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script11_beta-diver.R
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script11_beta-diver.R
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# ANALYZING BETA DIVERSITY #
# ------------------------ #
#--- Load required objects ----
pqs <- readRDS(file = "./rds-files/pqs.rds")
pqs.rel <- readRDS(file = "./rds-files/pqs_rel.rds")
pqs.hell <- readRDS(file = "./rds-files/pqs_hell.rds")
metadata <- readRDS(file = "./rds-files/metadata.rds")
#--- Define colors for graphics ----
colors.hab <- as.matrix(jcolors("rainbow"))[c(6, 9)]
colors.hab <- rev(brewer.pal(n = 11, name = "RdBu"))[c(3,9)]
color.latzone <- c(Polar = "#2166ac", Temperate = "#DD8B71", Tropical = "#b2182b")
colors.1 <- c("darkorchid4", "deepskyblue2", "coral3", "tomato4",
"turquoise", "darkgoldenrod3", "lightpink", "#2166ac", "seagreen4",
"limegreen", "orchid1", "olivedrab4", "red1", "skyblue3", "maroon4",
"darkorange", "gold2", "khaki", "plum4", "maroon2", "lightgreen",
"aquamarine", "grey0", "mediumpurple", "sienna4", "tan", "darkcyan",
"yellow", "thistle", "gray81")#darkblue
### Ordination of community composition from Bray-Curtis dissimilarities ----
#--- Agglomerate to the different taxa levels:
pqs.genus <- tax_glom(pqs, taxrank = "Genus", NArm=FALSE,
bad_empty = c(NA, "", " ", "\t"))
pqs.family <- tax_glom(pqs, taxrank = "Family", NArm=FALSE,
bad_empty = c(NA, "", " ", "\t"))
pqs.order <- tax_glom(pqs, taxrank = "Order", NArm=FALSE,
bad_empty = c(NA, "", " ", "\t"))
pqs.class <- tax_glom(pqs, taxrank = "Class", NArm=FALSE,
bad_empty = c(NA, "", " ", "\t"))
pqs.phylum <- tax_glom(pqs, taxrank = "Phylum", NArm=FALSE,
bad_empty = c(NA, "", " ", "\t"))
#--- Transform data by Hellinger transformation:
pqs.genus.rel <- transform_sample_counts(pqs.genus, function(x) x/sum(x))
pqs.genus.hell <- transform_sample_counts(pqs.genus.rel, function(x) sqrt(x))
pqs.family.rel <- transform_sample_counts(pqs.family, function(x) x/sum(x))
pqs.family.hell <- transform_sample_counts(pqs.family.rel, function(x) sqrt(x))
pqs.order.rel <- transform_sample_counts(pqs.order, function(x) x/sum(x))
pqs.order.hell <- transform_sample_counts(pqs.order.rel, function(x) sqrt(x))
pqs.class.rel <- transform_sample_counts(pqs.class, function(x) x/sum(x))
pqs.class.hell <- transform_sample_counts(pqs.class.rel, function(x) sqrt(x))
pqs.phylum.rel <- transform_sample_counts(pqs.phylum, function(x) x/sum(x))
pqs.phylum.hell <- transform_sample_counts(pqs.phylum.rel, function(x) sqrt(x))
#--- Set a seed because the starting position in NMDS algorithm is random:
set.seed(1000)
#--- Calculate ordination from dissimilarities:
bray.ord <- ordinate(pqs.hell, method = "NMDS", try = 100, trymax = 1000,
k = 2, distance = "bray", pc = TRUE, maxit = 1000) #autotransform = FALSE)
bray.genus.ord <- ordinate(pqs.genus.hell, method = "NMDS", try = 100, trymax = 1000,
k = 3, distance = "bray", pc = TRUE, maxit = 1000)
bray.family.ord <- ordinate(pqs.family.hell, method = "NMDS", try = 100, trymax = 1000,
k = 3, distance = "bray", pc = TRUE, maxit = 1000)
bray.order.ord <- ordinate(pqs.order.hell, method = "NMDS", try = 100, trymax = 1000,
k = 3, distance = "bray", pc = TRUE, maxit = 1000)
