In [88]:
%load_ext rpy2.ipython
In [89]:
%%R
library(dplyr)
library(tidyr)
library(ggplot2)
library(compositions)
library(coenocliner)
library(vegan)
library(gridExtra)
In [90]:
%%R
set.seed(2)
M = 1000 # number of species
ming = 1.67323 # gradient minimum...
maxg = 1.76 # ...and maximum
#meang = mean(c(ming, maxg))
meang = 1.71
locs = seq(ming, maxg, length = 30) # gradient locations
opt = rnorm(M, mean=meang, sd=0.005) # runif(M, min = ming, max = maxg) # species optima
tol = rep(0.005, M) # species tolerances
h = ceiling(rlnorm(M, meanlog = 11)) # max abundances
pars = cbind(opt = opt, tol = tol, h = h) # put in a matrix
In [91]:
%%R -w 600 -h 350
opt1 = rnorm(M, mean=meang, sd=0.001)
pars = cbind(opt = opt1, tol = tol, h = h) # put in a matrix
PL1 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
# community total abundance
apply(PL1 %>% as.matrix, 1, sum) %>% sum %>% print
matplot(locs, PL1, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [92]:
%%R -w 600 -h 350
opt1 = rnorm(M, mean=meang, sd=0.005)
pars = cbind(opt = opt1, tol = tol, h = h) # put in a matrix
PL2 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PL2, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [93]:
%%R -w 600 -h 350
opt1 = rnorm(M, mean=meang, sd=0.01)
pars = cbind(opt = opt1, tol = tol, h = h) # put in a matrix
PL3 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PL3, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [94]:
%%R -w 600 -h 350
tol1 = rep(0.0001, M) # species tolerances
pars = cbind(opt = opt, tol = tol1, h = h) # put in a matrix
PW1 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PW1, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [95]:
%%R -w 600 -h 350
tol1 = rep(0.005, M) # species tolerances
pars = cbind(opt = opt, tol = tol1, h = h) # put in a matrix
PW2 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PW2, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [96]:
%%R -w 600 -h 350
tol1 = rep(0.01, M) # species tolerances
pars = cbind(opt = opt, tol = tol1, h = h) # put in a matrix
PW3 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PW3, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [97]:
%%R -w 600 -h 350
h1 = ceiling(rlnorm(M, meanlog = 9)) # max abundances
pars = cbind(opt = opt, tol = tol, h = h1) # put in a matrix
PH1 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PH1, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [98]:
%%R -w 600 -h 350
h1 = ceiling(rlnorm(M, meanlog = 11)) # max abundances
pars = cbind(opt = opt, tol = tol, h = h1) # put in a matrix
PH2 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PH2, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [99]:
%%R -w 600 -h 350
h1 = ceiling(rlnorm(M, meanlog = 13)) # max abundances
pars = cbind(opt = opt, tol = tol, h = h1) # put in a matrix
PH3 <- coenocline(locs, responseModel = "gaussian", params = pars,
countModel = "poisson")
matplot(locs, PH3, lty = "solid", type = "l", xlab = "BD", ylab = "Abundance")
In [100]:
%%R
# making a list
comm.l = list(
"PL1" = PL1,
"PL2" = PL2,
"PL3" = PL3,
"PW1" = PW1,
"PW2" = PW2,
"PW3" = PW3,
"PH1" = PH1,
"PH2" = PH2,
"PH3" = PH3
)
comm.l %>% names
In [69]:
%%R
add.tail.abund = function(mtx){
nzero = mtx[mtx == 0] %>% length
mtx[mtx == 0] = round(runif(nzero, 0, 1), 0)
return(mtx)
}
comm.l = lapply(comm.l, add.tail.abund)
comm.l = lapply(comm.l, add.tail.abund)
comm.l[[1]][1:5, 1:5]
In [127]:
%%R
to.rel = function(x){
y = sum(x)
if(y == 0){
return(x)
} else {
return(x / y)
}
}
comm.l.abs = lapply(comm.l, function(x) x %>% apply(1, function(x) x) %>% t %>% as.data.frame)
df = do.call(rbind, comm.l.abs) %>% as.data.frame
df$BD = rep(locs, comm.l.abs %>% names %>% length)
df$dataset = rownames(df)
df = df %>%
gather(OTU, rel_abund, starts_with('V')) %>%
mutate(dataset = gsub('\\.[0-9]+$', '', dataset))
df %>% head
In [130]:
%%R -w 800 -h 600
comm.l.abs = lapply(comm.l, function(x) x %>% apply(1, function(x) x) %>% t %>% as.data.frame)
df = do.call(rbind, comm.l.abs) %>% as.data.frame
df$BD = rep(locs, comm.l.abs %>% names %>% length)
df$dataset = rownames(df)
df = df %>%
gather(OTU, abs_abund, starts_with('V')) %>%
mutate(dataset = gsub('\\.[0-9]+$', '', dataset))
p1 = ggplot(df, aes(BD, abs_abund, group=OTU)) +
geom_line(alpha=0.5) +
labs(x='Buoyant density', y='Abundance') +
