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BRC_growth_rate_microbiome.RMD
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BRC_growth_rate_microbiome.RMD
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---
title: "How fast does biological rock crust grow?"
subtitle: "Microbial community analysis"
date: "`r format(Sys.time(), '%d %B, %Y')`"
bibliography: references.bib
link-citations: yes
always_allow_html: true
output:
rmarkdown::github_document:
toc: true
toc_depth: 5
df_print: "kable"
keep_html: TRUE
---
```{r libraries, include=F}
library(extrafont) # Tools for using fonts, CRAN v0.17
library(svglite) # An 'SVG' Graphics Device, CRAN v1.2.3.2
library(ragg) # Graphic Devices Based on AGG, CRAN v0.3.1
library(tidyverse) # Easily Install and Load the 'Tidyverse', CRAN v1.3.0
library(magrittr) # A Forward-Pipe Operator for R, CRAN v1.5
library(scales) # Scale Functions for Visualization, CRAN v1.1.1
library(ggpomological) # Pomological plot themes for ggplot2, [github::gadenbuie/ggpomological] v0.1.2
library(cowplot) # Streamlined Plot Theme and Plot Annotations for 'ggplot2', CRAN v1.1.0
library(patchwork) # The Composer of Plots, CRAN v1.0.1 # The Composer of Plots, CRAN v1.0.1
library(kableExtra) # Construct Complex Table with 'kable' and Pipe Syntax, CRAN v1.2.1
library(see) # Visualisation Toolbox for 'easystats' and Extra Geoms, Themes and Color Palettes for 'ggplot2', CRAN v0.6.0
library(phyloseq) # Handling and analysis of high-throughput microbiome census data, Bioconductor v1.32.0
library(speedyseq) # psmelt
library(vegan) # Community Ecology Package, CRAN v2.5-6
library(BiodiversityR) # Package for Community Ecology and Suitability Analysis, CRAN v2.12-1
# library(agricolae)
library(car) # Companion to Applied Regression, CRAN v3.0-9
library(userfriendlyscience) # Quantitative Analysis Made Accessible, CRAN v0.7.2
library(emmeans) # Estimated Marginal Means, aka Least-Squares Means, CRAN v1.5.1
library(multcomp) # Simultaneous Inference in General Parametric Models, CRAN v1.4-14
library(ALDEx2) # Analysis Of Differential Abundance Taking Sample Variation Into Account, Bioconductor v1.20.0
library(corncob) # Count Regression for Correlated Observations with the Beta-Binomial, [github::bryandmartin/corncob] v0.1.0
library(ggrepel) # Automatically Position Non-Overlapping Text Labels with 'ggplot2', CRAN v0.8.2
```
```{r style settings, include=F}
options(width = 90, knitr.table.format = "html")
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
cache = TRUE,
dev = c("svglite", "png"),
dev.args = list(svglite = list(bg = 'white'), png = list(bg = 'white')),
dpi = 300,
# fig.width = 12,
# fig.height = 8,
cache.path = "BRC_growth_rate_cache/",
fig.path = "BRC_growth_rate_figures/"
)
f_name <- "DejaVu Sans" #sub("\\s//", "", f_name)
f_size <- 12
font_import(pattern = "DejaVuSans\\.", prompt = FALSE)
loadfonts() # registers fonts
theme_set(theme_bw(base_size = f_size,
base_family = f_name
))
```
```{r functions, include=FALSE}
PlotLibDist <- function(Ps_obj, x = "Replicate", fill = "Rock.type", facet = "Location") {
Library.size <- rowSums(otu_table(Ps_obj))
ggplot(sample_data(Ps_obj),
aes(x = !!sym(x), y = Library.size, fill = !!sym(fill))) +
geom_bar(stat = "identity",
position = "dodge",
color = "black") +
scale_y_log10(
breaks = trans_breaks("log10", function(x)
10 ^ x),
labels = trans_format("log10", math_format(10 ^ .x))
) +
background_grid(major = "xy", minor = "none") +
scale_fill_pomological() +
facet_grid(get(facet) ~ .)
}
PlotReadHist <- function(OTUmat, b.width = 10) {
OTUmat %>%
t() %>%
as.tibble() %>%
gather(key = sample, value = abundance) %>%
ggplot(aes(abundance)) +
# geom_histogram(binwidth = 1000) +
geom_freqpoly(binwidth = b.width) +
scale_y_log10()
}
GMPR <- function (comm,
intersect.no = 4,
ct.min = 4) {
require(matrixStats)
