R/amp_venn.R
In MadsAlbertsen/ampvis2: Tools for visualising amplicon data
Defines functions
amp_venn
Documented in
amp_venn
#' Venn diagram of core OTUs
#'
#' Calculates the number of "core" OTUs shared by groups given thresholds for how frequent the OTUs should be above a certain abundance. Also returns the average abundance of the OTUs in a particular group.
#'
#' @param data (\emph{required}) Data list as loaded with \code{\link{amp_load}}.
#' @param group_by Group the data based on a sample metadata variable. \code{amp_venn} currently only supports a maximum of 3 different groups.
#' @param cut_a Abundance cutoff in percent. OTU's below this abundance are excluded from the analysis. (\emph{default:} \code{0.1})
#' @param cut_f Frequency cutoff in percent. OTU's within the top \code{cut_f} of the reads are considered a "core" OTU. (\emph{default:} \code{80})
#' @param text_size Size of the plotted text. (\emph{default:} \code{5})
#' @param normalise (\emph{logical}) Normalise the OTU read counts to be in percent per sample. (\emph{default:} \code{TRUE})
#' @param detailed_output (\emph{logical}) Return additional details or not. If \code{TRUE}, it is recommended to save to an object and then access the additional data by \code{View(object$data)}. (\emph{default:} \code{FALSE})
#'
#' @return A ggplot2 object.
#'
#' @export
#'
#' @section Preserving relative abundances in a subset of larger data:
#' See \code{?\link{amp_filter_samples}} or the \href{https://kasperskytte.github.io/ampvis2/articles/faq.html#preserving-relative-abundances-in-a-subset-of-larger-data}{ampvis2 FAQ}.
#'
#' @import ggplot2
#' @importFrom dplyr group_by mutate summarise if_else n
#' @importFrom tidyr gather spread
#'
#' @seealso
#' \code{\link{amp_load}}
#'
#' @examples
#' # Load example data
#' data("AalborgWWTPs")
#'
#' # Venn diagram grouped by WWTP
#' amp_venn(AalborgWWTPs, group_by = "Plant")
#' @references
#' [Saunders et al, 2016](https://www.nature.com/articles/ismej2015117)
#' @author Kasper Skytte Andersen \email{ksa@@bio.aau.dk}
#' @author Mads Albertsen \email{MadsAlbertsen85@@gmail.com}
amp_venn <- function(data,
group_by = NULL,
cut_a = 0.1,
cut_f = 80,
text_size = 5,
normalise = TRUE,
detailed_output = FALSE) {
### Data must be in ampvis2 format
is_ampvis2(data)
# normalise counts
if (isTRUE(normalise)) {
data <- normaliseTo100(data)
}
## Test for number of groups
if (length(unique(data$metadata[, group_by])) > 3) {
stop(paste(
"Only up to 3 different groups in the group_by variable is supported. The chosen group_by variable has",
as.character(length(unique(data$metadata[, group_by]))),
"different groups:\n",
paste(unique(as.character(data$metadata[, group_by])), collapse = ", ")
), call. = FALSE)
}
## Select grouping variable
colnames(data$metadata)[1] <- "SeqID"
if (!is.null(group_by)) {
data$metadata <- data$metadata[, c("SeqID", group_by)]
colnames(data$metadata)[2] <- "GRP"
} else {
data$metadata <- data.frame(SeqID = data$metadata[, 1], GRP = "Core")
}
## Add OTU names to the abundance information
abund1 <- cbind.data.frame(data[["abund"]], data$tax[, "OTU", drop = FALSE])
