSE, tip.length = 0.0,
comparisons = list(c(1, 2), c(1, 3)))
alpha_loc
library(gridExtra)
library(cowplot)
Alpha <- grid.arrange(alpha_mos, alpha_loc, nrow = 1)
Alpha
## 2. PCoA
PCA <- ordinate(MOS20_final, "PCoA")
p <- plot_ordination(MOS20_final, PCA, type = "samples", color = "Species", shape = "Location") +
geom_point(size = 3, aes(group = "Species")) +
geom_text(aes(label = Sample), vjust = 1.5) +
theme_bw()+
scale_fill_manual(values = myColors) + # Assigning colors manually
scale_color_manual(values = myColors, name = "Mosquito Species",
labels = expression(italic("Ae. africanus"), italic("Ae. albopictus"))) + # Assigning custom italicized labels for legend
theme(legend.text.align = 0,
legend.key.size = unit(1.5, "lines"),
legend.title = element_text(face = "bold", size = 16),
legend.text = element_text(size = 14),
axis.text = element_text(size = 14), # Increase size of axis text
axis.title = element_text(size = 16, face = "bold")) + # Increase size of axis title
ggtitle("") #Add title to plot b
p <- p + stat_ellipse(type = "norm", linetype = 3, aes_string(group = "Species"), show.legend = FALSE) # Adds circles
p
# Load taxonomy info from clean phyloseq object (MOS20.V2)
MOS20_NGS_mastertable <- tax_table(MOS20.V2)
MOS20_NGS_mastertable <- as.data.frame(MOS20_NGS_mastertable)
# Extract row names, "Family," and "Species" columns into a new table
Master_table <- data.frame(
Contigs = rownames(MOS20_NGS_mastertable),
Family = MOS20_NGS_mastertable$Family,
Species = MOS20_NGS_mastertable$Species
)
# Extract the sample names from the Contig column
Master_table$Sample_name <- sub(".*_P([^_]+)$", "P\\1", Master_table$Contigs)
#sample_names <- sapply(strsplit(Master_table$Contig, "_"), function(x) paste(x[7:length(x)], collapse = "_"))
# Extract all the length from the contig names
Contig_lengths <- str_match(Master_table$Contigs, "length_(\\d+)_")[, 2]
Master_table$Contig_length <- as.numeric(Contig_lengths)
# Calculate the total abundance per sample
Total_abundance <- rowSums(otu_table(MOS20.V2))
# Add the total abundance as a new column in the Master_table
Master_table$Total_abundance <- Total_abundance
#Extract coverage from contig name
library(stringr)
cov <- str_match(Master_table$Contigs, "NODE_\\d+_length_\\d+_cov_([^.]+)")[, 2]
Master_table$Coverage <- cov
# Assuming that the column in the `sample_data` containing mosquito species is named "Species"
sample_data <- sample_data(MOS20.V2)
Mosquito_species <- sample_data$Species
Mosquito_species <- sample_data(MOS20.V2)$Species
# Match the sample names and merge the species information into the Master_table
Master_table$Mosquito_species <- Mosquito_species[match(Master_table$Sample_name, rownames(sample_data))]
# Match the sample names and merge the Location information into the Master_table
Location <- sample_data(MOS20.V2)$Location
Master_table$Location <- Location[match(Master_table$Sample_name, rownames(sample_data))]
# Calculate the total abundance in percentage
Relative_abundance <- (Master_table$Total_abundance / sum(Master_table$Total_abundance)) * 100
# Add the total abundance percentage as a new column in the Master_table
Master_table$Relative_abundance <- Relative_abundance
#'*MOVE TO RELATIVE ABUNDANCE SCRIPT*
#'*MOVE TO RELATIVE ABUNDANCE SCRIPT*
#'*1. Treemap*
#'*1. Treemap*
#'
# Aggregate the Relative_Abundance values by Family
Aggregated_Family <- Master_table %>%
group_by(Family) %>%
summarise(Relative_abundance = sum(Relative_abundance))