# Computes the GMPR size factor
#
# Args:
# comm: a matrix of counts, row - features (OTUs, genes, etc) , column - sample
# intersect.no: the minimum number of shared features between sample pair, where the ratio is calculated
# ct.min: the minimum number of counts required to calculate ratios （Empirical study found ct.min=4 is suitable)
#
# Returns:
# a list that contains:
# gmpr： the GMPR size factors for all samples; Samples with distinct sets of features will be output as NA.
# nss: number of samples with significant sharing (> intersect.no) including itself
# mask counts < ct.min
comm[comm < ct.min] <- 0
if (is.null(colnames(comm))) {
colnames(comm) <- paste0('S', 1:ncol(comm))
}
cat('Begin GMPR size factor calculation ...\n')
comm.no <- numeric(ncol(comm))
gmpr <- sapply(1:ncol(comm), function(i) {
if (i %% 50 == 0) {
cat(i, '\n')
}
x <- comm[, i]
# Compute the pairwise ratio
pr <- x / comm
# Handling of the NA, NaN, Inf
pr[is.nan(pr) | !is.finite(pr) | pr == 0] <- NA
# Counting the number of non-NA, NaN, Inf
incl.no <- colSums(!is.na(pr))
# Calculate the median of PR
pr.median <- colMedians(pr, na.rm = TRUE)
# Record the number of samples used for calculating the GMPR
comm.no[i] <<- sum(incl.no >= intersect.no)
# Geometric mean of PR median
if (comm.no[i] > 1) {
return(exp(mean(log(pr.median[incl.no >= intersect.no]))))
} else {
return(NA)
}
})
if (sum(is.na(gmpr))) {
warning(
paste0(
'The following samples\n ',
paste(colnames(comm)[is.na(gmpr)], collapse = '\n'),
'\ndo not share at least ',
intersect.no,
' common taxa with the rest samples! ',
'For these samples, their size factors are set to be NA! \n',
'You may consider removing these samples since they are potentially outliers or negative controls!\n',
'You may also consider decreasing the minimum number of intersecting taxa and rerun the procedure!\n'
)
)
}
cat('Completed!\n')
cat(
'Please watch for the samples with limited sharing with other samples based on NSS! They may be outliers! \n'
)
names(gmpr) <- names(comm.no) <- colnames(comm)
return(list(gmpr = gmpr, nss = comm.no))
}
PlotLmResid <- function(lm.df, which = c(1:6), mfrow = c(3, 2)){
require(grid)
if (length(levels(as.factor(lm.df$.fitted))) < 10) {# if number of unique x values is <10 just draw a line through the means
smoother <- stat_summary(fun = mean, colour = "red", geom = "line")
} else smoother <- stat_smooth(method = "loess", geom = "smooth", se = FALSE, colour = "firebrick", size = 1)
# residuals vs fitted
g1 <- ggplot(lm.df, aes(.fitted, .resid)) +
geom_point2() +
smoother +
geom_hline(yintercept = 0, linetype = 2, size = .2) +
scale_x_continuous("Fitted Values") +
scale_y_continuous("Residual") +
labs(title = "Residuals vs Fitted")
# normal qq
a <- quantile(lm.df$.stdresid, c(0.25, 0.75), na.rm = TRUE)
b <- qnorm(c(0.25, 0.75))
slope <- diff(a)/diff(b)
int <- a[1] - slope * b[1]
g2 <- ggplot(lm.df, aes(sample = .stdresid)) +
stat_qq() +
geom_abline(slope = slope, intercept = int, colour = "firebrick", size = 1) +
scale_x_continuous("Theoretical Quantiles") +
scale_y_continuous("Standardized Quantiles") +
labs(title = "Normal Q-Q")
# scale-location
g3 <- ggplot(lm.df, aes(.fitted, sqrt(abs(.stdresid)))) +
geom_point2() +
smoother +
scale_x_continuous("Fitted Values") +
scale_y_continuous("Root of Standardized Residuals") +
labs(title = "Scale-Location")
# residuals vs leverage
g4 <- ggplot(lm.df, aes(factors, .stdresid)) +
geom_point2() +
smoother +
geom_hline(yintercept = 0, linetype = 2, size = .2) +
scale_x_continuous("Factor Level Combinations") +
scale_y_continuous("Standardized Residuals") +
labs(title = "Residuals vs Factor Levels")
# # cook's distance
# g5 <- ggplot(lm.df, aes(rows, .cooksd, ymin=0, ymax=.cooksd)) +
# geom_point2() + geom_linerange() +
# scale_x_continuous("Observation Number") +
# scale_y_continuous("Cook's distance") +
# labs(title="Cook's Distance")
# cooksd vs leverage
g5 <- ggplot(lm.df, aes(factors, .cooksd)) +
geom_point2() +
smoother +
scale_x_continuous("Factor Level Combinations") +
scale_y_continuous("Cook's distance") +
labs(title = "Cook's dist vs Leverage")
bw <- diff(range(lm.df$.resid)) / (2 * IQR(lm.df$.resid) / length(lm.df$.resid) ^ (1/3))
sshist <- function(x){ # optimise bins
# 2006 Author Hideaki Shimazaki
# Department of Physics, Kyoto University
# shimazaki at ton.scphys.kyoto-u.ac.jp
N <- 2 : 100
C <- numeric(length(N))
D <- C
for (i in 1:length(N)) {
D[i] <- diff(range(x)) / N[i]
edges = seq(min(x), max(x), length=N[i])
hp <- hist(x, breaks = edges, plot=FALSE)
ki <- hp$counts
k <- mean(ki)
v <- sum((ki-k) ^ 2) / N[i]
C[i] <- (2 * k-v) / D[i] ^ 2 #Cost Function
}
idx <- which.min(C)
optD <- D[idx]
bins <- seq(min(x), max(x), length=N[idx])
# h = hist(x, breaks = bins)
# rug(x)
return(bins)
}
bins <- sshist(lm.df$.resid)
g6 <- ggplot(lm.df, aes(.resid)) +
geom_histogram(breaks = bins)