bray.class.ord <- ordinate(pqs.class.hell, method = "NMDS", try = 100, trymax = 1000,
k = 3, distance = "bray", pc = TRUE, maxit = 1000)
bray.phylum.ord <- ordinate(pqs.phylum.hell, method = "NMDS", try = 100, trymax = 1000,
k = 3, distance = "bray", pc = TRUE, maxit = 1000)
### Plotting community composition from Bray-Curtis dissimilarities ----
#--- Plot NMDS comparing environments and habitats at ASV level:
bray.plot.envs <- plot_ordination(pqs.hell, bray.ord, type = "samples", axes = 1:2,
color = "Environment", shape = "Habitat",
title = NULL, justDF = FALSE, label = NULL) +
geom_point(size = 3) +
scale_color_manual(values = color.latzone,
labels = c("Antarctic", "Temperate", "Tropical")) +
labs(shape = "Habitat", color = "Environment", title = NULL) +
# caption = paste0("PERMANOVA p=0.001, R2=0.1105 |",
# " ANOSIM p=", round(anosim.bray$signif, 4), ", R=", round(anosim.bray$statistic, 4))) +
theme_bw() +
#scale_shape_manual(values = c(17, 16)) +
annotation_custom(textGrob(label = paste0("stress = ", round(bray.ord$stress, 3)), x = 0.7, y = 0.95, hjust = 0)) +
theme(legend.position = "bottom", legend.title = element_text(size = 16, face = "bold"),
aspect.ratio = 1,
text = element_text(size = 15),
plot.title = element_text(hjust = 0.5, size = 16),
legend.text = element_text(size = 16),
plot.caption = element_text(size = 16, face = "bold"),
legend.box = "vertical") +
guides(color = guide_legend(override.aes = list(size = 5)),#text = element_text(size = 20)
shape = guide_legend(override.aes = list(size = 5))) +
stat_ellipse(level = 0.8, linetype = 1, aes(group = Environment), show.legend = F) # type = "norm"
#--- Plot NMDS comparing ecoregions at ASV level:
bray.plot.eco <- plot_ordination(pqs.hell, bray.ord, type = "samples", axes = 1:2,
color = "Marine_Ecoregion", shape = "Habitat",
title = NULL, justDF = FALSE, label = NULL) +
geom_point(size = 4) +
scale_color_manual(values = colors.1) +
#labs(color = "Habitat") + # no me funciona
labs(shape = "Habitat", color = "Marine_Ecoregion") +
# caption = paste0("PERMANOVA p=0.001, R2=0.0949 |",
# " ANOSIM p=", round(anosim.bray$signif, 4), ", R=", round(anosim.bray.prov$statistic, 4))) +
theme_bw() +
scale_shape_discrete(labels = c("Antarctic sponge", "Non-Antarctic sponge")) +
# values = c(17, 16)) +
annotation_custom(textGrob(label = paste0("stress = ", round(bray.ord$stress, 3)), x = 0.7, y = 0.95, hjust = 0)) +
theme(legend.position = "right", legend.title = element_text(size = 15, face = "bold"),
aspect.ratio = 1,
text = element_text(size = 15),
plot.title = element_text(hjust = 0.5, size = 16),
legend.text = element_text(size = 16),
plot.caption = element_text(size = 16, face = "bold")) +
guides(color = guide_legend(override.aes = list(size = 5)),#text = element_text(size = 20)
shape = guide_legend(override.aes = list(size = 5)))
#stat_ellipse(level = 0.95, linetype = 1, aes(group = Habitat), show.legend = F) # type = "norm"
#--- Save HQ image (also can use ggsave() or png() ):
png("./results/graphics/beta-envs-bray.png", units = "in", width = 7, height = 7, res = 300)
bray.plot.envs
dev.off()
png("./results/graphics/beta-eco-bray.png", units = "in", width = 12, height = 7.5, res = 300)
bray.plot.eco
