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Moss_Exploration.R
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# SMP Moss Microbiome ##########################################################
## Author: [email protected] University of Fribourg 2021 - 2023 #############
library("phyloseq")
library("ggplot2")
theme_set(theme_bw(base_size = 20) +
theme(rect = element_rect(fill = "transparent")))
library("gridExtra")
library("vegan")
library("gplots")
library("reshape2")
rm(list = ls())
gc()
color_scheme <- c("#7EC8E3", "#B6D7A8", "#B4B4B4", "#FFD966", "#C9A0DC") # LT-LM-LV-LE-CB
## Moss data #################################################################
moss.pa <- readRDS("rds/Moss_prok.a.RDS")
moss.ea <- readRDS("rds/Moss_euk.a.RDS")
moss <- readRDS("rds/Moss_moss.RDS")
sample_data(moss)$Site <- ordered(sample_data(moss)$Site,
levels = c("LT", "LM", "LV", "LE", "CB"))
## Convert ps to df ###########################################################
tax <- as.data.frame(tax_table(moss))
otu <- as.data.frame(t(otu_table(moss)))
# summary(rownames(tax) == rownames(otu))
tax.otu <- merge(tax, otu, by = 0, all = TRUE)
rownames(tax.otu) <- tax.otu$Row.names
names(tax.otu)[1] <- "Taxa"
rm(tax, otu)
## Plot taxa ##################################################################
tax.otu[, -c(1:7)] <- log1p(tax.otu[, -c(1:7)])
tax.otu$CB <- rowSums(tax.otu[grepl("CB", names(tax.otu))])
tax.otu$LE <- rowSums(tax.otu[grepl("LE", names(tax.otu))])
tax.otu$LM <- rowSums(tax.otu[grepl("LM", names(tax.otu))])
tax.otu$LT <- rowSums(tax.otu[grepl("LT", names(tax.otu))])
tax.otu$LV <- rowSums(tax.otu[grepl("LV", names(tax.otu))])
tax.otu.agg <- tax.otu[colnames(tax.otu) %in%
c("Domain", "Phylum", "LT", "LM", "LV", "LE", "CB")]
tax.otu.agg <- reshape2::melt(tax.otu.agg, id.vars = c("Domain", "Phylum"),
variable.name = "Site",
value.name = "Count")
tax.otu.agg <- aggregate(Count ~ Domain + Phylum + Site, tax.otu.agg, sum)
tax.otu.agg$Site <- ordered(tax.otu.agg$Site,
levels = c("LT", "LM", "LV", "LE", "CB"))
tax.otu.agg$Domain[tax.otu.agg$Domain == "Archaea"] <- "A."
## Reorder taxonomically
tax.otu.agg[, c(1:2)] <- lapply(tax.otu.agg[, c(1:2)], as.factor)
str(tax.otu.agg)