plots <- list(g1, g2, g3, g4, g5, g6)
# making the plots
grid.newpage()
if (prod(mfrow) > 1) {
mypos <- expand.grid(1:mfrow[1], 1:mfrow[2])
mypos <- mypos[with(mypos, order(Var1)), ]
pushViewport(viewport(layout = grid.layout(mfrow[1], mfrow[2])))
formatter <- function(.){}
} else {
mypos <- data.frame(matrix(1, length(which), 2))
pushViewport(viewport(layout = grid.layout(1, 1)))
formatter <- function(.) {
.dontcare <- readline("Hit <Return> to see next plot: ")
grid.newpage()
}
}
j <- 1
for (i in which) {
formatter()
print(plots[[i]], vp = viewport(layout.pos.row = mypos[j, ][1], layout.pos.col = mypos[j, ][2]))
j <- j + 1
}
}
TestAlphaV3 <- function(data2test = Richness_Diversity_long_sub,
response_name = "Estimate",
factor_names = c("Location", "Rock.type"),
boxcox.trans = FALSE) {
require(dplyr)
mod_lm <-
aov(as.formula(paste(
response_name,
paste(factor_names[1], factor_names[2], sep = " * "),
sep = " ~ "
)), data2test)
# }
if (boxcox.trans) {
# employ boxcox transformation then recalculate model
print("Performing Box-Cox transformation of the data")
lambdas <- boxcox(as.formula(paste(
response_name,
paste(factor_names[1], factor_names[2], sep = " * "),
sep = " ~ "
)),
data = data2test,
lambda = seq(0, 1.0, 0.01))
print(range(lambdas$x[lambdas$y > max(lambdas$y) - qchisq(0.95, 1) /
2]))
print(l.max <- lambdas$x[which.max(lambdas$y)])
if (l.max == 0)
l.max <- 1
data2test$Estimate.box <-
(data2test$Estimate ^ l.max - 1) / l.max
mod_lm <-
aov(as.formula(paste(
"Estimate.box",
paste(factor_names[1], factor_names[2], sep = " * "),
sep = " ~ "
)), data2test)
}
if (exists("mod_lm")) {
print(mod_lm)
mod_df <- fortify(mod_lm)
factor.combinations <-
as.numeric(factor(paste(mod_df[, factor_names[1]], mod_df[, factor_names[2]]),
levels = unique(as.character(
paste(mod_df[, factor_names[1]], mod_df[, factor_names[2]])
)))) # needed for "residuals vs leverage
mod_df <-
cbind(mod_df,
rows = 1:nrow(mod_df),
factors = factor.combinations)
PlotLmResid(mod_df)
if ((data2test %>% group_by(!!sym(factor_names[1]),!! sym(factor_names[2])) %>% dplyr::count() %>% pull(n) %>% n_distinct() == 1)) {
print("Equal group sizes - showing SS type I")
print(summary(mod_lm)) # display Type I ANOVA table
} else {
print("Unequal group sizes - showing SS type III")
options(contrasts = c("contr.sum", "contr.poly"))
print(Anova(mod_lm, type = "III")) # type III SS
}
print(model.tables(mod_lm, "means"), digits = 3) # Show the means
return(mod_lm)
}
}
PairwiseAdonis <- function(x, factors, sim.function = "vegdist", sim.method = "horn",
p.adjust.m = "BH", reduce = NULL)
{
# Taken from: https://github.com/pmartinezarbizu/pairwiseAdonis
co <- combn(unique(as.character(factors)), 2)
pairs <- c()
total.DF <- c()
F.Model <- c()
R2 <- c()
p.value <- c()
for (elem in 1:ncol(co)) {
if (sim.function == "daisy") {
x1 = cluster::daisy(x[factors %in% c(co[1, elem], co[2, elem]),
], metric = sim.method)
}
else {
x1 = vegdist(x[factors %in% c(co[1, elem], co[2,
elem]), ], method = sim.method)
}
ad <- adonis(x1 ~ factors[factors %in% c(co[1, elem],
co[2, elem])], permutations = 999)
pairs <- c(pairs, paste(co[1, elem], "-", co[2, elem]))
total.DF <- c(total.DF, ad$aov.tab["Total", 1])
F.Model <- c(F.Model, ad$aov.tab[1, 4])
R2 <- c(R2, ad$aov.tab[1, 5])
p.value <- c(p.value, ad$aov.tab[1, 6])
}
p.adjusted <- p.adjust(p.value, method = p.adjust.m)
sig = c(rep("", length(p.adjusted)))
sig[p.adjusted <= 0.05] <- "."
sig[p.adjusted <= 0.01] <- "*"
sig[p.adjusted <= 0.001] <- "**"
sig[p.adjusted <= 1e-04] <- "***"
pairw.res <- data.frame(pairs, total.DF, F.Model, R2, p.value,
p.adjusted, sig)
if (!is.null(reduce)) {
pairw.res <- subset(pairw.res, grepl(reduce, pairs))
pairw.res$p.adjusted <- p.adjust(pairw.res$p.value, method = p.adjust.m)
sig = c(rep("", length(pairw.res$p.adjusted)))
sig[pairw.res$p.adjusted <= 0.05] <- "."
sig[pairw.res$p.adjusted <= 0.01] <- "*"
sig[pairw.res$p.adjusted <= 0.001] <- "**"
sig[pairw.res$p.adjusted <= 1e-04] <- "***"
pairw.res <- data.frame(pairw.res[, 1:5], sig)
}
class(pairw.res) <- c("pwadonis", "data.frame")
return(pairw.res)
}
STAMPR2 <- function(physeq_obj,
tax_rank = "Phylum",
vars2test = c("Location", "Rock.type"),
threshold = 0.005,
outputfile = "STAMPR_output") {
# run a STAMP-like analysis: compare relative abundance differences using two-way analysis, then run a post-hoc test and correct for multiple comparison. This is an extended and updated version for Parks, D. H., Tyson, G. W., Hugenholtz, P., and Beiko, R. G. (2014). STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. doi:10.1093/bioinformatics/btu494.)
# The test is meant as a follow-up test for a pair-wise ADONIS and use the significant test pairs
# The global analysis is done using the Aligned Rank Transformed ANOVA test. This is a "modern" non-parametric test that should be more robust than the Kruskal-Wallis Rank Sum Test or the Scheirer Ray Hare.