## Melt the dataframe for subsequent processing
abund2 <- tidyr::gather(data = abund1, key = SeqID, value = Abundance, -OTU)
## Merge metadata information with the abundance data
abund3 <- merge(abund2, data$metadata, by = "SeqID")
## Add frequent abundant column
abund4 <- mutate(abund3,
Freq = ifelse(Abundance > cut_a, 1, 0)
)
## Evaluate if OTUs are part of the core
abund5 <- group_by(abund4, OTU, GRP) %>%
summarise(
Abundance = mean(Abundance),
cFreq = sum(Freq) / n() * 100,
Core = ifelse(sum(Freq) / n() * 100 >= cut_f, 1, 0)
)
## Convert back into matrix format
a <- tidyr::spread(abund5[, c("OTU", "GRP", "Core")], key = GRP, value = Core)
################### PLOT #############################
## 1 group
if (ncol(a) == 2) {
c_A <- subset(a, a[, 2] == 1)
c_n <- subset(a, a[, 2] == 0)
a_A <- subset(abund5, OTU %in% c_A$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_n <- subset(abund5, OTU %in% c_n$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
AD <- data.frame(
counts = c(A = nrow(c_A), N = nrow(c_n)),
abund = round(c(A = a_A$Sum, N = a_n$Sum), 1)
)
## Plot
p <- ggplot(data.frame(), aes(x = 0, y = 0)) +
annotate("text",
x = c(0, 0), y = c(0, -0.5),
label = c(
paste(AD[1, 1], "\n(", AD[1, 2], if_else(normalise == FALSE, "", "%"), ")", sep = ""),
paste("Non-core: ", AD[2, 1], " (", AD[2, 2], if_else(normalise == FALSE, "", "%"), ")", sep = "")
),
size = text_size
) +
annotate("text", x = c(0), y = 0.45, label = colnames(a)[2], size = text_size) +
xlim(-0.65, 0.65) +
ylim(-0.65, 0.65) +
annotate("path",
x = 00.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "mm")
)
## Generate lists of species in each group
ot <- cbind.data.frame(data$tax, data$abund) %>%
mutate(Shared = "Non-core") %>%
mutate(Shared = ifelse(OTU %in% as.character(unique(c_A$OTU)), colnames(a)[2], Shared))
res <- list(
plot = p,
A = as.character(unique(c_A$OTU)),
Noncore = as.character(unique(c_n$OTU)),
Otutable = ot
)
names(res)[2] <- colnames(a)[2]
}
## 2 groups
if (ncol(a) == 3) {
c_AB <- subset(a, a[, 2] == 1 & a[, 3] == 1)
c_A <- subset(a, a[, 2] == 1 & a[, 3] == 0)
c_B <- subset(a, a[, 2] == 0 & a[, 3] == 1)
c_n <- subset(a, a[, 2] == 0 & a[, 3] == 0)
a_AB <- subset(abund5, OTU %in% c_AB$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_A <- subset(abund5, OTU %in% c_A$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_B <- subset(abund5, OTU %in% c_B$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_n <- subset(abund5, OTU %in% c_n$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
AD <- data.frame(
counts = c(A = nrow(c_A), AB = nrow(c_AB), B = nrow(c_B), N = nrow(c_n)),
abund = round(c(A = a_A$Sum, AB = a_AB$Sum, B = a_B$Sum, N = a_n$Sum), 1)
)
p <- ggplot(data.frame(), aes(x = c(-0.2, 0.2), y = 0)) +
annotate("text",
x = c(-0.4, 0, 0.4, 0), y = c(0, 0, 0, -0.5),
label = c(
paste(AD[1:3, 1], "\n(", AD[1:3, 2], if_else(normalise == FALSE, "", "%"), ")", sep = ""),
paste("Non-core: ", AD[4, 1], " (", AD[4, 2], if_else(normalise == FALSE, "", "%"), ")", sep = "")
),
size = text_size
) +
annotate("text", x = c(-0.2, 0.2), y = 0.45, label = colnames(a)[2:3], size = text_size) +
xlim(-0.65, 0.65) +
ylim(-0.65, 0.65) +
annotate("path",