dev.off()
#--- Plot NMDS comparing envs and habitats for distinct taxa levels (NOTE: CHANGE TAX RANKS):
bray.taxa.plot <- plot_ordination(pqs.phylum.hell, bray.phylum.ord, type = "samples", axes = 1:2,
color = "Environment", shape = "Habitat",
title = NULL, justDF = FALSE, label = NULL) +
geom_point(size = 2) +
scale_color_manual(values = color.latzone,
labels = c("Antarctic", "Temperate", "Tropical")) +
#labs(color = "Habitat") + # no me funciona
labs(shape = "Habitat", color = "Environment", title = NULL) +
# caption = paste0("ANOSIM p=", round(anosim.phylum.bray$signif, 4), ", R=",
# round(anosim.phylum.bray$statistic, 4))) +
theme_bw() +
#scale_shape_manual(values = c(17, 16)) +
annotation_custom(textGrob(label = paste0("stress = ", round(bray.phylum.ord$stress, 3)), x = 0.7, y = 0.95, hjust = 0)) +
theme(legend.position = "right", legend.title = element_text(size = 18, face = "bold"),
aspect.ratio = 1,
text = element_text(size = 15),
plot.title = element_text(hjust = 0.5, size = 16),
legend.text = element_text(size = 16),
plot.caption = element_text(size = 16, face = "bold")) +
guides(color = guide_legend(override.aes = list(size = 5)),#text = element_text(size = 20)
shape = guide_legend(override.aes = list(size = 5)))
#--- Save HQ image (NOTE: CHANGE TAX RANK):
png("./results/graphics/beta-phylum-bray.png", units = "in", width = 8.5, height = 5, res = 300)
bray.taxa.plot
dev.off()
#--- Plot NMDS comparing sequencing runs at ASV level:
bray.plot.sr <- plot_ordination(pqs.hell, bray.ord, type = "samples", axes = 1:4,
color = "Sequencing_run_ID", label = NULL,
title = NULL, justDF = FALSE) +# shape = "Sample_type"
geom_point(size = 1) +
scale_color_manual(values = colors.1) +
labs(color = "Sequencing run") +
theme_bw() +
theme(legend.position = "right", aspect.ratio = 1)
#--- Save HQ image (also can use ggsave() or png() ):
png("./results/graphics/beta-sr-bray.png", units = "in", width = 8, height = 8, res = 300)
bray.plot.sr
dev.off()
#--- Plot NMDS comparing host status at ASV level:
# Replace NAs by a character:
sample_data(pqs.hell)$Host_status <- as.character(sample_data(pqs.hell)$Host_status)
sample_data(pqs.hell)$Host_status[is.na(sample_data(pqs.hell)$Host_status)] <- "N/A"
# Reorder levels:
sample_data(pqs.hell)$Host_status <- factor(sample_data(pqs.hell)$Host_status,
levels = c("HMA", "LMA", "N/A"))
# Make ordination plot:
bray.plot.ht <- plot_ordination(pqs.hell, bray.ord, type = "samples", axes = 1:4,
color = "Host_status", label = NULL, title = NULL,
justDF = FALSE) +
geom_point(size = 1) +
scale_color_brewer(palette = "Set2", direction = 1) +
#labs(shape = ("Sample type")) +
theme_bw() +
theme(legend.position = "right", legend.title = element_blank(),
aspect.ratio = 1)
#--- Save HQ image (also can use ggsave() or png() ):
png("./results/graphics/beta-ht-bray.png", units = "in", width = 8, height = 8, res = 300)
bray.plot.ht
dev.off()
#--- Plot NMDS comparing sponge family at ASV level:
bray.plot.or <- plot_ordination(pqs.hell, bray.ord, type = "samples", axes = 1:4,
color = "Sponge_Family", label = NULL, title = NULL,
justDF = FALSE) +
geom_point(size = 1) +
scale_color_manual(values = colors.1) +
#labs(shape = ("Sample type")) +
theme_bw() +
