R/SingleFunctionTaxaContributionPlots.R
In borenstein-lab/fishtaco-plot: Functions to Produce the FishTaco Plots
Defines functions
SingleFunctionTaxaContributionPlots
Documented in
SingleFunctionTaxaContributionPlots
#' Plots one of the following plots for a single function:
#' - Steps plot with mean and std contributions for all taxa
#' - Ring plot where the contributions are shown as a ring
#' - Bar plot where the contributions are shown as a bar
#'
#' @param input_dir The directory where all the FiShTaCo input files can be found (default: NULL).
#' @param input_prefix The prefix for all input files (e.g., pathway_with_t2f, default: NULL).
#' @param input_suffix The suffix for all input files (default: ".tab").
#' @param input_score The calculated shift score for all input files (default: wilcoxon).
#' @param input_matrix_mean The mean contribution stat name (default: "mean_stat").
#' @param input_matrix_std The contribution std stat name (default: "std_stat").
#' @param input_permutation The permutation type used for all input files (default: "multi_taxa").
#' @param input_original The original shift stat name (default: "original_value").
#' @param input_function The function to plot (default: NULL).
#' @param min_contribution The minimum taxon contribution to include in plot (default: 0).
#' @param input_function_meta Metadata file for the functions to include (default: NULL).
#' @param input_taxa_taxonomy Taxonomy file for the given taxa (default: NULL).
#' @param plot_type defines the type of plot to be plotted (default: "steps").
#' @param flip_case_control flip the case and control contribution (default: FALSE).
#' @param class_label_1 label for the the controls (default: NULL).
#' @param class_label_2 label for the the cases (default: NULL).
#' @param add_phyla_names_to_species_label indicating whether to add the phylum to each species (default: FALSE).
#'
#' @return a handle to the resulting plot.
#' @export
SingleFunctionTaxaContributionPlots <- function(input_dir=NULL,
input_prefix=NULL,
input_suffix=".tab",
input_score="wilcoxon",
input_matrix_mean="mean_stat",
input_matrix_std="std_stat",
input_permutation="multi_taxa",
input_original="original_value",
input_function=NULL,
min_contribution=0,
input_function_meta=NULL,
input_taxa_taxonomy=NULL,
plot_type="steps",
flip_case_control=FALSE,
class_label_1=NULL,
class_label_2=NULL,
add_phyla_names_to_species_label=FALSE) {
use_example_input = FALSE
if (use_example_input) {
print("Using example input for testing...")
# example input
input_dir="/Volumes/ohadm/OhadM/METAFIT/HMP_DATA/TONGUE_DORSUM_vs_BUCCAL_MUCOSA/Output_METAPHLAN_REMOVE_RESIDUAL_SCALE_PERMUTED"
input_prefix="pathway_with_t2f"
input_score="wilcoxon"
input_matrix_mean="mean_stat"
input_matrix_std="std_stat"
input_original="original_value"
input_permutation="multi_taxa"
input_function="ko00020"
input_suffix=".tab"
plot_type="steps" # ring steps bar
input_function_meta="/Volumes/ohadm/OhadM/MUSiCC/Matrices/PATHWAYvsNAME_BACTERIAL_KEGG_2013_07_15.lst"
input_taxa_taxonomy = "/Volumes/ohadm/OhadM/METAFIT/HMP_DATA/METAPHLAN_taxa_vs_TAXONOMY.tab"
min_contribution=0.025
flip_case_control=TRUE
class_label_1="TONGUE"
class_label_2="BUCCAL"
add_phyla_names_to_species_label=FALSE
}
#
##############
# READ INPUT
##############
mean_stat = read.table(paste(input_dir,'/',input_prefix,"_STAT_",input_matrix_mean,"_SCORE_",input_score,"_ASSESSMENT_",input_permutation,".tab", sep=""), sep = "\t", header=TRUE, stringsAsFactors=FALSE)
std_stat = read.table(paste(input_dir,'/',input_prefix,"_STAT_",input_matrix_std,"_SCORE_",input_score,"_ASSESSMENT_",input_permutation,".tab", sep=""), sep = "\t", header=TRUE, stringsAsFactors=FALSE)
original_stat = read.table(paste(input_dir,'/',input_prefix,"_STAT_",input_original,"_SCORE_",input_score,"_ASSESSMENT_",input_permutation,".tab", sep=""), sep = "\t", header=TRUE, stringsAsFactors=FALSE)
original_stat[, input_score] = 0