x = 0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
annotate("path",
x = -0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "mm")
)
ot <- cbind.data.frame(data$tax, data$abund) %>%
mutate(Shared = "Non-core") %>%
mutate(
Shared = ifelse(OTU %in% as.character(unique(c_A$OTU)), colnames(a)[2], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_B$OTU)), colnames(a)[3], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_AB$OTU)), "Core", Shared)
)
res <- list(
plot = p,
A = as.character(unique(c_A$OTU)),
B = as.character(unique(c_B$OTU)),
Core = as.character(unique(c_AB$OTU)),
Noncore = as.character(unique(c_n$OTU)),
Otutable = ot
)
names(res)[2:4] <- c(
colnames(a)[2],
colnames(a)[3],
"Core"
)
}
## 3 groups
if (ncol(a) == 4) {
c_ABC <- subset(a, a[, 2] == 1 & a[, 3] == 1 & a[, 4] == 1)
c_AB <- subset(a, a[, 2] == 1 & a[, 3] == 1 & a[, 4] == 0)
c_AC <- subset(a, a[, 2] == 1 & a[, 3] == 0 & a[, 4] == 1)
c_BC <- subset(a, a[, 2] == 0 & a[, 3] == 1 & a[, 4] == 1)
c_A <- subset(a, a[, 2] == 1 & a[, 3] == 0 & a[, 4] == 0)
c_B <- subset(a, a[, 2] == 0 & a[, 3] == 1 & a[, 4] == 0)
c_C <- subset(a, a[, 2] == 0 & a[, 3] == 0 & a[, 4] == 1)
c_n <- subset(a, a[, 2] == 0 & a[, 3] == 0 & a[, 4] == 0)
a_ABC <- subset(abund5, OTU %in% c_ABC$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_AB <- subset(abund5, OTU %in% c_AB$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_AC <- subset(abund5, OTU %in% c_AC$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_BC <- subset(abund5, OTU %in% c_BC$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_A <- subset(abund5, OTU %in% c_A$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_B <- subset(abund5, OTU %in% c_B$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_C <- subset(abund5, OTU %in% c_C$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_n <- subset(abund5, OTU %in% c_n$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
AD <- data.frame(
counts = c(
ABC = nrow(c_ABC), AB = nrow(c_AB), AC = nrow(c_AC), BC = nrow(c_BC),
A = nrow(c_A), B = nrow(c_B), C = nrow(c_C), N = nrow(c_n)
),
abund = round(c(
ABC = a_ABC$Sum, AB = a_AB$Sum, AC = a_AC$Sum, BC = a_BC$Sum,
A = a_A$Sum, B = a_B$Sum, C = a_C$Sum, N = a_n$Sum
), 1)
)
p <- ggplot(data.frame(), aes(x = c(-0.2, 0.2), y = 0)) +
annotate("text",
x = c(0, 0, -0.25, 0.25, -0.4, 0.4, 0, 0.5), y = c(0.05, 0.4, -0.05, -0.05, 0.3, 0.3, -0.3, -0.6),
label = c(
paste(AD[1:7, 1], "\n(", AD[1:7, 2], if_else(normalise == FALSE, "", "%"), ")", sep = ""),
paste("Non-core:\n", AD[8, 1], " (", AD[8, 2], if_else(normalise == FALSE, "", "%"), ")", sep = "")
),
size = text_size
) +
annotate("text", x = c(-0.2, 0.2, 0), y = c(0.65, 0.65, -0.65), label = colnames(a)[2:4], size = text_size) +
xlim(-0.65, 0.65) +
ylim(-0.65, 0.65) +
annotate("path",
x = 0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0.2 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
annotate("path",
x = -0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0.2 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
annotate("path",
x = 0 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = -0.2 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "mm")