#scale_shape_manual(values = c(17, 16)) +
#annotation_custom(textGrob(label = "stress = 0.1180", x = 0.8, y = 0.95, hjust = 0)) +
theme(legend.position = "right", legend.title = element_blank(),
aspect.ratio = 1)
#--- Save HQ image (also can use ggsave() or png() ):
png("./results/graphics/beta-fam-bray.png", units = "in", width = 8, height = 8, res = 300)
bray.plot.or
dev.off()
### Ordination of composition from UniFrac distances ----
#--- Set a seed because the starting position in NMDS algorithm is random:
set.seed(1000)
#--- Calculate ordination from dissimilarities:
wunif.ord <- ordinate(pqs.hell, method = "NMDS", distance = "unifrac",
weighted = FALSE)
wunifw.ord <- ordinate(pqs.hell, method = "NMDS", distance = "unifrac",
weighted = TRUE)
### Plotting community composition from UniFrac distances ----
#--- Plot NMDS comparing environments and habitats at ASV level:
unifrac.plot <- plot_ordination(pqs.hell, wunif.ord, type = "samples",
color = "Environment", shape = "Habitat", label = NULL) +
geom_point(size = 3) +
scale_color_manual(values = color.latzone,
labels = c("Antarctic", "Temperate", "Tropical")) +
#labs(shape = "Habitat", fill = "Zone") +
labs(shape = "Habitat", color = "Environment", title = NULL) +#"Unweighted UniFrac distance"
# caption = paste0("PERMANOVA p=0.001, R2=0.0755 |",
# " ANOSIM p=", round(anosim.wunif$signif, 4), ", R=", round(anosim.wunif$statistic, 4))) +
theme_bw() +
#scale_shape_manual(values = c(17, 16)) +
annotation_custom(textGrob(label = paste0("stress = ", round(wunif.ord$stress, 3)), x = 0.7, y = 0.95, hjust = 0)) +
theme(legend.position = "bottom", legend.title = element_text(size = 18, face = "bold"),
aspect.ratio = 1,
text = element_text(size = 15),
plot.title = element_text(hjust = 0.5, size = 20),
legend.text = element_text(size = 20),
plot.caption = element_text(size = 17, face = "bold"),
legend.box = "vertical") +
guides(color = guide_legend(override.aes = list(size = 5)),
shape = guide_legend(override.aes = list(size = 5)))
#--- Plot NMDS comparing ecoregions at ASV level:
unif.plot.eco <- plot_ordination(pqs.hell, wunif.ord, type = "samples",
color = "Marine_Ecoregion", shape = "Environment", label = NULL) +
geom_point(size = 2) +
scale_color_manual(values = colors.1) +
#labs(shape = "Habitat", fill = "Zone") +
labs(shape = "Environment", color = "Marine_Ecoregion") +
# caption = paste0("PERMANOVA p=0.001, R2=0.0755 |",
# " ANOSIM p=", round(anosim.wunif$signif, 4), ", R=", round(anosim.wunif$statistic, 4))) +
theme_bw() +
scale_shape_discrete(labels = c("Antarctic", "Temperate", "Tropical")) +
# values = c(17, 16)) +
annotation_custom(textGrob(label = paste0("stress = ", round(wunif.ord$stress, 3)), x = 0.7, y = 0.95, hjust = 0)) +
theme(legend.position = "right", legend.title = element_text(size = 18, face = "bold"),
aspect.ratio = 1,
text = element_text(size = 15),
plot.title = element_text(hjust = 0.5, size = 20),
legend.text = element_text(size = 20),
plot.caption = element_text(size = 17, face = "bold")) +
guides(color = guide_legend(override.aes = list(size = 5)),
shape = guide_legend(override.aes = list(size = 5)))
#stat_ellipse(level = 0.95, linetype = 1, aes(group = Habitat), show.legend = F) # type = "norm"