# Sort the aggregated data by Relative_Abundance in descending order
Aggregated_Family <- Aggregated_Family %>%
arrange(desc(Relative_abundance))
Aggregated_Family
#Assign colors to
display.brewer.all()
display.brewer.all(colorblindFriendly = TRUE) #Color blind friendly
display.brewer.pal(n = 11, name = 'Paired') # Change 'n' and 'name' depending on the color palette
# Hexadecimal color specification
brewer.pal(n = 11, name = "Paired")
# count the number of unique families
n_families <- Master_table %>%
distinct(Family) %>%
nrow()
n_families
n <- 17 #Number of families
mypalette<- c("#C43B80", "#1B9E77", "#7570B3","#E31A1C", "#824CDE", "#A6761D", "#666666", "#82906F",
"#FFFF99","#A6CEE3","#E05fAD","#74EA56","#DBA875","#E6DC50","#27408B", "#FF5733", "#5FBBEA")
names(mypalette) <- levels(as.factor(Master_table$Family))
mypalette
pal.bands(mypalette, main="Virus Family colors")
#Create a treemap
### Make treemap (TM)
library(treemapify)
library(randomcoloR)
library(ggplot2)
library(treemap)
library(forcats)
TM <- ggplot(data = Aggregated_Family, aes(area = Relative_abundance, fill = Family, label = paste0(Family, "\n"))) +
geom_treemap(color = "black") +
geom_treemap_text(fontface = "italic", colour = "black", place = "centre", size = 14) +
labs(title = element_text("a", face = "bold")) +
theme(legend.position = "right")+
scale_fill_manual(values = mypalette) + # Custom color palette
theme(plot.caption = element_text(hjust = 0)) +
guides(fill = guide_legend(
label.position = "right",
title = "Virus families", # Change the legend title here
title.position = "top",
title.hjust = 0.5,
label.hjust = 0,
label.vjust = 0.5,
title.theme = element_text(face = "bold", size = 14),
label.theme = element_text(face = "italic", size = 12)))
TM
### Make treenap for only unclassified Family
###
# Filter data to include only "Unclassified" rows
Unclassified_fam <- Master_table %>%
filter(Family == "unclassified")
# Recalculate relative abundance of filtered data (unclassified) in percentage and add to new column
Relative_abundance_unclass <- (Unclassified_fam$Relative_abundance / sum(Unclassified_fam$Relative_abundance)) * 100
# Add the total abundance percentage as a new column in the Unclassified_fam data frame
Unclassified_fam$Relative_abundance_unclass <- Relative_abundance_unclass
# Aggregate Viral_species values
Aggregated_Unclassified_fam <- Unclassified_fam %>%
group_by(Species) %>%
summarise(Relative_abundance_unclass = sum(Relative_abundance_unclass))
# # Reverse the order of the 'Viral_species' factor levels
# Aggregated_Unclassified_fam$Viral_species <- fct_reorder(Aggregated_Unclassified_fam$Viral_species, Aggregated_Unclassified_fam$Relative_abundance_unclass)
# Aggregated_Unclassified_fam$Species <- fct_reorder(Aggregated_Unclassified_fam$Species, -Aggregated_Unclassified_fam$Relative_abundance_unclass)
# Sort the aggregated data by Relative_Abundance in descending order
Aggregated_Unclassified_fam<- Aggregated_Unclassified_fam %>%
arrange(desc(Relative_abundance_unclass))
Aggregated_Unclassified_fam
n_families_unclass <- Aggregated_Unclassified_fam %>%
distinct(Species) %>%
nrow()
n_families_unclass
n_fam <- 11
palette <- distinctColorPalette(n_fam)
# To choose your own colors
palette <- c("#D86ED9", "#E04E8E", "#75E3A0", "#88954D", "#DDACA5", "#715CD1", "#D96255","#D4E691", "#AD39E5", "#80E7E1", "#976790", "#E6A764", "#DAA7DF", "#D0E1BD","#69AE9D", "#828ADD", "#DBE450", "#D3D9E2", "#7CB2D9", "#76E760")