)
ot <- cbind.data.frame(data$tax, data$abund) %>%
mutate(Shared = "Non-core") %>%
mutate(
Shared = ifelse(OTU %in% as.character(unique(c_ABC$OTU)), "Core", Shared),
Shared = ifelse(OTU %in% as.character(unique(c_AB$OTU)), paste(colnames(a)[2], colnames(a)[3], sep = "_"), Shared),
Shared = ifelse(OTU %in% as.character(unique(c_AC$OTU)), paste(colnames(a)[2], colnames(a)[4], sep = "_"), Shared),
Shared = ifelse(OTU %in% as.character(unique(c_BC$OTU)), paste(colnames(a)[3], colnames(a)[4], sep = "_"), Shared),
Shared = ifelse(OTU %in% as.character(unique(c_A$OTU)), colnames(a)[2], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_B$OTU)), colnames(a)[3], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_C$OTU)), colnames(a)[4], Shared)
)
res <- list(
plot = p,
Core = as.character(unique(c_ABC$OTU)),
AB = as.character(unique(c_AB$OTU)),
AC = as.character(unique(c_AC$OTU)),
BC = as.character(unique(c_BC$OTU)),
A = as.character(unique(c_A$OTU)),
B = as.character(unique(c_B$OTU)),
C = as.character(unique(c_C$OTU)),
Noncore = as.character(unique(c_n$OTU)),
Otutable = ot
)
names(res)[2:8] <- c(
"Core",
paste(colnames(a)[2], colnames(a)[3], sep = "_"),
paste(colnames(a)[2], colnames(a)[4], sep = "_"),
paste(colnames(a)[3], colnames(a)[4], sep = "_"),
colnames(a)[2],
colnames(a)[3],
colnames(a)[4]
)
}
p <- p + theme(axis.line = element_blank())
## Export data
if (isTRUE(detailed_output)) {
invisible(res)
} else if (isFALSE(detailed_output)) {
return(p)
}
}
MadsAlbertsen/ampvis2 documentation built on Jan. 28, 2024, 7:12 a.m.
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Defines functions amp_venn
Documented in amp_venn
#' Venn diagram of core OTUs
#'
#' Calculates the number of "core" OTUs shared by groups given thresholds for how frequent the OTUs should be above a certain abundance. Also returns the average abundance of the OTUs in a particular group.
#'
#' @param data (\emph{required}) Data list as loaded with \code{\link{amp_load}}.
#' @param group_by Group the data based on a sample metadata variable. \code{amp_venn} currently only supports a maximum of 3 different groups.
#' @param cut_a Abundance cutoff in percent. OTU's below this abundance are excluded from the analysis. (\emph{default:} \code{0.1})
#' @param cut_f Frequency cutoff in percent. OTU's within the top \code{cut_f} of the reads are considered a "core" OTU. (\emph{default:} \code{80})
#' @param text_size Size of the plotted text. (\emph{default:} \code{5})
#' @param normalise (\emph{logical}) Normalise the OTU read counts to be in percent per sample. (\emph{default:} \code{TRUE})
#' @param detailed_output (\emph{logical}) Return additional details or not. If \code{TRUE}, it is recommended to save to an object and then access the additional data by \code{View(object$data)}. (\emph{default:} \code{FALSE})
#'
#' @return A ggplot2 object.
#'
#' @export
#'
#' @section Preserving relative abundances in a subset of larger data:
#' See \code{?\link{amp_filter_samples}} or the \href{https://kasperskytte.github.io/ampvis2/articles/faq.html#preserving-relative-abundances-in-a-subset-of-larger-data}{ampvis2 FAQ}.