#--- Save HQ image (also can use ggsave() or png() ):
png("./results/graphics/beta-envs-unif.png", units = "in", width = 7.5, height = 7.5, res = 300)
unifrac.plot
dev.off()
png("./results/graphics/beta-eco-unif.png", units = "in", width = 11, height = 11, res = 300)
unif.plot.eco
dev.off()
#--- Plot just the legend:
legend.beta <- cowplot::get_legend(plot_ordination(pqs.hell, wunif.ord, type = "samples",
color = "Environment", shape = "Habitat", label = NULL) +
geom_point(size = 3) +
scale_color_manual(values = color.latzone,
labels = c("Antarctic", "Temperate", "Tropical")) +
#labs(shape = "Habitat", fill = "Zone") +
labs(shape = "Habitat", color = "Environment", title = "Unweighted UniFrac distance") +
# caption = paste0("PERMANOVA p=0.001, R2=0.0755 |",
# " ANOSIM p=", round(anosim.wunif$signif, 4), ", R=", round(anosim.wunif$statistic, 4))) +
theme_bw() +
#scale_shape_manual(values = c(17, 16)) +
annotation_custom(textGrob(label = paste0("stress = ", round(wunif.ord$stress, 3)), x = 0.7, y = 0.95, hjust = 0)) +
theme(legend.position = "bottom", legend.title = element_text(size = 18, face = "bold"),
aspect.ratio = 1,
text = element_text(size = 15),
plot.title = element_text(hjust = 0.5, size = 20),
legend.text = element_text(size = 20),
plot.caption = element_text(size = 17, face = "bold")) +
guides(color = guide_legend(override.aes = list(size = 5)),
shape = guide_legend(override.aes = list(size = 5))))
legend.beta <- as_ggplot(legend.beta)
#--- Save HQ image (also can use ggsave() or png() ):
png("./results/graphics/beta-legend.png", units = "in", width = 15, height = 1.5, res = 300)
legend.beta
dev.off()
### Calculating distance matrices ----
#--- Set a seed:
set.seed(1000)
#--- Calculate distance matrixes with Bray-Curtis and UniFrac distances:
# At ASV level:
bray.dist <- phyloseq::distance(pqs.hell, method = "bray")
wunif.dist <- phyloseq::distance(pqs.hell, method = "unifrac")
# At different taxonomic levels:
bray.genus.dist <- phyloseq::distance(pqs.genus.hell, method = "bray")
bray.family.dist <- phyloseq::distance(pqs.family.hell, method = "bray")
bray.order.dist <- phyloseq::distance(pqs.order.hell, method = "bray")
bray.class.dist <- phyloseq::distance(pqs.class.hell, method = "bray")
bray.phylum.dist <- phyloseq::distance(pqs.phylum.hell, method = "bray")
wu.genus.dist <- phyloseq::distance(pqs.genus.hell, method = "unifrac")
wu.family.dist <- phyloseq::distance(pqs.family.hell, method = "unifrac")
wu.order.dist <- phyloseq::distance(pqs.order.hell, method = "unifrac")
wu.class.dist <- phyloseq::distance(pqs.class.hell, method = "unifrac")
wu.phylum.dist <- phyloseq::distance(pqs.phylum.hell, method = "unifrac")
### Performing PERMANOVA among groups ----
#--- Prepare metadata:
# metadata <- as(sample_data(pqs), "data.frame")
# metadata.genus <- as(sample_data(pqs.genus), "data.frame")
#--- Set a seed:
set.seed(1000)
# From Bray-Curtis:
permanova.bray <- adonis2(bray.dist ~ Habitat*Sponge_Family,
data = metadata,
permutations = 999,
strata = metadata$Sequencing_run_ID)
set.seed(1000)
permanova.bray.eco <- adonis2(bray.dist ~ Marine_Ecoregion*Sponge_Family,
data = metadata,
permutations = 999,
strata = metadata$Sequencing_run_ID)