# The post-hoc test is an Estimated marginal means test on each significant pair.
# If two variables are provided only the interactions are computed (for the main effects run it separately on each variable)
# TODO:
require("emmeans") # Estimated marginal means (Least-squares means))
require("ARTool") # Aligned Rank Transform
require("multcomp") # Simultaneous Inference in General Parametric Models
if (length(vars2test) != 1 &
length(vars2test) != 2)
{stop('This function only suppurts 1 or two independent variables')}
test_expression <-
as.formula(paste("Abundance", paste(vars2test, collapse = " * "), sep = " ~ "))
physeq_rel <-
transform_sample_counts(physeq_obj, function(x)
x / sum(x)) # convert to relative abundance
physeq_glom_rel <- tax_glom(physeq_rel,
tax_rank,
NArm = TRUE) # agglomerate taxa using taxonomy
# Test only abundant taxa: group dataframe by rank, calculate total rel. abundance per phylum and keep only taxa above threshold
physeq_glom_rel %>%
psmelt(.) %>%
group_by(!!sym(tax_rank)) %>%
summarise(tot_abundance = sum(Abundance)) %>%
filter(tot_abundance >= threshold) %>%
pull(1) %>%
as.character() ->
Taxa2test
physeq_glom_rel_abund <- # remove rare taxa
prune_taxa(tax_table(physeq_glom_rel)[, tax_rank] %in% Taxa2test, physeq_glom_rel)
if (length(vars2test) == 1) {# One-way test
cmb <- combn(unique(as.character(get_variable(physeq_glom_rel_abund, vars2test))), 2)
as_tibble(matrix( # prepare results table (P and Eta)
NA,
nrow = ntaxa(physeq_glom_rel_abund),
ncol = ncol(cmb) + 2,
), .name_repair = "minimal") %>%
bind_cols(Phylum = as(tax_table(physeq_glom_rel_abund)[, tax_rank], "vector"), .) %>%
set_names(c(tax_rank,
paste(vars2test, "- P"),
paste(vars2test, "- EtaSq"),
seq(ncol(cmb)))) %>%
mutate_if(., is.logical, as.numeric) ->
taxa_test_results
as_tibble(matrix( # prepare estimates table
NA,
nrow = ntaxa(physeq_glom_rel_abund),
ncol = (ncol(cmb) * 5)
),
.name_repair = "minimal") %>%
bind_cols(Phylum = as(tax_table(physeq_glom_rel_abund)[, tax_rank], "vector"), .) %>%
mutate_if(., is.logical, as.numeric) ->
taxa_test_estimates
colnames(taxa_test_estimates) <- # cannot use set_names with non-unique names
c(tax_rank, c(rbind(
cmb, matrix(rep(
c("Estimate diff.", "low CI", "high CI"), ncol(cmb)
), ncol = ncol(cmb))
))) # this is ugly but it works well, basically c() flattens a matrix
for (phy_id in seq(ntaxa(physeq_glom_rel_abund))) {
data2test <-
bind_cols(Abundance = as.numeric(otu_table(physeq_glom_rel_abund)[, phy_id] * 100),
as(sample_data(physeq_glom_rel_abund), "data.frame"))
print(tax_table(physeq_glom_rel_abund)[phy_id, tax_rank])
art_mod <- art(test_expression, data = data2test)
print(mod_summary <- anova(art_mod))
taxa_test_results[phy_id, c(2)] <- mod_summary$`Pr(>F)` # p values
mod_summary %>%
transmute(`Part Eta Sq` = `Sum Sq`/(`Sum Sq` + `Sum Sq.res`)) ->
taxa_test_results[phy_id, c(3)] # EtaSq (effect size)
art_mod_lm <- artlm(art_mod, vars2test) # build a linear model for ART data
# Compute estimated marginal means (EMMs)
(emm_mod <- emmeans(art_mod_lm, as.formula(paste("", vars2test, sep = " ~ ")), weights = "equal"))
# Contrasts, pairwise comparisons, tests, and confidence intervals.
(emm_mod_cons <- contrast(emm_mod, method = "pairwise", adjust = "tukey"))
# (emm_mod_CI <- confint(emm_mod_cons, adjust = "fdr", level = 0.95, type = "response"))
# (emm_mod_CLD <- multcomp::cld(emm_mod, alpha = 0.05, Letters = letters, adjust = "tukey"))
print("Note that the estimates aren’t as in a linear model because they are on the scale of ranks and not the data, but the p-values are useful")
# assign pairwise results to table
colnames(taxa_test_results)[seq(4, 3 + ncol(cmb))] <- summary(emm_mod_cons)$contrast
taxa_test_results[phy_id, seq(4, 3 + ncol(cmb))] <-
as.list(summary(emm_mod_cons)$p.value)
# assign estimates to table
mod_lm <- lm(test_expression, data = data2test) # because we want the real means not the rank means!
emm_mod_lm <- emmeans(mod_lm, as.formula(paste("", vars2test, sep = " ~ ")), weights = "equal")
emm_mod_lm_cons <- contrast(emm_mod, method = "pairwise", adjust = "tukey")
emm_mod_lm_CI <- confint(emm_mod_lm_cons, adjust = "fdr", level = 0.95, type = "response")
taxa_test_estimates[phy_id, c(2,3)] <- as.list(summary(emm_mod_lm)$emmean)
taxa_test_estimates[phy_id, 4] <- emm_mod_lm_CI$estimate
taxa_test_estimates[phy_id, 5] <- as.list(emm_mod_lm_CI$lower.CL)
taxa_test_estimates[phy_id, 6] <- as.list(emm_mod_lm_CI$upper.CL)
# # assign pairwise means and CI to table
# # I use wilcox.test here to get estimates on the moments and CI of the data not the ranks and p-value
# for (pair in seq(length(sig_pairs[[1]]))) {
# pair2test <- unlist(str_split(sig_pairs[[1]][pair], " - "))
# possibleError <- tryCatch(
# wilcox_mod <- wilcox.test(
# test_expression,
# data = data2test,
# subset = do.call("%in%", list(get(vars2test), pair2test)), # grab only pair2test from vars2test
# conf.int = TRUE,
# exact = FALSE
# ),
# error = function(e)
# e
# ) # AKA Mann Whitney
# if (inherits(possibleError, 'error')) {
# print(possibleError)
# taxa_test_results[phy_id, pair + 3] <- NA
# } else {
# print(wilcox_mod)
# taxa_test_estimates[phy_id, (pair - 1) * 5 + 2] <-
# mean(data2test[unlist(data2test[vars2test]) %in% pair2test[1],]$Abundance)
# taxa_test_estimates[phy_id, (pair - 1) * 5 + 3] <-
# mean(data2test[unlist(data2test[vars2test]) %in% pair2test[2],]$Abundance)
# taxa_test_estimates[phy_id, (pair - 1) * 5 + 4] <-
# wilcox_mod$estimate
# taxa_test_estimates[phy_id, (pair - 1) * 5 + c(5, 6)] <-
# as.list(wilcox_mod$conf.int[c(1, 2)])
# }
# }
}
} else {# Two-way test
cmb <- combn(unique(c(as.character(get_variable(physeq_glom_rel_abund, vars2test[1])), as.character(get_variable(physeq_glom_rel_abund, vars2test[2])))), 2) # just for setting the right number of columns
sample_data(physeq_glom_rel_abund)$Vars_combination <- with(sample_data(physeq_glom_rel_abund), get(vars2test[1]):get(vars2test[2]))
as_tibble(matrix(
NA,
nrow = ntaxa(physeq_glom_rel_abund),
ncol = sum(ncol(cmb)) + 6, # sig_pairs is taken from pairwise adonis. In addition the factors are tested alone.