names(palette) <- levels(as.factor(Master_table$Family$unclassified))
palette
pal.bands(mypalette, main="Virus Family colors")
TMU <- ggplot(data = Aggregated_Unclassified_fam, aes(area = Relative_abundance_unclass, fill = Species, label = paste0(Species, "\n"))) +
geom_treemap(colour = "black") +
geom_treemap_text(colour = "black", place = "centre") +
labs(title = "b") +
theme(legend.position = "right")+
scale_fill_manual(values = palette) + # Custom color palette
theme(plot.caption = element_text(hjust = 0)) +
guides(fill = guide_legend(
label.position = "right",
title = "unclassified viruses", # Change the legend title here
title.position = "top",
title.hjust = 0.5,
label.hjust = 0,
label.vjust = 0.5,
title.theme = element_text(size = 14, face = "bold"),
label.theme = element_text(size = 12)))
TMU
library(gridExtra)
library(cowplot)
# Assuming TM and TMU are already defined ggplot objects
Treemap <- grid.arrange(TM, TMU, ncol = 1)
Treemap
combined_Treemap <- ggdraw() +
draw_plot(Treemap, width = 1, height = 1, x = 0, y = 0) +
draw_label("Combined Plot Title", x = 0.5, y = 1,
size = 16, vjust = 1, hjust = 0.5, fontface = "bold")
combined_Treemap
#'*2. Piechart*
#'*2. Piechart*
#'
# Calculate the total relative abundance for each mosquito species
Reads_mos <- aggregate(Relative_abundance ~ Mosquito_species, data = Master_table, sum)
Reads_mos
Pie_Mos <- ggplot(Reads_mos, aes(x = "", y = Relative_abundance, fill = Mosquito_species, label = Mosquito_species)) +
geom_bar(colour = "black", stat = "identity", width = 1) +
geom_text(aes(label = Mosquito_species), position = position_stack(vjust = 0.7), fontface = "italic") + # Add labels
coord_polar("y", start = 0) +
labs(title = "a") +
theme_void() +
scale_fill_manual(values = mosColors) + # Assigning colors manually
scale_color_manual(values = mosColors) + # Assigning colors manually
#scale_fill_manual(values = palette)
theme(plot.caption = element_text(hjust = 0)) +
guides(fill = guide_legend(
label.position = "right",
title = "Virus families", # Change the legend title here
title.position = "top",
title.hjust = 0.5,
label.hjust = 0,
label.vjust = 0.5,
title.theme = element_text(face = "bold", size = 14),
label.theme = element_text(face = "italic", size = 12)))
Pie_Mos
Pie_Mos <- ggplot(Reads_mos, aes(x = "", y = Relative_abundance, fill = Mosquito_species, label = paste0(Mosquito_species, "\n (", round(Relative_abundance, 2), "%)"))) +
geom_bar(colour = "black", stat = "identity", width = 1) +
geom_text(size = 4, position = position_stack(vjust = 0.65), fontface = "italic") + # Add labels and adjust position
coord_polar("y", start = 0) +
labs(title = "a") +
theme_void() +
scale_fill_manual(values = mosColors) + # Assigning colors manually
scale_color_manual(values = mosColors) + # Assigning colors manually
theme(plot.caption = element_text(hjust = 0)) +
guides(fill = guide_legend(
label.position = "right",
title = "Virus families", # Change the legend title here
title.position = "top",
title.hjust = 0.5,
label.hjust = 0,
label.vjust = 0.5,
title.theme = element_text(face = "bold", size = 14),
label.theme = element_text(face = "italic", size = 12)))
Pie_Mos
#'*Details on Pie*
#'*Details on Pie*
# Filter data for Ae. africanus
# Filter data for Ae. africanus
africanus_data <- subset(Master_table, Mosquito_species == "Ae. africanus")
# Get unique families and their abundances
unique_families_afri <- unique(africanus_data$Family)