#'
#' @import ggplot2
#' @importFrom dplyr group_by mutate summarise if_else n
#' @importFrom tidyr gather spread
#'
#' @seealso
#' \code{\link{amp_load}}
#'
#' @examples
#' # Load example data
#' data("AalborgWWTPs")
#'
#' # Venn diagram grouped by WWTP
#' amp_venn(AalborgWWTPs, group_by = "Plant")
#' @references
#' [Saunders et al, 2016](https://www.nature.com/articles/ismej2015117)
#' @author Kasper Skytte Andersen \email{ksa@@bio.aau.dk}
#' @author Mads Albertsen \email{MadsAlbertsen85@@gmail.com}
amp_venn <- function(data,
group_by = NULL,
cut_a = 0.1,
cut_f = 80,
text_size = 5,
normalise = TRUE,
detailed_output = FALSE) {
### Data must be in ampvis2 format
is_ampvis2(data)
# normalise counts
if (isTRUE(normalise)) {
data <- normaliseTo100(data)
}
## Test for number of groups
if (length(unique(data$metadata[, group_by])) > 3) {
stop(paste(
"Only up to 3 different groups in the group_by variable is supported. The chosen group_by variable has",
as.character(length(unique(data$metadata[, group_by]))),
"different groups:\n",
paste(unique(as.character(data$metadata[, group_by])), collapse = ", ")
), call. = FALSE)
}
## Select grouping variable
colnames(data$metadata)[1] <- "SeqID"
if (!is.null(group_by)) {
data$metadata <- data$metadata[, c("SeqID", group_by)]
colnames(data$metadata)[2] <- "GRP"
} else {
data$metadata <- data.frame(SeqID = data$metadata[, 1], GRP = "Core")
}
## Add OTU names to the abundance information
abund1 <- cbind.data.frame(data[["abund"]], data$tax[, "OTU", drop = FALSE])
## Melt the dataframe for subsequent processing
abund2 <- tidyr::gather(data = abund1, key = SeqID, value = Abundance, -OTU)
## Merge metadata information with the abundance data
abund3 <- merge(abund2, data$metadata, by = "SeqID")
## Add frequent abundant column
abund4 <- mutate(abund3,
Freq = ifelse(Abundance > cut_a, 1, 0)
)
## Evaluate if OTUs are part of the core
abund5 <- group_by(abund4, OTU, GRP) %>%
summarise(
Abundance = mean(Abundance),
cFreq = sum(Freq) / n() * 100,
Core = ifelse(sum(Freq) / n() * 100 >= cut_f, 1, 0)
)
## Convert back into matrix format
a <- tidyr::spread(abund5[, c("OTU", "GRP", "Core")], key = GRP, value = Core)
################### PLOT #############################
## 1 group
if (ncol(a) == 2) {
c_A <- subset(a, a[, 2] == 1)
c_n <- subset(a, a[, 2] == 0)
a_A <- subset(abund5, OTU %in% c_A$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_n <- subset(abund5, OTU %in% c_n$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
AD <- data.frame(
counts = c(A = nrow(c_A), N = nrow(c_n)),
abund = round(c(A = a_A$Sum, N = a_n$Sum), 1)
)
## Plot
p <- ggplot(data.frame(), aes(x = 0, y = 0)) +
annotate("text",
x = c(0, 0), y = c(0, -0.5),
label = c(
paste(AD[1, 1], "\n(", AD[1, 2], if_else(normalise == FALSE, "", "%"), ")", sep = ""),
paste("Non-core: ", AD[2, 1], " (", AD[2, 2], if_else(normalise == FALSE, "", "%"), ")", sep = "")
),
size = text_size
) +
annotate("text", x = c(0), y = 0.45, label = colnames(a)[2], size = text_size) +
xlim(-0.65, 0.65) +
ylim(-0.65, 0.65) +
annotate("path",
x = 00.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "mm")
)
## Generate lists of species in each group
ot <- cbind.data.frame(data$tax, data$abund) %>%