),
.name_repair = "minimal") %>%
bind_cols(Phylum = as(tax_table(physeq_glom_rel_abund)[, tax_rank], "vector"), .) %>%
set_names(c(tax_rank,
paste(vars2test[1], "- P"),
paste(vars2test[1], "- EtaSq"),
paste(vars2test[2], "- P"),
paste(vars2test[2], "- EtaSq"),
paste(paste(vars2test, collapse = " X "), "- P"),
paste(paste(vars2test, collapse = " X "), "- EtaSq"),
seq(ncol(cmb)))) %>%
mutate_if(., is.logical, as.numeric) ->
taxa_test_results # prepare results table (P and Eta)
as_tibble(matrix(
NA,
nrow = ntaxa(physeq_glom_rel_abund),
ncol = (ncol(cmb) * 5)
),
.name_repair = "minimal") %>%
bind_cols(Phylum = as(tax_table(physeq_glom_rel_abund)[, tax_rank], "vector"), .) %>%
mutate_if(., is.logical, as.numeric) ->
taxa_test_estimates # prepare estimates table
colnames(taxa_test_estimates) <- # cannot use set_names with non-unique names
c(tax_rank, c(rbind(
cmb, matrix(rep(
c("Estimate diff.", "low CI", "high CI"), ncol(cmb)
), ncol = ncol(cmb))
))) # this is ugly but it works well, basically c() flattens a matrix
for (phy_id in seq(ntaxa(physeq_glom_rel_abund))) {
bind_cols(Abundance = as.numeric(otu_table(physeq_glom_rel_abund)[, phy_id] * 100), as(sample_data(physeq_glom_rel_abund), "data.frame")) %>%
mutate_at(., vars2test[1],as.factor) %>%
mutate_at(., vars2test[2],as.factor) %>%
mutate_at(., "Vars_combination", as.factor) ->
data2test
print(tax_table(physeq_glom_rel_abund)[phy_id, tax_rank])
art_mod <- art(test_expression, data = data2test)
print(mod_summary <- anova(art_mod))
taxa_test_results[phy_id, c(2, 4, 6)] <-
as.list(mod_summary$`Pr(>F)`) # p values
mod_summary %>%
transmute(`Part Eta Sq` = `Sum Sq`/(`Sum Sq` + `Sum Sq.res`)) %>%
t() %>%
as.list() ->
taxa_test_results[phy_id, c(3, 5, 7)] # EtaSq (effect size)
# Post hoc of the main effects
posthoc_expression <- as.formula(paste("Abundance", "Vars_combination", sep = " ~ "))
art_mod_lm <- artlm(art(posthoc_expression, data = data2test), "Vars_combination") # build a linear model for ART data (This only works on combined factors; see: https://cran.r-project.org/web/packages/ARTool/vignettes/art-contrasts.html). Also, this might not be generalisable for more factors than two!!
# Compute estimated marginal means (EMMs)
(emm_mod <- emmeans(art_mod_lm, as.formula(paste("~", "Vars_combination"))))
# Contrasts, pairwise comparisons, tests, and confidence intervals.
(emm_mod_cons <- contrast(emm_mod, method = "pairwise", adjust = "tukey"))
# (emm_mod_CI <- confint(emm_mod_cons, adjust = "tukey", level = 0.95, type = "response"))
# (emm_mod_CLD <- cld(emm_mod, alpha = 0.05, Letters = letters, adjust = "tukey"))
print("Note that the estimates aren’t as in a linear model because they are on the scale of ranks and not the data, but the p-values are useful")
# assign pairwise results to table
colnames(taxa_test_results)[seq(8, 7 + ncol(cmb))] <- summary(emm_mod_cons)$contrast
taxa_test_results[phy_id, seq(8, 7 + length(summary(emm_mod_cons)$p.value))] <-
as.list(summary(emm_mod_cons)$p.value) # need to check!! (trouble is it outputs everything and not just significant ones)
# assign estimates to table
mod_lm <- lm(test_expression, data = data2test) # because we want the real means not the rank means!