tax <- c(
## Archaea
"Euryarchaeota", "Nanoarchaeota",
## Proteobacteria
"Bdellovibrionota", "Desulfobacterota", "Proteobacteria",
"SAR324_clade(Marine_group_B)",
## Deltaproteobacteria
"Myxococcota",
## PVC group
"Planctomycetota", "Verrucomicrobiota",
## FCB group
"Gemmatimonadota",
## Singles
"Acidobacteriota", "Bacteroidota", "Chloroflexi", "Dependentiae",
## Terrabacteria
"Actinobacteriota", "Armatimonadota", "Cyanobacteria", "Firmicutes",
## ?
"WPS-2", "FCPU426", "Patescibacteria", "MBNT15",
"Elusimicrobiota", "Spirochaetota", "NB1-j", "Abditibacteriota",
"RCP2-54", "Latescibacterota", "Sumerlaeota", "Fibrobacterota", "WS4",
"Deinococcota",
### Eukaryotes ###
## Amoebazoa
"Amoebozoa_ph", "Dictyostelia", "Gracilipodida", "Myxogastria",
"Schizoplasmodiida", "Protosteliida",
## SAR: Heterokonta/Stramenopiles
"Bicosoecida", "Ochrophyta_ph", "Diatomea", "Hyphochytriomycetes",
"MAST-3", "MAST-12", "Peronosporomycetes",
## SAR: Rhizaria
"Cercozoa",
## Harosa: Alveolata
"Apicomplexa", "Ciliophora", "Dinoflagellata", "Protalveolata",
## Excavata
"Euglenozoa", "Heterolobosea", "Parabasalia",
## Algae
"Chlorophyta_ph", "Cryptophyceae_ph", "Phragmoplastophyta",
"Klebsormidiophyceae",
"Incertae_Sedis",
## Holomycota
"Nucleariidae_and_Fonticula_group",
## Fungi
"Ascomycota", "Basidiomycota", "Chytridiomycota",
"Cryptomycota", "LKM15", "Microsporidia", "Mucoromycota", "Zoopagomycota",
## Sister group of fungi
"Aphelidea",
## Choanozoa
"Holozoa_ph",
## Metazoa
"Annelida", "Platyhelminthes", "Nematozoa", "Rotifera", "Tardigrada",
"Gastrotricha", "Arthropoda"
)
## Pronounce small values as they are invisible otherwise
tax.otu.agg$Count[tax.otu.agg$Count < 0.1 & tax.otu.agg$Count > 0] <- 0.1
## Plot
ggplot(tax.otu.agg,
aes(x = factor(Phylum, level = rev(tax)),
y = Count,
fill = Site)) +
geom_bar(stat = "identity") +
facet_grid(Domain ~ Site, scales = "free", space = "free_y") +
ylab(expression(paste(ln[e], "(Abundance + 1) [%]"))) +
xlab("") +
coord_flip() +
theme_bw(base_size = 14) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, vjust = 1)) +
scale_fill_manual(values = color_scheme)
# ggsave("img/Moss_Phyla.pdf", width = 8.27, height = 11.69)
### Inspect taxa and frequencies
df <- tax.otu[tax.otu$Domain == "Bacteria", ]
sort(unique(tax.otu$Phylum))
summary(colSums(df[, -c(1:7)]) == 0)
unique(df$Genus)
## Alpha-diversity ############################################################
### Prokaryotes ############################################################
otu.p <- data.frame(
otu_table(transform_sample_counts(
moss.pa, function(abund) {1 * (abund > 0)})
))
otu.p$alpha <- rowSums(otu.p)
otu.p <- otu.p[c("alpha")]
otu.p$FullID <- rownames(otu.p)
otu.p$Domain <- "Prokaryotes"
## Eukaryotes
otu.e <- data.frame(otu_table(
transform_sample_counts(moss.ea,
function(abund) {1 * (abund > 0)})
))
otu.e$alpha <- rowSums(otu.e)
otu.e <- otu.e[c("alpha")]
otu.e$FullID <- rownames(otu.e)
otu.e$Domain <- "Eukaryotes"
## Merge
otus <- rbind(otu.p, otu.e)
rm(otu.p, otu.e)
## Merge with metadata
meta.m <- data.frame(sample_data(moss.pa))
otus <- merge(otus, meta.m, by = "FullID", all = TRUE)
# otus$FullID == rownames(meta.m)
otus$Domain <- ordered(otus$Domain, levels = c("Prokaryotes", "Eukaryotes"))
otus$Site <- ordered(otus$Site, levels = c("LT", "LM", "LV", "LE", "CB"))
## Alpha-diversity
ggplot(otus, aes(x = Site, y = alpha)) +
geom_boxplot(color = "black", size = 0.2, outlier.shape = NA) +
geom_jitter(aes(fill = Site), height = 0, width = 0.3, size = 2, shape = 21) +
stat_summary(aes(group = Site), fun = mean,