mutate(Shared = "Non-core") %>%
mutate(Shared = ifelse(OTU %in% as.character(unique(c_A$OTU)), colnames(a)[2], Shared))
res <- list(
plot = p,
A = as.character(unique(c_A$OTU)),
Noncore = as.character(unique(c_n$OTU)),
Otutable = ot
)
names(res)[2] <- colnames(a)[2]
}
## 2 groups
if (ncol(a) == 3) {
c_AB <- subset(a, a[, 2] == 1 & a[, 3] == 1)
c_A <- subset(a, a[, 2] == 1 & a[, 3] == 0)
c_B <- subset(a, a[, 2] == 0 & a[, 3] == 1)
c_n <- subset(a, a[, 2] == 0 & a[, 3] == 0)
a_AB <- subset(abund5, OTU %in% c_AB$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_A <- subset(abund5, OTU %in% c_A$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_B <- subset(abund5, OTU %in% c_B$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_n <- subset(abund5, OTU %in% c_n$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
AD <- data.frame(
counts = c(A = nrow(c_A), AB = nrow(c_AB), B = nrow(c_B), N = nrow(c_n)),
abund = round(c(A = a_A$Sum, AB = a_AB$Sum, B = a_B$Sum, N = a_n$Sum), 1)
)
p <- ggplot(data.frame(), aes(x = c(-0.2, 0.2), y = 0)) +
annotate("text",
x = c(-0.4, 0, 0.4, 0), y = c(0, 0, 0, -0.5),
label = c(
paste(AD[1:3, 1], "\n(", AD[1:3, 2], if_else(normalise == FALSE, "", "%"), ")", sep = ""),
paste("Non-core: ", AD[4, 1], " (", AD[4, 2], if_else(normalise == FALSE, "", "%"), ")", sep = "")
),
size = text_size
) +
annotate("text", x = c(-0.2, 0.2), y = 0.45, label = colnames(a)[2:3], size = text_size) +
xlim(-0.65, 0.65) +
ylim(-0.65, 0.65) +
annotate("path",
x = 0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
annotate("path",
x = -0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "mm")
)
ot <- cbind.data.frame(data$tax, data$abund) %>%
mutate(Shared = "Non-core") %>%
mutate(
Shared = ifelse(OTU %in% as.character(unique(c_A$OTU)), colnames(a)[2], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_B$OTU)), colnames(a)[3], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_AB$OTU)), "Core", Shared)
)
res <- list(
plot = p,
A = as.character(unique(c_A$OTU)),
B = as.character(unique(c_B$OTU)),
Core = as.character(unique(c_AB$OTU)),
Noncore = as.character(unique(c_n$OTU)),
Otutable = ot
)
names(res)[2:4] <- c(
colnames(a)[2],
colnames(a)[3],
"Core"
)
}
## 3 groups
if (ncol(a) == 4) {
c_ABC <- subset(a, a[, 2] == 1 & a[, 3] == 1 & a[, 4] == 1)
c_AB <- subset(a, a[, 2] == 1 & a[, 3] == 1 & a[, 4] == 0)
c_AC <- subset(a, a[, 2] == 1 & a[, 3] == 0 & a[, 4] == 1)
c_BC <- subset(a, a[, 2] == 0 & a[, 3] == 1 & a[, 4] == 1)
c_A <- subset(a, a[, 2] == 1 & a[, 3] == 0 & a[, 4] == 0)
c_B <- subset(a, a[, 2] == 0 & a[, 3] == 1 & a[, 4] == 0)
c_C <- subset(a, a[, 2] == 0 & a[, 3] == 0 & a[, 4] == 1)
c_n <- subset(a, a[, 2] == 0 & a[, 3] == 0 & a[, 4] == 0)
a_ABC <- subset(abund5, OTU %in% c_ABC$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_AB <- subset(abund5, OTU %in% c_AB$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_AC <- subset(abund5, OTU %in% c_AC$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_BC <- subset(abund5, OTU %in% c_BC$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_A <- subset(abund5, OTU %in% c_A$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_B <- subset(abund5, OTU %in% c_B$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_C <- subset(abund5, OTU %in% c_C$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