emm_mod_lm <- emmeans(mod_lm, as.formula(paste("~", vars2test[1], "*",vars2test[2])), weights = "equal")
# emm_mod_lm <- emmeans(mod_lm, as.formula(paste("~", "Vars_combination")), weights = "equal")
emm_mod_lm_cons <- contrast(emm_mod, method = "pairwise", adjust = "tukey")
emm_mod_lm_CI <- confint(emm_mod_lm_cons, adjust = "fdr", level = 0.95, type = "response")
colnames(taxa_test_estimates)[seq(2, ncol(taxa_test_estimates), by = 5)] <-
str_split(emm_mod_lm_CI$contrast, " - ", simplify = TRUE)[, 1]
colnames(taxa_test_estimates)[seq(3, ncol(taxa_test_estimates), by = 5)] <-
str_split(emm_mod_lm_CI$contrast, " - ", simplify = TRUE)[, 2]
taxa_test_estimates[phy_id, -1] <- as.list(summary(emm_mod_lm)$emmean[match(colnames(taxa_test_estimates), with(summary(emm_mod_lm), get(vars2test[1]):get(vars2test[2])))])[-1]
taxa_test_estimates[phy_id, seq(4, ncol(taxa_test_estimates), by = 5)] <- as.list(emm_mod_lm_CI$estimate)
taxa_test_estimates[phy_id, seq(5, ncol(taxa_test_estimates), by = 5)] <- as.list(emm_mod_lm_CI$lower.CL)
taxa_test_estimates[phy_id, seq(6, ncol(taxa_test_estimates), by = 5)] <- as.list(emm_mod_lm_CI$upper.CL)
}
}
# Correct for FDR for each comparison pair (not needed if adjust has been used in contrast())
# for (ps2correct in seq(2, ncol(taxa_test_results), by = 2)) {
# # print(pair)
# taxa_test_results[, ps2correct] <-
# p.adjust(pull(taxa_test_results[, ps2correct]) , method = "BY") # Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165–1188.
# # calculated by replacing the alpha of the Benjamini-Hochberg procedure by alpha/sum(1/1:m)).
# # we use this and not BH because of dependency in the tests (compositional data)
# }
#
write.csv(taxa_test_results, file = paste0("./Results/", outputfile, "_", tax_rank, "_Pvals.csv"))
write.csv(taxa_test_estimates, file = paste0("./Results/", outputfile, "_", tax_rank, "_CI.csv"))
Taxa_tests <- list(taxa_test_results, taxa_test_estimates)
return(Taxa_tests)
}
plotSTAMPR <- function(STAMPR_output = Taxa_tests_phylum1, pair = "City - Slope", tax_level = "Phylum", f_size = 14){
require(ggthemes)
require(ggpomological)
if (!is.list(STAMPR_output) | length(STAMPR_output) != 2) {print("The function accepts only lists with two elements")}
pair_number <- which(colnames(STAMPR_output[[1]]) == pair) - 3
pvals_col <- pair_number + 3
stats_col <- seq(from = 2, to = (ncol(STAMPR_output[[1]]) - 3) * 5, by = 5)[pair_number - 4]
bind_cols(STAMPR_output[[1]][, 1],
STAMPR_output[[1]][, pair] ,
STAMPR_output[[2]][, stats_col:(stats_col + 4)]
) %>%
mutate(Higher = if_else(.[3] > .[4], colnames(.)[3], colnames(.)[4])) %>%
gather("Factor", `Mean abundance (%)`, 3:4) %>%
mutate_at(c(tax_level, "Higher", "Factor"), ~fct_rev(.)) -> # fct_rev because coord_flip() reverses the order
STAMPR_df
tibble(
min = seq(
from = 0.5,
to = max(as.numeric(pull(STAMPR_df, tax_level))),
by = 1
),
max = seq(
from = 1.5,
to = max(as.numeric(pull(STAMPR_df, tax_level))) + 0.5,
by = 1
)) %>%
add_column(Shade = rep(c(0, 1), length.out = nrow(.))) %>%
mutate_at("Shade", ~as.factor(.)) ->
# %>% slice(rep(1:n(), each = 2))
# mutate(col = ifelse(allyrs == TRUE, 1, 0))
#
# mutate_at("min", ~case_when(col == 1 ~ . - 0.3,
# TRUE ~ .)) %>%
# mutate_at("max", ~case_when(col == 1 ~ . - 0.3,
# TRUE ~ .)) ->
shading
# ggthemr("grape")
p1 <- ggplot() +
geom_rect(data = shading,
aes(xmin = min, xmax = max, ymin = -Inf, ymax = Inf,
fill = Shade, alpha = 0.1), fill = rep(c("white", "#E9EDED"), length.out = nrow(shading)), show.legend = F) +
geom_col(data = STAMPR_df,
mapping = aes(!! sym(tax_level), y = `Mean abundance (%)`, fill = Factor),
width = 0.8,
position = position_dodge(),
alpha = 2/3) +
scale_y_continuous(expand = c(0, 0)) +
scale_x_discrete(expand = c(0, 0)) +
coord_flip() +
geom_rangeframe(data = STAMPR_df, aes(!! sym(tax_level), y = `Mean abundance (%)`), sides = "b") +
scale_fill_manual(values = Gradient.colours[c(6, 5)]) +
theme_tufte(base_size = f_size, base_family = "sans") +
theme(legend.position = "top",
legend.justification = 'left',
legend.title = element_blank()) +
guides(fill = guide_legend(reverse = TRUE))
# panel.grid.major.x = element_line(colour = "white"),
# panel.ontop = TRUE)
p2 <- ggplot() +
geom_rect(
data = shading,
aes(
xmin = min,
xmax = max,
ymin = -Inf,
ymax = Inf,
fill = Shade,
alpha = 0.1
),
fill = rep(c("white", "#E9EDED"), length.out = nrow(shading)),
show.legend = F
) +
geom_hline(yintercept = 0, linetype = "dashed", color = "slategray", size = 1, alpha = 2/3) +