facet_wrap(~dataset) +
theme_bw() +
theme(
text = element_text(size=16)
)
p1
In [103]:
%%R -w 800 -h 600
to.rel = function(x){
y = sum(x)
if(y == 0){
return(x)
} else {
return(x / y)
}
}
comm.l.rel = lapply(comm.l, function(x) x %>% apply(1, to.rel) %>% t %>% as.data.frame)
df.rel = do.call(rbind, comm.l.rel) %>% as.data.frame
df.rel$BD = rep(locs, comm.l.rel %>% names %>% length)
df.rel$dataset = rownames(df.rel)
df.rel = df.rel %>%
gather(OTU, rel_abund, starts_with('V')) %>%
mutate(dataset = gsub('\\.[0-9]+$', '', dataset))
p1 = ggplot(df.rel, aes(BD, rel_abund, group=OTU)) +
geom_line() +
labs(x='Buoyant density', y='Relative abundance') +
facet_wrap(~dataset) +
theme_bw() +
theme(
text = element_text(size=16)
)
p1
In [140]:
%%R -w 650
df.shan = lapply(comm.l.rel, function(x) diversity(x))
df.shan = do.call(rbind, df.shan) %>% t %>% as.data.frame
df.shan$BD = locs
df.shan = df.shan %>%
gather(dataset, shannon, starts_with('P'))
p1 = ggplot(df.shan, aes(BD, shannon)) +
geom_line() +
facet_wrap(~dataset) +
labs(x='Buoyant density', y='Shannon index') +
theme_bw() +
theme(
text = element_text(size=16)
)
p1
In [119]:
%%R -w 500 -h 400
# pairwise correlations for each dataset
df.shan.bin = df.shan %>%
group_by(BD_bin = ntile(BD, 24))
calc.spearman = function(x){
cor(x[,'shannon.x'], x['shannon.y'], method='spearman')[1,1]
}
calc.pearson = function(x){
cor(x[,'shannon.x'], x['shannon.y'], method='pearson')[1,1]
}
df.shan.corr = inner_join(df.shan.bin, df.shan.bin, c('BD_bin' = 'BD_bin')) %>%
group_by(dataset.x, dataset.y) %>%
nest() %>%
mutate(model = purrr::map(data, calc.pearson)) %>%
unnest(pearson = model %>% purrr::map(function(x) x)) %>%
ungroup() %>%
select(-data, -model) %>%
mutate(pearson_txt = round(pearson, 2))
# plotting
ggplot(df.shan.corr, aes(dataset.x, dataset.y, fill=pearson)) +
geom_tile() +
geom_text(aes(label=pearson_txt), color='white') +
scale_fill_gradient('Pearson', low='black', high='red') +
labs(title='Shannon index') +
theme(
text = element_text(size=16),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
In [73]:
%%R
total.acomp.var = function(x){
x.acomp = acomp(x)
variation(x.acomp) %>% lower.tri %>% sum
}
lapply(comm.l.rel, total.acomp.var)
In [74]:
%%R -w 800
comm.l.rel.var = lapply(comm.l.rel, function(x) apply(x, 1, var))
df.var = do.call(rbind, comm.l.rel.var) %>% t %>% as.data.frame
df.var$BD = locs
df.var = df.var %>%
gather(dataset, variance, starts_with('P'))
p1 = ggplot(df.var, aes(BD, variance)) +
geom_line() +
facet_wrap(~dataset) +
theme_bw() +
theme(
text = element_text(size=16)
)
p1
In [75]:
%%R -w 800
p1 + facet_wrap(~ dataset, scale='free_y')
In [120]:
%%R -w 500 -h 400
# pairwise correlations for each dataset
df.var.bin = df.var %>%
group_by(BD_bin = ntile(BD, 24))
#calc.spearman = function(x){
# cor(x[,'variance.x'], x['variance.y'], method='spearman')[1,1]
#}
calc.pearson = function(x){
cor(x[,'variance.x'], x['variance.y'], method='pearson')[1,1]
}
df.var.corr = inner_join(df.var.bin, df.var.bin, c('BD_bin' = 'BD_bin')) %>%
group_by(dataset.x, dataset.y) %>%
nest() %>%
mutate(model = purrr::map(data, calc.pearson)) %>%
unnest(pearson = model %>% purrr::map(function(x) x)) %>%
ungroup() %>%
select(-data, -model) %>%
mutate(pearson_txt = round(pearson, 2))
# plotting
ggplot(df.var.corr, aes(dataset.x, dataset.y, fill=pearson)) +
geom_tile() +
geom_text(aes(label=pearson_txt), color='white') +
scale_fill_gradient(low='black', high='red') +
theme(
text = element_text(size=16)
)
In [121]:
%%R
# plotting shannon vs variance spearman correlations
df.corr.j = inner_join(df.shan.corr, df.var.corr, c('dataset.x' = 'dataset.x',
'dataset.y' = 'dataset.y'))
ggplot(df.corr.j, aes(pearson.x, pearson.y)) +
geom_point() +
labs(x='Pearson correlations of\nShannnon index values',
y='Pearson correlations of\nvariance values') +
theme_bw() +
theme(
text = element_text(size=16)
)
In [122]:
%%R
BD.diffs = function(BDs){
df.BD = expand.grid(BDs, BDs)
df.BD$diff = df.BD %>% apply(1, diff) %>% abs %>% as.vector
df.BD = df.BD %>%
spread(Var1, diff)
rownames(df.BD) = df.BD$Var2
df.BD$Var2 = NULL
dist.BD = df.BD %>% as.matrix
dist.BD[upper.tri(dist.BD, diag=TRUE)] = 0
dist.BD %>% as.dist
}
dist.BDs = BD.diffs(locs)
dist.BDs %>% length
In [123]:
%%R -w 800 -h 650
cal.corr = function(df, BDs, nclass=24, ...){
d = vegdist(df, ...)