colour = "black", geom = "point",
shape = 8, size = 3.5, show.legend = FALSE) +
facet_grid(Domain ~ Site, scales = "free") +
scale_fill_manual(values = color_scheme) +
xlab("") +
ylab(expression(alpha-diversity)) +
theme(legend.position = "none", legend.direction = "horizontal",
axis.text.x = element_blank())
# ggsave("img/Moss_Alpha.pdf", width = 11.69, height = 5)
## Mean alpha-diversity by site and domain
aggregate(alpha ~ Site + Domain, data = otus, mean)
## Hypothesis test for difference of mean alpha between fen and raised bog sites
### Test for normality
# pdf("img/Moss_QQNormAlpha.pdf", width = 11.69, height = 6)
par(mfrow = c(1, 2))
qqnorm(otus$alpha[otus$Domain == "Prokaryotes"], main = "Prokaryotes")
qqline(otus$alpha[otus$Domain == "Prokaryotes"], lty = 2)
qqnorm(otus$alpha[otus$Domain == "Eukaryotes"], main = "Eukaryotes", ylab = "")
qqline(otus$alpha[otus$Domain == "Eukaryotes"], lty = 2)
dev.off()
## One-way ANOVA followed by Tukey's HSD
### Prokaryotes
res.aov <- aov(alpha ~ Site, data = otus[otus$Domain == "Prokaryotes", ])
summary(res.aov)
TukeyHSD(res.aov)
### Eukaryotes
res.aov <- aov(alpha ~ Site, data = otus[otus$Domain == "Eukaryotes", ])
summary(res.aov)
TukeyHSD(res.aov)
################################################################################
### Ordination ################################################################
## Prokaryotes
set.seed(128252)
sample_data(moss.pa)$Site <- ordered(sample_data(moss.pa)$Site,
levels = c("LT", "LM", "LV", "LE", "CB"))
sample_data(moss.pa)$Sector <- as.factor(sample_data(moss.pa)$Sector)
moss.pa.log <- transform_sample_counts(moss.pa,
function(otu) {log1p(otu)})
moss.nmds <- ordinate(moss.pa.log,
method = "NMDS", distance = "bray", k = 2,
trymax = 50)
moss.nmds
nmds.prok <- plot_ordination(moss.pa.log, moss.nmds,
color = "Site", shape = "Sector",
title = NULL) +
stat_ellipse(aes(group = Site), type = "t", linetype = 2, linewidth = 0.2) +
geom_point(size = 3) +
scale_colour_manual(values = color_scheme) +
coord_fixed(ratio = 1) +
theme(legend.position = "top", legend.direction = "horizontal",
legend.box = "vertical", legend.margin = margin()) +
xlim(-1, 0.9) +
ylim(-1, 0.6) +
xlab("nMDS1") +
ylab("nMDS2")
## Eukaryotes
set.seed(128252)
sample_data(moss.ea)$Site <- ordered(sample_data(moss.ea)$Site,
levels = c("LT", "LM", "LV", "LE", "CB"))
sample_data(moss.ea)$Sector <- as.factor(sample_data(moss.ea)$Sector)
moss.ea.log <- transform_sample_counts(moss.ea, function(otu) {log1p(otu)})
moss.nmds <- ordinate(moss.ea.log, method = "NMDS", distance = "bray", k = 2,
trymax = 50)
moss.nmds
nmds.euk <- plot_ordination(moss.ea.log, moss.nmds,
color = "Site", shape = "Sector",
title = NULL) +
stat_ellipse(aes(group = Site), type = "t", linetype = 2, linewidth = 0.2) +
geom_point(size = 3) +
scale_colour_manual(values = color_scheme) +
coord_fixed(ratio = 1) +
theme(legend.position = "top", legend.direction = "horizontal",
legend.box = "vertical", legend.margin = margin()) +
xlim(-1, 0.9) +
ylim(-1, 0.6) +
xlab("nMDS1") +
ylab("")
## Arrange plots side by side and export
nmds.both <- arrangeGrob(nmds.prok, nmds.euk, nrow = 1)
# ggsave("img/Moss_nMDS.pdf", nmds.both, width = 11.69, height = 8.27)
################################################################################
################################################################################
### Prepare metadata for SEM ##################################################
## Read metadata
mossMeta <- read.table("csv/MossesMetadata.csv", sep = "\t", header = TRUE)
mossMeta$FullID <- gsub("_", "", mossMeta$FullID)