geom_errorbar(data = STAMPR_df,
aes(
!! sym(tax_level),
ymin = `low CI`,
ymax = `high CI`,
colour = Higher),
width = 0.3,
alpha = 2/3
) +
geom_point2(data = STAMPR_df,
aes(!! sym(tax_level),
y = `Estimate diff.`,
colour = Higher),
size = 4,
alpha = 2/3) +
scale_y_continuous(expand = c(0, 0)) +
scale_x_discrete(
expand = c(0, 0),
breaks = STAMPR_df[[tax_level]],
labels = formatC(deframe(STAMPR_df[, 2]), format = "e", digits = 2),
position = "top"
) +
geom_rangeframe(data = STAMPR_df, aes(!! sym(tax_level), y = seq(
min(`low CI`),
max(`high CI`),
length.out = nrow(STAMPR_df)
)), sides = "b") +
scale_colour_manual(values = Gradient.colours[c(6, 5)]) +
theme_tufte(base_size = f_size, base_family = "sans") +
theme(legend.position = "none",
plot.title = element_text(margin = margin(10, 0, 18, 0), hjust = 0.5),
axis.title.y = element_text(margin = margin(t = 0, r = 0, b = 20, l = 0), angle = 180)) + # BUG: no response
labs(title = "95% confidence intervals",
y = "Difference in mean abundance (%)",
x = "p-value (corrected)") +
coord_flip()
# p1 + p2 + plot_layout(widths = c(1, 2)) # doesn't look as good
plot_grid(p1, p2, rel_widths = c(2, 3)) + theme(plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm"))
# ggthemr_reset()
}
GGPlotCorncob <- function(da_analysis_df, OTU_labels = FALSE, Taxa = "Phylum", Y_val = "Differential abundance", sig_level = 0.05, Rank = Taxa_rank) {
# Plot differential abundance model results
require(magrittr)
require(ggplot2)
require(ggrepel)
require(see)
pos <- position_jitter(width = 0.1, seed = 1)
da_analysis_df %<>%
mutate_at(vars(matches(Taxa)), as_factor) %>%
mutate(!!Taxa := fct_relevel(get(Taxa), pull(Rank, !!Taxa))) %>% # Taxa_rank is calcuted for the taxa box plots
mutate(!!Taxa := fct_relevel(get(Taxa), "Rare", after = Inf))
corncob_summary <- tibble(Label = c(paste0("⬆", sum(da_analysis_df$`Differential abundance` > 0 & da_analysis_df$Significance == "Pass"), " ⬇", sum(da_analysis_df$`Differential abundance` < 0 & da_analysis_df$Significance == "Pass"), " (", nrow(da_analysis_df), ")")))
p <-
ggplot(da_analysis_df) +
geom_point2(aes(
x = !!sym(Taxa),
y = !!sym(Y_val),
colour = !!sym("Significance"),
size = !!sym("Mean abundance (%)")),
position = pos,
alpha = 2 / 3,
stroke = 0) +
geom_linerange(aes(x = !!sym(Taxa),
y = !!sym(Y_val),
ymin = `ymin`,
ymax = `ymax`,
colour = !!sym("Significance")),
position = pos,
alpha = 1/5,
show.legend = FALSE) +
geom_text(
data = corncob_summary,
mapping = aes(x = Inf, y = Inf, label = Label),
hjust = 1.1,
vjust = 1.6
) +
xlab("") +
ylab("Differential abundance") +
labs(colour = paste("Significance at \n p <", sig_level), size = "Mean abundance (%)") +
theme_grey(base_size = 18) +
theme(axis.text.x = element_text(angle = 45.0, vjust = 1, hjust = 1),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()) +
guides(colour = guide_legend(override.aes = list(size = 5))) +
scale_colour_manual(values = c(ggpomological:::pomological_base[[7]], ggpomological:::pomological_palette[[1]])) +
scale_size_continuous(name = "Mean abundance (%)",
range = c(2, 8),
breaks = c(round(seq(min(da_analysis_df$`Mean abundance (%)`), max(da_analysis_df$`Mean abundance (%)`), length.out = 5), 1)))
if (OTU_labels) {
p <- p + geom_label_repel(
aes(x = !!sym(Taxa), y = !!sym(Y_val)),
size = 6,
label = sub("Seq_([0-9]+)", "\\1", pull(da_analysis_df[da_analysis_df$Significance == "Pass", ], "OTU")),
position = pos,
data = da_analysis_df[da_analysis_df$Significance == "Pass", ],
# nudge_x = 0.4,
colour = "#4a4a4a",
label.size = NA,
alpha = 0.75,
# fontface = 'bold',
box.padding = 0.80,
point.padding = 0.5
)
}
return(p)
}
gz <- function (in_path, out_path = tempfile())
{
# Compress a file using gz and delete the uncompressed file
out <- gzfile(out_path, "w")
writeLines(readLines(in_path), out)
close(out)
file.remove(in_path)
invisible(out_path)
}
```
### Setting general parameters:
```{r general parameters}
set.seed(15102020)
bootstraps <- 1000
data_path <- "./Data/"
Proj_name <- "BRC_growth_rate"
Browns <- RColorBrewer::brewer.pal(n = 9, "YlOrBr")[9:6]
Greens <- RColorBrewer::brewer.pal(n = 9, "YlGn")[9:6]
Blues <- RColorBrewer::brewer.pal(n = 9, "YlGnBu")[9:6]
Gradient.colours <- c(Browns[1], Greens[1], Browns[2], Greens[2], Browns[3], Greens[3], Browns[4], Greens[4], Blues)
```
## Description
This script reproduces all sequence analysis steps and plots included in the paper plus some additional exploratory analyses.