# From unweighted UniFrac:
set.seed(1000)
permanova.wunif <- adonis2(wunif.dist ~ Habitat*Sponge_Family,
data = metadata,
permutations = 999,
strata = metadata$Sequencing_run_ID)
set.seed(1000)
permanova.wunif.eco <- adonis2(wunif.dist ~ Marine_Ecoregion*Sponge_Family,
data = metadata,
permutations = 999,
strata = metadata$Sequencing_run_ID)
### Performing pairwise PERMANOVA ----
#--- Set a seed:
set.seed(1000)
# For habitat:
pairw.bray <- pairwise.adonis(bray.dist,
factors = metadata$Habitat,
p.adjust.m = "none",
perm = 999) #, reduce = "Antarctic", also "bonferroni" in p.adjust
pairw.wunif <- pairwise.adonis(wunif.dist,
factors = metadata$Habitat,
p.adjust.m = "none",
perm = 999) #, reduce = "Antarctic", also "bonferroni" in p.adjust
# For sponge family:
pairw.bray.f <- pairwise.adonis(bray.dist,
factors = metadata$Sponge_Family,
p.adjust.m = "none",
perm = 999)
# For ecoregion:
pairw.bray.e <- pairwise.adonis(bray.dist,
factors = metadata$Marine_Ecoregion,
p.adjust.m = "none",
perm = 999)
### Testing homogeneity of dispersion (variances) PERMDISP ----
#--- Set a seed:
set.seed(1000)
# From Bray-Curtis:
bray.disp <- betadisper(bray.dist, metadata$Habitat)
homog.bray <- permutest(bray.disp, permutations = 999)
# bray.disp2 <- betadisper(bray.dist, metadata$Sponge_Family)
# homog.bray2 <- permutest(bray.disp2, permutations = 999)
#
# bray.disp3 <- betadisper(bray.dist, metadata$Marine_Ecoregion)
# homog.bray3 <- permutest(bray.disp3, permutations = 999)
# From unweighted UniFrac:
wunif.disp <- betadisper(wunif.dist, metadata$Habitat)
homog.wunif <- permutest(wunif.disp, permutations = 999)
### Performing ANOSIM ----
#--- Set a seed:
set.seed(1000)
# From Bray-Curtis:
anosim.bray <- anosim(x = bray.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.bray.eco <- anosim(x = bray.dist, grouping = metadata$Marine_Ecoregion,
permutations = 999)
anosim.genus.bray <- anosim(x = bray.genus.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.family.bray <- anosim(x = bray.family.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.order.bray <- anosim(x = bray.order.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.class.bray <- anosim(x = bray.class.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.phylum.bray <- anosim(x = bray.phylum.dist, grouping = metadata$Habitat,
permutations = 999)
# From unweighted UniFrac:
anosim.wunif <- anosim(x = wunif.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.genus.unif <- anosim(x = wu.genus.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.family.unif <- anosim(x = wu.family.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.order.unif <- anosim(x = wu.order.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.class.unif <- anosim(x = wu.class.dist, grouping = metadata$Habitat,
permutations = 999)
anosim.phylum.unif <- anosim(x = wu.phylum.dist, grouping = metadata$Habitat,
permutations = 999)
### Performing SIMPER ----
#--- Join taxa names:
# From ASV phyloseq object:
taxa_names(pqs.rel) <- paste(as.data.frame(tax_table(pqs.rel))$Class,
as.data.frame(tax_table(pqs.rel))$Order,
as.data.frame(tax_table(pqs.rel))$Family,
as.data.frame(tax_table(pqs.rel))$Genus,
rownames(tax_table(pqs.rel)),
sep = "; ")