d[is.nan(d)] = 0
stopifnot((d %>% length) == (BDs %>% length))
mantel.correlog(d, BDs, n.class=nclass)
}
corr.l = lapply(comm.l.rel, cal.corr, BDs=dist.BDs)
par(mfrow=c(3,3))
for (n in names(corr.l)){
plot(corr.l[[n]])
title(n)
}
In [137]:
%%R -w 500 -h 400
# binning
tmp.l = list()
for(x in names(corr.l)){
tmp = corr.l[[x]][1][['mantel.res']] %>% as.data.frame
tmp$data = x
tmp = tmp %>%
filter(!is.na(Mantel.cor)) %>%
group_by(class.index.bin = ntile(class.index, 12))
tmp.l[[x]] = tmp
}
df.corr = do.call(rbind, tmp.l) %>%
as.data.frame
# calculating correlations
calc.pearson = function(x){
cor(x[,'Mantel.cor.x'], x['Mantel.cor.y'], method='pearson')[1,1]
}
df.corr = inner_join(df.corr, df.corr, c('class.index.bin' = 'class.index.bin')) %>%
group_by(data.x, data.y) %>%
nest() %>%
mutate(model = purrr::map(data, calc.pearson)) %>%
unnest(pearson = model %>% purrr::map(function(x) x)) %>%
ungroup() %>%
select(-data, -model) %>%
mutate(pearson_txt = round(pearson, 2))
# plotting
ggplot(df.corr, aes(data.x, data.y, fill=pearson)) +
geom_tile() +
geom_text(aes(label=pearson_txt), color='white') +
scale_fill_gradient('Pearson', low='black', high='red') +
labs(title='Beta diversity correlogram') +
theme(
text = element_text(size=16),
axis.title.x = element_blank(),
axis.title.y = element_blank()
)
In [136]:
%%R -w 500 -h 400
# # converting to dataframe
# tmp.l = list()
# for(x in names(corr.l)){
# tmp = corr.l[[x]][1][['mantel.res']] %>% as.data.frame
# tmp$data = x
# tmp = tmp %>%
# filter(!is.na(Mantel.cor)) %>%
# group_by(class.index.bin = ntile(class.index, 12))
# tmp.l[[x]] = tmp
# }
# df.corr = do.call(rbind, tmp.l) %>%
# as.data.frame
# # pairwise linear regressions for each dataset
# df.corr = inner_join(df.corr, df.corr, c('class.index.bin' = 'class.index.bin')) %>%
# group_by(data.x, data.y) %>%
# nest() %>%
# mutate(model = purrr::map(data, ~lm(Mantel.cor.y ~ Mantel.cor.x, data=.))) %>%
# unnest(model %>% purrr::map(broom::glance)) %>%
# ungroup() %>%
# select(-data, -model) %>%
# mutate(r.squared_txt = round(r.squared, 2))
# # plotting
# ggplot(df.corr, aes(data.x, data.y, fill=r.squared)) +
# geom_tile() +
# geom_text(aes(label=r.squared_txt), color='white', size=4) +
# labs(title='Beta diversity correlogram') +
# scale_fill_gradient(low='black', high='red') +
# theme(
# text = element_text(size=16),
# axis.title.x = element_blank(),
# axis.title.y = element_blank()
# )
In [83]:
%%R -w 800 -h 650
corr.l = lapply(comm.l.rel, cal.corr, BDs=dist.BDs, binary=TRUE, method='jaccard')
par(mfrow=c(3,3))
for (n in names(corr.l)){
plot(corr.l[[n]])
title(n)
}