### Load data
```{r load data, cache=TRUE}
OTUmat <- t(read.csv(paste0(data_path, "Shivta_site_otuTab2.txt"), header = TRUE, row.names = 1))
sort.order <- as.numeric(gsub("OTU([0-9]+)", "\\1", colnames( OTUmat )))
OTUmat <- OTUmat[, order(sort.order )]
Metadata <- read.csv(paste0(data_path, "Shivta_metadata.csv"), row.names = 1, header = TRUE)
read_csv(paste0(data_path, "Shivta_metadata.csv"),
trim_ws = TRUE) %>%
mutate_at(
c(
"Rock.type",
"Location"
),
~(factor(.))
) %>%
column_to_rownames("Sample.code") ->
Metadata
row.names(OTUmat) <- gsub("(.*)Nimrod[0-9]+|Osnat[0-9]+", "\\1", row.names( OTUmat))
Metadata <- Metadata[order(row.names(Metadata)), ]
OTUmat <- OTUmat[order(row.names(OTUmat)), ]
# calculate sample size
Metadata$Library.size = rowSums(OTUmat)
Metadata$Location.rock <- with(Metadata, Location:Rock.type)
# Load taxonomy data
tax.file <- "Shivta_site_silva.nrv119.taxonomy"
Taxonomy <- read.table(paste0(data_path, tax.file), stringsAsFactors = FALSE) # read taxonomy file
# count how many ';' in each cell and add up to 6
for (i in 1:nrow(Taxonomy)){
semicolons <- length(gregexpr(";", Taxonomy$V2[i] )[[1]])
if (semicolons < 6){
x <- paste0( rep("Unclassified;", 6 - semicolons ), collapse = "")
Taxonomy$V2[i] <- paste0( Taxonomy$V2[i], x, sep = "")
}
}
# split taxonomy to columns
do.call( "rbind", strsplit( Taxonomy$V1, ";", fixed = TRUE)) %>%
gsub( "size=([0-9]+)", "\\1", .) %>%
data.frame( ., do.call( "rbind", strsplit( Taxonomy$V2, ";", fixed = TRUE)), stringsAsFactors = F) %>%
apply(., 2, function(x) gsub( "\\(.*\\)", "", x)) %>%
replace(., . == "unclassified", "Unclassified") ->
Taxonomy
colnames( Taxonomy ) <- c( "OTU", "Frequency", "Domain", "Phylum", "Class", "Order", "Family", "Genus" )
# rownames(Taxonomy) <- colnames(Rock_weathering_OTUmat)
rownames(Taxonomy) <- Taxonomy[, 1]
Tree_IQ <- read_tree(paste0(data_path, "Shivta_site_otuReps.filtered.align.treefile"))
# generate phyloseq object
Ps_obj <- phyloseq(otu_table(OTUmat, taxa_are_rows = FALSE),
tax_table(Taxonomy[, -c(1, 2)]),
sample_data(Metadata),
phy_tree(Tree_IQ)
)
# Reorder factors for plotting
sample_data(Ps_obj)$Location %<>% fct_relevel("Slope", "City")
```
Remove un- and mis-classified sequences, chloroplasts and mitochondria
```{r remove samples, cache=T}
domains2remove <- c("", "Archaea", "Eukaryota", "Unclassified")
classes2remove <- c("Chloroplast")
families2remove <- c("Mitochondria")
Ps_obj_filt <- subset_taxa(Ps_obj, !is.na(Phylum) &
!Domain %in% domains2remove &
!Class %in% classes2remove &
!Family %in% families2remove)
```
### Inspect library size and number of OTU
```{r Library Sizes, cache=T}
Ps_obj_df <-
as.data.frame(sample_data(Ps_obj_filt)) # Put sample_data into a ggplot-friendly data.frame
Ps_obj_df <- Ps_obj_df[order(Ps_obj_df$Library.size), ]
Ps_obj_df$Index <- seq(nrow(Ps_obj_df))
ggplot(data = Ps_obj_df,
aes(x = Index, y = Library.size, color = Location.rock)) +
geom_point2(size = 4) +
scale_colour_manual(values = ggpomological:::pomological_palette[c(2, 1, 9, 3)], name = "Location.rock")
summary(sample_sums(Ps_obj_filt))
summary(taxa_sums(Ps_obj_filt))
```
### Explore the prevalence of different taxa in the database
```{r explore prevalence, cache=T}
prevdf <- apply(X = otu_table(Ps_obj_filt),
MARGIN = ifelse(taxa_are_rows(Ps_obj_filt), yes = 1, no = 2),
FUN = function(x){sum(x > 0)})
# Add taxonomy and total read counts to this data.frame
prevdf <- data.frame(Prevalence = prevdf,
TotalAbundance = taxa_sums(Ps_obj_filt),
tax_table(Ps_obj_filt))
prevdf %>%
group_by(Phylum) %>%
summarise(`Mean prevalence` = mean(Prevalence),
`Sum prevalence` = sum(Prevalence)) ->
Prevalence_phylum_summary
Prevalence_phylum_summary %>%
kable(., digits = c(0, 1, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)
prevdf %>%
group_by(Order) %>%
summarise(`Mean prevalence` = mean(Prevalence),
`Sum prevalence` = sum(Prevalence)) ->
Prevalence_Order_summary
Prevalence_Order_summary %>%
kable(., digits = c(0, 1, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = F)
```
Based on that we will remove all phyla with a prevalence of under 8
```{r remove rare taxa, cache=T}
Prevalence_phylum_summary %>%
filter(`Sum prevalence` < 8) %>%
dplyr::select(Phylum) %>%
map(as.character) %>%
unlist() ->
filterPhyla
Ps_obj_filt %<>% subset_taxa(!Phylum %in% filterPhyla)
sample_data(Ps_obj_filt)$Library.size <- rowSums(otu_table(Ps_obj_filt))
print(Ps_obj)
print(Ps_obj_filt)
```
#### Plot general prevalence features of the phyla
```{r prevalence phylum, cahce=T, fig.height=12, fig.width=10}
# Subset to the remaining phyla