mossMeta$Sector <- substr(mossMeta$FullID, 3, 3)
## Ellenberg values
ev <- read.table("csv/EVMeans.csv", sep = "\t", header = TRUE)
## Merge
mossMeta <- merge(mossMeta, ev, by = "Site")
## Canopy cover
cover <- read.table("csv/CanopyCover.csv", header = TRUE, sep = "\t",
fill = FALSE, check.names = TRUE)
leaves <- read.table("csv/LeafSamples.csv",
header = TRUE, sep = ",", fill = FALSE)
cover <- merge(leaves, cover[, c(1:2, 8)],
by.x = c("Site", 'PlantOld'),
by.y = c("Site", 'PlantOld'))
cover$Sector <- substr(cover$FullID, 4, 4)
## Calculate mean canopy cover sector-wise
cover.mean <- aggregate(cover$CanopyCover,
list(cover$Site, cover$Sector),
mean,
na.rm = TRUE)
names(cover.mean) <- c("Site", "Sector", "CanopyCover")
mossMeta <- merge(mossMeta, cover.mean, by = c("Site", "Sector"))
## Dry weight of sampled mosses as proxy of habitat size and sampling intensity
weight <- read.table("csv/MossSamples.csv",
header = TRUE, sep = ",",
fill = FALSE, check.names = TRUE)
weight$FullID <- gsub("_", "", weight$FullID)
mossMeta <- merge(mossMeta, weight[, c(1, 5)], by = "FullID")
################################################################################
### Reduce coordinates to 1D with MDS
rownames(mossMeta) <- mossMeta$FullID
xy.mds <- cmdscale(dist(mossMeta[names(mossMeta) %in% c("SiteSectorCentroidsX",
"SiteSectorCentroidsY")]),
eig = TRUE, k = 1)
xy.mds$GOF # should be > 0.8
distance <- as.data.frame(xy.mds$points)
colnames(distance) <- "Distance"
distance$FullID <- row.names(distance)
xy.mds.df <- as.data.frame(xy.mds$points)
colnames(xy.mds.df) <- "Points"
xy.mds.df$Plant <- rownames(xy.mds.df)
ggplot(xy.mds.df) +
geom_text(aes(x = 0, y = Points, label = Plant), na.rm = TRUE) +
scale_x_continuous(breaks = seq(-1, 1, 1)) +
xlab("") +
ylab("MDS 1")
# ggsave("img/Moss_Distance_1D.pdf", width = 3, height = 6)
mossMeta <- merge(mossMeta, distance, by = "FullID")
### Moss as habitat
det <- read.table("csv/MossComposition.csv", sep = "\t", header = TRUE,
row.names = 1)
det[det == "x"] <- 1
det[det == ""] <- 0
det[] <- lapply(det, as.integer)
# det <- log1p(t(det))
det <- t(det)
set.seed(128252)
det.nmds <- metaMDS(det, distance = "bray", k = 1, trymax = 75,
autotransform = FALSE)
det.nmds
stressplot(det.nmds)
# pdf("img/Moss_DetnMDS.pdf", height = 8.27, width = 8.27)
margin(5, 5, 10, 5)
plot(det.nmds)
# dev.off()
det.nmds.sc <- data.frame(scores(det.nmds,
display = "site"))
names(det.nmds.sc) <- "MossComposition"
det.nmds.sc$Site <- substr(rownames(det.nmds.sc), 1, 2)
det.nmds.sc$Sector <- substr(rownames(det.nmds.sc), 3, 3)
mossMeta <- merge(mossMeta, det.nmds.sc,
by = c("Site", "Sector"))
################################################################################
### Microbiomes
## Prokaryotes
moss.prok.mb <- as.data.frame(otu_table(moss.pa))
moss.prok.mb <- log1p(moss.prok.mb)
set.seed(128252)
moss.prok.nmds <- vegan:::metaMDS(moss.prok.mb, distance = "bray",
trymax = 50, k = 2, autotransform = FALSE)
moss.prok.nmds
stressplot(moss.prok.nmds)
moss.prok.nmds.sc <- as.data.frame(scores(moss.prok.nmds,
display = "site"))
names(moss.prok.nmds.sc)[1] <- "mb.moss.prok"
moss.prok.nmds.sc$FullID <- rownames(moss.prok.nmds.sc)
mossMeta <- merge(mossMeta,
moss.prok.nmds.sc[, colnames(moss.prok.nmds.sc) %in% c("mb.moss.prok", "FullID")],
by = "FullID", all = TRUE)
## Eukaryotes
moss.euk.mb <- as.data.frame(otu_table(moss.ea))
moss.euk.mb <- log1p(moss.euk.mb)
set.seed(128252)
moss.euk.nmds <- vegan:::metaMDS(moss.euk.mb, distance = "bray",
trymax = 50, k = 2, autotransform = FALSE)
moss.euk.nmds