if (flip_case_control) {
mean_stat[, -1] = -1 * mean_stat[, -1]
std_stat[, -1] = -1 * std_stat[, -1]
tmp_label = class_label_1
class_label_1 = class_label_2
class_label_2 = tmp_label
}
if (!is.null(input_function)){
function_index = which(original_stat$KO == input_function)
} else {
function_index = 1
input_function = original_stat$KO[function_index]
}
if (!is.null(input_function_meta)) {
meta = read.table(input_function_meta, sep = "\t", header=FALSE, stringsAsFactors=FALSE, quote="")
rownames(meta) = meta[,1]
meta[,1] = NULL
function_name = meta[input_function,]
} else {
function_name = input_function
}
if (!is.null(input_taxa_taxonomy)) {
print("Uploading taxonomy file")
taxonomy = read.table(input_taxa_taxonomy, sep = "\t", header=FALSE, stringsAsFactors=FALSE)
rownames(taxonomy) = taxonomy[,1]
taxonomy[,1] = NULL
names(taxonomy) = c('kingdom','phylum','class','order','family','genus','species')
for (i in 1:length(mean_stat$Taxa)) {
curr_taxa = mean_stat$Taxa[i]
#print(curr_taxa)
if (!is.na(taxonomy[curr_taxa,"species"])){
#print(paste(taxonomy[curr_taxa,"genus"],taxonomy[curr_taxa,"species"]))
if (taxonomy[curr_taxa,"species"] != " s__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"genus"], taxonomy[curr_taxa,"species"])
} else if (taxonomy[curr_taxa,"genus"] != " g__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"genus"],paste("s__",curr_taxa,sep=""))
} else if (taxonomy[curr_taxa,"family"] != " f__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"family"],paste("s__",curr_taxa,sep=""))
} else if (taxonomy[curr_taxa,"order"] != " o__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"order"],paste("s__",curr_taxa,sep=""))
} else if (taxonomy[curr_taxa,"order"] != " c__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"class"],paste("s__",curr_taxa,sep=""))
} else {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"kingdom"], taxonomy[curr_taxa,"phylum"], curr_taxa)
}
} else if (curr_taxa == "Unknown"){
curr_taxonomy_names = curr_taxa
} else {
curr_taxonomy_names = paste(" g__", paste("s__",curr_taxa,sep=""))
}
mean_stat$Taxa[i] = curr_taxonomy_names
}
}
sort_index = sort(mean_stat[,function_index+1], index.return=TRUE, decreasing=TRUE)$ix
df = data.frame(mean_stat[sort_index,function_index+1], std_stat[sort_index,function_index+1])
names(df) = c("mean","std")
df$Taxa = factor(mean_stat$Taxa[sort_index], levels=mean_stat$Taxa[sort_index])
df = df[c("Taxa","mean","std")]
## remove taxa that have a very low contribution on selected function
taxa_with_contribution = which(abs(df$mean) > min_contribution)
df = df[taxa_with_contribution,]
# if needed, clean the taxa names a bit
if (!add_phyla_names_to_species_label) {
df$Taxa = as.character(df$Taxa)
df$Taxa[grepl("g__", df$Taxa)] = gsub("s__","", gsub("g__","", gsub("p__.*g__","g__", df$Taxa[grepl("g__", df$Taxa)])))
df$Taxa = factor(df$Taxa, levels=unique(df$Taxa))
}
############################################
## STEPS PLOT
############################################
stepsPlot = ggplot(df, aes(x=mean, y=Taxa)) +
geom_vline(xintercept = original_stat[function_index,2], colour="blue") +
geom_errorbarh(aes(xmax = mean+std, xmin = mean-std, height = .4), size=1.5) +
geom_point(colour="#990000", size=2) +
ggtitle(paste(original_stat$KO[function_index], class_label_1, "vs", class_label_2)) +
xlab(paste(function_name,": Mean contribution to",input_score,"when assessing with",input_permutation))
############################################
## RING PLOT
############################################
if (plot_type == "ring" || plot_type == "bar") {
# extract only the positive contributions
pos = df[df['mean'] > 0,]
neg = df[df['mean'] < 0,]
pos_sum = abs(sum(pos$mean))
neg_sum = abs(sum(neg$mean))
if (neg_sum > pos_sum) { # this is a control-enriched function, so swipe the pos/neg data.frames
tmp = neg
neg = pos
pos = tmp
}
pos_sum = abs(sum(pos$mean))