# From genus-agglomerated phyloseq object:
taxa_names(pqs.genus.rel) <- paste(as.data.frame(tax_table(pqs.genus.rel))$Class,
as.data.frame(tax_table(pqs.genus.rel))$Order,
as.data.frame(tax_table(pqs.genus.rel))$Family,
as.data.frame(tax_table(pqs.genus.rel))$Genus,
rownames(tax_table(pqs.genus.rel)),
sep = "; ")
#--- Run SIMPER (RAM expensive):
simper.asv <- simper(otu_table(pqs), sample_data(pqs)$Habitat) #pqs
simper.asv <- simper(otu_table(pqs.rel), sample_data(pqs.rel)$Habitat) #pqs.rel
simper.genus <- simper(otu_table(pqs.genus.rel), sample_data(pqs.genus.rel)$Habitat) #pqs.genus.rel
#--- Prepare the resulting table:
simper.asv.tab <- as.data.frame(summary(simper.asv)$`Antarctic sponge_Non-Antarctic sponge` %>%
round(3) %>%
head(100))
#--- Export table:
write.table(simper.asv.tab, file ="./results/simper-100first.csv",
sep="\t", na = "NA", row.names = T, col.names = NA)
### Performing LDA (Genera differentially abundant) ----
#--- Set a seed:
set.seed(1000)
#--- Run LDA:
pqs.lefse <- run_lefse(pqs, wilcoxon_cutoff = 0.01, group = "Habitat",
multigrp_strat = F, taxa_rank = "Genus", norm = "none",
lda_cutoff = 3)
# pqs.lefse.g <- run_lefse(pqs.genus, wilcoxon_cutoff = 0.01, group = "Habitat",
# multigrp_strat = F, taxa_rank = "none", norm = "none",
# lda_cutoff = 3)
#
# pqs.lefse2 <- run_lefse(pqs, wilcoxon_cutoff = 0.01, group = "Habitat",
# multigrp_strat = F, taxa_rank = "none", norm = "none",
# lda_cutoff = 3)
#--- Inspect results:
tail(marker_table(pqs.lefse),10)
plot_ef_bar(pqs.lefse)
marker_table(pqs.lefse)$enrich_group <- factor(as.factor(marker_table(pqs.lefse)$enrich_group),
levels = c("Non-Antarctic sponge", "Antarctic sponge"))
#--- Customize format of results:
lda.features <- marker_table(pqs.lefse)$feature
lda.features[c(1,5,6,7,9,15,16,18,19,20)] <- c("Unassigned Gammaproteobacteria",
"Unassigned Burkholderiales",
"Unassigned Flavobacteriaceae",
"Unassigned Halieaceae",
"Unassigned Nitrincolaceae",
"UBA10353 marine group",
"Unnasigned Bacteria",
"Unassigned Alphaproteobacteria",
"Unassigned Nitrosopumilaceae",
"Unassigned Proteobacteria")
#--- Plot abundances of each group in boxplots:
# abund.plot <- plot_abundance(pqs.lefse, group = "Habitat")
#--- Plot LDA:
lda.plot <- plot_ef_bar(pqs.lefse) +
scale_fill_manual(values = rev(colors.hab)) +
guides(fill = guide_legend(reverse = TRUE)) +
theme_bw() +
theme(panel.grid.major.y = element_blank(), legend.position = "none",
legend.title = element_text(size = 13, face = "bold"),
text = element_text(size = 15)) +
scale_y_discrete(labels = rev(lda.features))
#--- Plot legend:
legend.lda <- cowplot::get_legend(lda.plot)
legend.lda <- as_ggplot(legend.lda)
#--- Save HQ image (also can use ggsave() or png() ):
png("./results/graphics/lda-hab.png", units = "in", width = 6, height = 6, res = 300)
lda.plot
dev.off()
png("./results/graphics/lda-leg.png", units = "in", width = 6, height = 1, res = 300)
legend.lda
dev.off()
### Making a heatmap based on Bray-Curtis dissimilarities (bonus) ----
#--- Fix matrix of distances:
bray.dist.mat <- as.matrix(bray.dist)
wunif.dist.mat <- as.matrix(wunif.dist)
#--- Isolate annotations for heatmap:
colnames(get_variable(pqs.hell))
tmp.data <- data.frame(sample_data(pqs.hell)[,6]) # Environment