stressplot(moss.euk.nmds)
moss.euk.nmds.sc <- as.data.frame(scores(moss.euk.nmds,
display = "site"))
names(moss.euk.nmds.sc)[1] <- "mb.moss.euk"
moss.euk.nmds.sc$FullID <- rownames(moss.euk.nmds.sc)
mossMeta <- merge(mossMeta,
moss.euk.nmds.sc[, colnames(moss.euk.nmds.sc) %in% c("mb.moss.euk", "FullID")],
by = "FullID", all = TRUE)
## Both
moss.mb <- as.data.frame(otu_table(moss))
moss.mb <- log1p(moss.mb)
set.seed(128252)
moss.nmds <- vegan:::metaMDS(moss.mb, distance = "bray",
trymax = 50, k = 2, autotransform = FALSE)
moss.nmds
stressplot(moss.nmds)
moss.nmds.sc <- as.data.frame(scores(moss.nmds, display = "sites"))
names(moss.nmds.sc)[1] <- "mb.moss"
moss.nmds.sc$FullID <- rownames(moss.nmds.sc)
mossMeta <- merge(mossMeta,
moss.nmds.sc[, colnames(moss.nmds.sc) %in% c("mb.moss", "FullID")],
by = "FullID", all = TRUE)
### Export data
# write.table(mossMeta, "csv/Mosses_Metadata_unscaled.csv", sep = "\t",
# row.names = FALSE)
################################################################################
### Scale numeric variables that will be used in the SEM
str(mossMeta[, -c(1:9, 11:15)])
# str(mossMeta[, c(1:9, 11:15)])
par(mfrow = c(2, 1))
boxplot(mossMeta[, -c(1:9, 11:15)])
abline(h = 0, lty = 2, col = "gray")
mossMeta[, -c(1:9, 11:15)] <- apply(mossMeta[, -c(1:9, 11:15)], 2, scale)
boxplot(mossMeta[, -c(1:9, 11:15)])
abline(h = 0, lty = 2, col = "gray")
dev.off()
### Export data
# write.table(mossMeta, "csv/Mosses_Metadata_scaled.csv", sep = "\t",
# row.names = FALSE)
################################################################################
### Correlation and covariance
corPalette <- colorRampPalette(c("#D55E00", "white", "#56B4E9"))(n = 19)
## Sort covariance data.frame
covs <- data.frame(cov(mossMeta[, c(10, 16:17, 19:21, 23:26)],
use = "complete"))
covs <- covs[c("Altitude", "Light", "Temperature", "Humidity", "Soil.reaction",
"Nitrogen", "CanopyCover", "Moss_g", "Distance",
"MossComposition")]
covs <- covs[colnames(covs), ]
pdf("Moss_Metadata_Covariance.pdf", height = 8.27, width = 8.27)
heatmap.2(as.matrix(covs),
dendrogram = "none",
key = TRUE, key.title = "",
cellnote = round(covs, digits = 2),
notecol = "black",
tracecol = "black",
notecex = 0.6,
trace = "none",
distfun = function(x) {x},
cexCol = 1.5,
cexRow = 1.5,
symm = TRUE,
margins = c(16, 16),
col = corPalette,
Rowv = FALSE,
Colv = FALSE)
dev.off()
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theme_set(theme_bw(base_size = 9) +
theme(rect = element_rect(fill = "transparent")))
## Alpha-diversity vs environmental variables
mossMeta <- read.table("csv/Mosses_Metadata_unscaled.csv", sep = "\t",
header = TRUE)
mossMeta$Site <- ordered(mossMeta$Site,
levels = c("LT", "LM", "LV", "LE", "CB"))
# names(otus)
# names(mossMeta)
mossMeta <- merge(mossMeta, otus[, c(1:3)])
names(mossMeta)
mossMeta.s <- mossMeta[, c(2, 10, 16:17, 19:21, 23:31)]
names(mossMeta.s)
mossMeta.s.m <- melt(mossMeta.s, id.vars = c("Domain", "alpha", "Site"),
variable.name = "Variable", value.name = "Value")
names(mossMeta.s.m)
ggplot(mossMeta.s.m, aes(x = Value, y = alpha, color = Site)) +
geom_smooth(aes(group = 1), color = "gray",
method = "lm", formula = y ~ x, se = FALSE) +
geom_point(pch = 21) +
facet_grid(Domain ~ Variable, scales = "free", space = "free_y") +
ylab(expression(alpha-diversity)) +
xlab("m | - | - | - | - | - | % | g | m | - | - | - | -") +
scale_color_viridis_d() +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1))
# ggsave("Moss_AlphaVsEnvironment.pdf", height = 6, width = 11.69)
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