pos$mean = round(100 * abs(pos$mean) / pos_sum)
pos = pos[with(pos, order(mean, decreasing=TRUE)), ]
pos$ymax = cumsum(pos$mean)
pos$ymin = c(0, head(pos$ymax, n=-1))
neg_sum = abs(sum(neg$mean))
neg$mean = round(100 * abs(neg$mean) / neg_sum)
neg = neg[with(neg, order(mean, decreasing=TRUE)), ]
neg$ymax = cumsum(neg$mean)
neg$ymin = c(0, head(neg$ymax, n=-1))
neg_pos_ratio = neg_sum / pos_sum
if (neg_pos_ratio < 0.1){
neg_pos_ratio = 0.1
}
pos$pos_xmin = 3
pos$pos_xmax = 4
neg$neg_xmin = 2
neg$neg_xmax = neg$neg_xmin + neg_pos_ratio
# create the text of the percentages
df_to_plot = data.frame(c(pos$mean, neg$mean), c(as.character(pos$Taxa), as.character(neg$Taxa)),
c(pos$ymax, neg$ymax), c(pos$ymin, neg$ymin),
c(pos$pos_xmax, neg$neg_xmax), c(pos$pos_xmin, neg$neg_xmin),
c((pos$pos_xmin+pos$pos_xmax)/2, (neg$neg_xmin+neg$neg_xmax)/2),
c(pos$mean/2 + c(0, cumsum(pos$mean)[-length(pos$mean)]),
neg$mean/2 + c(0, cumsum(neg$mean)[-length(neg$mean)])))
names(df_to_plot) = c("val","Taxa","ymax","ymin","xmax","xmin","x_text","y_text")
df_to_plot$Taxa = factor(df_to_plot$Taxa, levels=df_to_plot$Taxa)
RingPlot = ggplot(df_to_plot, aes(fill=Taxa, ymax=ymax, ymin=ymin, xmax=xmax, xmin=xmin)) +
geom_rect(colour="grey30") +
coord_polar(theta="y") +
geom_text(aes(x = x_text, y = y_text, label = paste(val,"%",sep="")), size=7) +
xlim(c(0, 4)) +
theme_bw() +
theme(panel.border = element_blank()) +
theme(panel.grid=element_blank()) +
theme(axis.text=element_blank()) +
theme(axis.ticks=element_blank()) +
theme(axis.title=element_blank()) +
ggtitle(paste(function_name,": Taxa contribution to",input_score,"when assessing with",input_permutation)) +
theme(plot.title = element_text(face="bold", size=20))
BarPlot = ggplot(df_to_plot, aes(fill=Taxa, xmax=ymax, xmin=ymin, ymax=xmax, ymin=xmin)) +
geom_rect(colour="grey30") +
geom_text(aes(x = y_text, y = x_text, label = paste(val,"%",sep="")), size=7) +
ylim(c(0, 4)) +
theme_bw() +
theme(panel.border = element_blank()) +
theme(panel.grid=element_blank()) +
theme(axis.text=element_blank()) +
theme(axis.ticks=element_blank()) +
theme(axis.title=element_blank()) +
ggtitle(paste(function_name,": Taxa contribution to",input_score,"when assessing with",input_permutation)) +
theme(plot.title = element_text(face="bold", size=20))
}
#################################################################################################
# RETURN
#################################################################################################
if (plot_type == "steps") {
return(stepsPlot)
} else if (plot_type == "ring") {
return(RingPlot)
} else if (plot_type == "bar") {
return(BarPlot)
} else {
print("Error: unknown plot type")
return(NULL)
}
}
borenstein-lab/fishtaco-plot documentation built on July 7, 2021, 4:18 a.m.
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Defines functions SingleFunctionTaxaContributionPlots
Documented in SingleFunctionTaxaContributionPlots
#' Plots one of the following plots for a single function:
#' - Steps plot with mean and std contributions for all taxa
#' - Ring plot where the contributions are shown as a ring
#' - Bar plot where the contributions are shown as a bar
#'
#' @param input_dir The directory where all the FiShTaCo input files can be found (default: NULL).
#' @param input_prefix The prefix for all input files (e.g., pathway_with_t2f, default: NULL).
#' @param input_suffix The suffix for all input files (default: ".tab").
#' @param input_score The calculated shift score for all input files (default: wilcoxon).
#' @param input_matrix_mean The mean contribution stat name (default: "mean_stat").
#' @param input_matrix_std The contribution std stat name (default: "std_stat").
#' @param input_permutation The permutation type used for all input files (default: "multi_taxa").
#' @param input_original The original shift stat name (default: "original_value").
#' @param input_function The function to plot (default: NULL).