a_n <- subset(abund5, OTU %in% c_n$OTU) %>%
group_by(OTU) %>%
summarise(Mean = mean(Abundance)) %>%
as.data.frame() %>%
summarise(Sum = sum(Mean))
AD <- data.frame(
counts = c(
ABC = nrow(c_ABC), AB = nrow(c_AB), AC = nrow(c_AC), BC = nrow(c_BC),
A = nrow(c_A), B = nrow(c_B), C = nrow(c_C), N = nrow(c_n)
),
abund = round(c(
ABC = a_ABC$Sum, AB = a_AB$Sum, AC = a_AC$Sum, BC = a_BC$Sum,
A = a_A$Sum, B = a_B$Sum, C = a_C$Sum, N = a_n$Sum
), 1)
)
p <- ggplot(data.frame(), aes(x = c(-0.2, 0.2), y = 0)) +
annotate("text",
x = c(0, 0, -0.25, 0.25, -0.4, 0.4, 0, 0.5), y = c(0.05, 0.4, -0.05, -0.05, 0.3, 0.3, -0.3, -0.6),
label = c(
paste(AD[1:7, 1], "\n(", AD[1:7, 2], if_else(normalise == FALSE, "", "%"), ")", sep = ""),
paste("Non-core:\n", AD[8, 1], " (", AD[8, 2], if_else(normalise == FALSE, "", "%"), ")", sep = "")
),
size = text_size
) +
annotate("text", x = c(-0.2, 0.2, 0), y = c(0.65, 0.65, -0.65), label = colnames(a)[2:4], size = text_size) +
xlim(-0.65, 0.65) +
ylim(-0.65, 0.65) +
annotate("path",
x = 0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0.2 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
annotate("path",
x = -0.2 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = 0.2 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
annotate("path",
x = 0 + 0.4 * cos(seq(0, 2 * pi, length.out = 100)),
y = -0.2 + 0.4 * sin(seq(0, 2 * pi, length.out = 100))
) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "mm")
)
ot <- cbind.data.frame(data$tax, data$abund) %>%
mutate(Shared = "Non-core") %>%
mutate(
Shared = ifelse(OTU %in% as.character(unique(c_ABC$OTU)), "Core", Shared),
Shared = ifelse(OTU %in% as.character(unique(c_AB$OTU)), paste(colnames(a)[2], colnames(a)[3], sep = "_"), Shared),
Shared = ifelse(OTU %in% as.character(unique(c_AC$OTU)), paste(colnames(a)[2], colnames(a)[4], sep = "_"), Shared),
Shared = ifelse(OTU %in% as.character(unique(c_BC$OTU)), paste(colnames(a)[3], colnames(a)[4], sep = "_"), Shared),
Shared = ifelse(OTU %in% as.character(unique(c_A$OTU)), colnames(a)[2], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_B$OTU)), colnames(a)[3], Shared),
Shared = ifelse(OTU %in% as.character(unique(c_C$OTU)), colnames(a)[4], Shared)
)
res <- list(
plot = p,
Core = as.character(unique(c_ABC$OTU)),
AB = as.character(unique(c_AB$OTU)),
AC = as.character(unique(c_AC$OTU)),
BC = as.character(unique(c_BC$OTU)),
A = as.character(unique(c_A$OTU)),
B = as.character(unique(c_B$OTU)),
C = as.character(unique(c_C$OTU)),
Noncore = as.character(unique(c_n$OTU)),
Otutable = ot
)
names(res)[2:8] <- c(
"Core",
paste(colnames(a)[2], colnames(a)[3], sep = "_"),
paste(colnames(a)[2], colnames(a)[4], sep = "_"),
paste(colnames(a)[3], colnames(a)[4], sep = "_"),
colnames(a)[2],
colnames(a)[3],
colnames(a)[4]
)
}
p <- p + theme(axis.line = element_blank())
## Export data
if (isTRUE(detailed_output)) {
invisible(res)
} else if (isFALSE(detailed_output)) {
return(p)
}
}
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