abundances_afri <- sapply(unique_families_afri, function(x) sum(africanus_data$Relative_abundance[africanus_data$Family == x]))
abundances_afri
#Number of virus spp. in Ae. africanus
num_species_africanus <- n_distinct(filter(Master_table, Mosquito_species == "Ae. africanus")$Species)
num_species_africanus
# Filter data for Ae. albopictus
# Filter data for Ae. albopictus
albopictus_data <- subset(Master_table, Mosquito_species == "Ae. albopictus ")
# Get unique families and their abundances
unique_families_albo <- unique(albopictus_data$Family)
abundances_albo <- sapply(unique_families_albo, function(x) sum(albopictus_data$Relative_abundance[albopictus_data$Family == x]))
abundances_albo
#Number of virus spp. in Ae. albopictus
num_species_albopictus <- n_distinct(filter(Master_table, Mosquito_species == "Ae. albopictus ")$Species)
num_species_albopictus
#Number of virus spp. in Ae. albopictus and Ae. africanus
# Extract the unique species for Ae. albopictus and Ae. africanus
species_albopictus <- unique(filter(Master_table, Mosquito_species == "Ae. albopictus ")$Species)
species_africanus <- unique(filter(Master_table, Mosquito_species == "Ae. africanus")$Species)
# Find the common species between the two
Aedes <- intersect(species_albopictus, species_africanus)
Aedes
# Count the number of species found in both
num_species_both_Aedes <- length(Aedes)
num_species_both_Aedes
# Calculate the total relative abundance for each mosquito species
Reads_Loc <- aggregate(Relative_abundance ~ Location, data = Master_table, sum)
Reads_Loc
Pie_Loc <- ggplot(Reads_Loc, aes(x = "", y = Relative_abundance, fill = Location, label = paste0(Location, "\n (", round(Relative_abundance, 0), "%)"))) +
geom_bar(colour = "black", stat = "identity", width = 1) +
geom_text(size = 2.8, colour = "black",position = position_stack(vjust = 0.55)) + # Add labels
coord_polar("y", start = 0) +
labs(title = "b") +
theme_void() +
scale_fill_manual(values = locColors) + # Assigning colors manually
scale_color_manual(values = locColors) + # Assigning colors manually
theme(plot.caption = element_text(hjust = 0)) +
guides(fill = guide_legend(
label.position = "right",
title = "unclassified viruses", # Change the legend title here
title.position = "top",
title.hjust = 0.5,
label.hjust = 0,
label.vjust = 0.5,
title.theme = element_text(size = 14, face = "bold"),
label.theme = element_text(size = 12)))
Pie_Loc
# Assuming TM and TMU are already defined ggplot objects
Pie<- grid.arrange(Pie_Mos, Pie_Loc, nrow = 1)
Pie
combined_Pie <- ggdraw() +
draw_plot(Pie, width = 1, height = 1, x = 0, y = 0) +
draw_label("Combined Plot Title", x = 0.5, y = 1,
size = 16, vjust = 1, hjust = 0.5, fontface = "bold")
combined_Pie
#'*Details on Pie*
#'*Details on Pie*
# Filter data for Ae. africanus
# Filter data for Ae. africanus
buea_data <- subset(Master_table, Location == "Buea")
# Get unique families and their abundances
unique_families_buea <- unique(africanus_data$Family)
abundances_buea <- sapply(unique_families_buea , function(x) sum(buea_data$Relative_abundance[buea_data$Family == x]))
abundances_buea
# Filter data for Ae. albopictus
# Filter data for Ae. albopictus
yaounde_data <- subset(Master_table, Location == "Yaounde ")
# Get unique families and their abundances
unique_families_yaounde <- unique(yaounde_data$Family)
abundances_yaounde <- sapply(unique_families_yaounde, function(x) sum(yaounde_data$Relative_abundance[yaounde_data$Family == x]))
abundances_yaounde