#' @param min_contribution The minimum taxon contribution to include in plot (default: 0).
#' @param input_function_meta Metadata file for the functions to include (default: NULL).
#' @param input_taxa_taxonomy Taxonomy file for the given taxa (default: NULL).
#' @param plot_type defines the type of plot to be plotted (default: "steps").
#' @param flip_case_control flip the case and control contribution (default: FALSE).
#' @param class_label_1 label for the the controls (default: NULL).
#' @param class_label_2 label for the the cases (default: NULL).
#' @param add_phyla_names_to_species_label indicating whether to add the phylum to each species (default: FALSE).
#'
#' @return a handle to the resulting plot.
#' @export
SingleFunctionTaxaContributionPlots <- function(input_dir=NULL,
input_prefix=NULL,
input_suffix=".tab",
input_score="wilcoxon",
input_matrix_mean="mean_stat",
input_matrix_std="std_stat",
input_permutation="multi_taxa",
input_original="original_value",
input_function=NULL,
min_contribution=0,
input_function_meta=NULL,
input_taxa_taxonomy=NULL,
plot_type="steps",
flip_case_control=FALSE,
class_label_1=NULL,
class_label_2=NULL,
add_phyla_names_to_species_label=FALSE) {
use_example_input = FALSE
if (use_example_input) {
print("Using example input for testing...")
# example input
input_dir="/Volumes/ohadm/OhadM/METAFIT/HMP_DATA/TONGUE_DORSUM_vs_BUCCAL_MUCOSA/Output_METAPHLAN_REMOVE_RESIDUAL_SCALE_PERMUTED"
input_prefix="pathway_with_t2f"
input_score="wilcoxon"
input_matrix_mean="mean_stat"
input_matrix_std="std_stat"
input_original="original_value"
input_permutation="multi_taxa"
input_function="ko00020"
input_suffix=".tab"
plot_type="steps" # ring steps bar
input_function_meta="/Volumes/ohadm/OhadM/MUSiCC/Matrices/PATHWAYvsNAME_BACTERIAL_KEGG_2013_07_15.lst"
input_taxa_taxonomy = "/Volumes/ohadm/OhadM/METAFIT/HMP_DATA/METAPHLAN_taxa_vs_TAXONOMY.tab"
min_contribution=0.025
flip_case_control=TRUE
class_label_1="TONGUE"
class_label_2="BUCCAL"
add_phyla_names_to_species_label=FALSE
}
#
##############
# READ INPUT
##############
mean_stat = read.table(paste(input_dir,'/',input_prefix,"_STAT_",input_matrix_mean,"_SCORE_",input_score,"_ASSESSMENT_",input_permutation,".tab", sep=""), sep = "\t", header=TRUE, stringsAsFactors=FALSE)
std_stat = read.table(paste(input_dir,'/',input_prefix,"_STAT_",input_matrix_std,"_SCORE_",input_score,"_ASSESSMENT_",input_permutation,".tab", sep=""), sep = "\t", header=TRUE, stringsAsFactors=FALSE)
original_stat = read.table(paste(input_dir,'/',input_prefix,"_STAT_",input_original,"_SCORE_",input_score,"_ASSESSMENT_",input_permutation,".tab", sep=""), sep = "\t", header=TRUE, stringsAsFactors=FALSE)
original_stat[, input_score] = 0
if (flip_case_control) {
mean_stat[, -1] = -1 * mean_stat[, -1]
std_stat[, -1] = -1 * std_stat[, -1]
tmp_label = class_label_1
class_label_1 = class_label_2
class_label_2 = tmp_label
}
if (!is.null(input_function)){
function_index = which(original_stat$KO == input_function)
} else {
function_index = 1
input_function = original_stat$KO[function_index]
}
if (!is.null(input_function_meta)) {
meta = read.table(input_function_meta, sep = "\t", header=FALSE, stringsAsFactors=FALSE, quote="")
rownames(meta) = meta[,1]
meta[,1] = NULL
function_name = meta[input_function,]
} else {
function_name = input_function
}
if (!is.null(input_taxa_taxonomy)) {
print("Uploading taxonomy file")
taxonomy = read.table(input_taxa_taxonomy, sep = "\t", header=FALSE, stringsAsFactors=FALSE)
rownames(taxonomy) = taxonomy[,1]
taxonomy[,1] = NULL
names(taxonomy) = c('kingdom','phylum','class','order','family','genus','species')
for (i in 1:length(mean_stat$Taxa)) {
curr_taxa = mean_stat$Taxa[i]