tmp.data2 <- data.frame(sample_data(pqs.hell)[,7]) # Ecoregion
#--- Define colors for plotting:
library(RColorBrewer)
library(viridis)
tmp.color.latzone <- list(Environment =
c(Antarctic = "#2166ac", Temperate = "#67a9cf", Tropical = "#b2182b"))
colors.eco <- list(Marine_Ecoregion =
c('Antarctic Peninsula' = "deepskyblue2",
'South Shetland Islands' = "#2166ac",
'Adriatic Sea' = "darkorchid4",
'Bahamian' = "coral3",
'Central and Southern Great Barrier Reef' = "tomato4",
'Central New Zealand' = "turquoise",
'Hawaii' = "darkgoldenrod3",
'South European Atlantic Shelf' = "lightpink",
'Southern Norway' = "seagreen4",
'Southwestern Caribbean' = "limegreen",
'Western Caribbean' = "orchid1",
'Western Mediterranean' = "olivedrab4"))
#--- Plot heatmaps:
# For environment:
tmp.heatmap <- as.ggplot(pheatmap(bray.dist.mat, scale = "none", clustering_method = "ward.D",
fontsize = 7,
cluster_cols = T,
cluster_rows = T,
show_colnames = T,
na_col = "white",
cutree_rows = 1,
cutree_cols = 2,
annotation_col = tmp.data,
annotation_row = tmp.data,
# legend = FALSE,
annotation_colors = tmp.color.latzone,
show_rownames = F,
border_color = NULL,
treeheight_row = 50,
treeheight_col = 50,
annotation_names_row = F,
annotation_names_col = F,
fontsize_col = 2,
fontsize_row = 2,
angle_col = 90,
filename = "./results/graphics/heatmap.png",
# color = colorRampPalette(colors = c())
color = brewer.pal(8, "BuPu"),#RdPu
legend = T,
annotation_legend = T))
#kmeans_k = 4))
# For ecoregion:
tmp.heatmap <- as.ggplot(pheatmap(bray.dist.mat, scale = "none", clustering_method = "ward.D",
fontsize = 7,
cluster_cols = T,
cluster_rows = T,
show_colnames = T,
na_col = "white",
cutree_rows = 1,
cutree_cols = 2,
annotation_col = tmp.data2,
annotation_row = tmp.data2,
# legend = FALSE,
annotation_colors = colors.eco,
show_rownames = F,
border_color = NULL,
treeheight_row = 50,
treeheight_col = 50,
annotation_names_row = F,
annotation_names_col = F,
fontsize_col = 2,
fontsize_row = 2,
angle_col = 90,
filename = "./results/graphics/heatmap-eco.png",
# color = colorRampPalette(colors = c())
color = brewer.pal(8, "BuPu"),#RdPu
legend = T,
annotation_legend = T))
#--- Export tables of PERMANOVA comparisons ----
write.table(permanova.bray, file ="./results/stats_bray_permanova.txt",
sep="\t", na = "NA", row.names = T, col.names = NA)
write.table(permanova.bray.eco, file ="./results/stats_bray_permanova_eco.txt",
sep="\t", na = "NA", row.names = T, col.names = NA)
write.table(permanova.wunif, file ="./results/stats_wunif_permanova.txt",
sep="\t", na = "NA", row.names = T, col.names = NA)
write.table(pairw.bray, file ="./results/permanova_pw_habitat.txt",
sep="\t", na = "NA", row.names = T, col.names = NA)
write.table(pairw.bray.f, file ="./results/permanova_pw_family.txt",
sep="\t", na = "NA", row.names = T, col.names = NA)
write.table(pairw.bray.e, file ="./results/permanova_pw_ecoreg.txt",
sep="\t", na = "NA", row.names = T, col.names = NA)
#--- Save things -----------------------------------------------
# Save objects:
saveRDS(bray.dist, file = "./rds-files/bray_dist.rds")
saveRDS(bray.ord, file = "./rds-files/bray_ord.rds")
# Save the current work and objects:
save.image("R-graph-bdiv.RData")