#print(curr_taxa)
if (!is.na(taxonomy[curr_taxa,"species"])){
#print(paste(taxonomy[curr_taxa,"genus"],taxonomy[curr_taxa,"species"]))
if (taxonomy[curr_taxa,"species"] != " s__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"genus"], taxonomy[curr_taxa,"species"])
} else if (taxonomy[curr_taxa,"genus"] != " g__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"genus"],paste("s__",curr_taxa,sep=""))
} else if (taxonomy[curr_taxa,"family"] != " f__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"family"],paste("s__",curr_taxa,sep=""))
} else if (taxonomy[curr_taxa,"order"] != " o__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"order"],paste("s__",curr_taxa,sep=""))
} else if (taxonomy[curr_taxa,"order"] != " c__") {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"phylum"], taxonomy[curr_taxa,"class"],paste("s__",curr_taxa,sep=""))
} else {
curr_taxonomy_names = paste(taxonomy[curr_taxa,"kingdom"], taxonomy[curr_taxa,"phylum"], curr_taxa)
}
} else if (curr_taxa == "Unknown"){
curr_taxonomy_names = curr_taxa
} else {
curr_taxonomy_names = paste(" g__", paste("s__",curr_taxa,sep=""))
}
mean_stat$Taxa[i] = curr_taxonomy_names
}
}
sort_index = sort(mean_stat[,function_index+1], index.return=TRUE, decreasing=TRUE)$ix
df = data.frame(mean_stat[sort_index,function_index+1], std_stat[sort_index,function_index+1])
names(df) = c("mean","std")
df$Taxa = factor(mean_stat$Taxa[sort_index], levels=mean_stat$Taxa[sort_index])
df = df[c("Taxa","mean","std")]
## remove taxa that have a very low contribution on selected function
taxa_with_contribution = which(abs(df$mean) > min_contribution)
df = df[taxa_with_contribution,]
# if needed, clean the taxa names a bit
if (!add_phyla_names_to_species_label) {
df$Taxa = as.character(df$Taxa)
df$Taxa[grepl("g__", df$Taxa)] = gsub("s__","", gsub("g__","", gsub("p__.*g__","g__", df$Taxa[grepl("g__", df$Taxa)])))
df$Taxa = factor(df$Taxa, levels=unique(df$Taxa))
}
############################################
## STEPS PLOT
############################################
stepsPlot = ggplot(df, aes(x=mean, y=Taxa)) +
geom_vline(xintercept = original_stat[function_index,2], colour="blue") +
geom_errorbarh(aes(xmax = mean+std, xmin = mean-std, height = .4), size=1.5) +
geom_point(colour="#990000", size=2) +
ggtitle(paste(original_stat$KO[function_index], class_label_1, "vs", class_label_2)) +
xlab(paste(function_name,": Mean contribution to",input_score,"when assessing with",input_permutation))
############################################
## RING PLOT
############################################
if (plot_type == "ring" || plot_type == "bar") {
# extract only the positive contributions
pos = df[df['mean'] > 0,]
neg = df[df['mean'] < 0,]
pos_sum = abs(sum(pos$mean))
neg_sum = abs(sum(neg$mean))
if (neg_sum > pos_sum) { # this is a control-enriched function, so swipe the pos/neg data.frames
tmp = neg
neg = pos
pos = tmp
}
pos_sum = abs(sum(pos$mean))
pos$mean = round(100 * abs(pos$mean) / pos_sum)
pos = pos[with(pos, order(mean, decreasing=TRUE)), ]
pos$ymax = cumsum(pos$mean)
pos$ymin = c(0, head(pos$ymax, n=-1))
neg_sum = abs(sum(neg$mean))
neg$mean = round(100 * abs(neg$mean) / neg_sum)
neg = neg[with(neg, order(mean, decreasing=TRUE)), ]
neg$ymax = cumsum(neg$mean)
neg$ymin = c(0, head(neg$ymax, n=-1))
neg_pos_ratio = neg_sum / pos_sum
if (neg_pos_ratio < 0.1){
neg_pos_ratio = 0.1
}
pos$pos_xmin = 3
pos$pos_xmax = 4
neg$neg_xmin = 2
neg$neg_xmax = neg$neg_xmin + neg_pos_ratio
# create the text of the percentages
df_to_plot = data.frame(c(pos$mean, neg$mean), c(as.character(pos$Taxa), as.character(neg$Taxa)),
c(pos$ymax, neg$ymax), c(pos$ymin, neg$ymin),
c(pos$pos_xmax, neg$neg_xmax), c(pos$pos_xmin, neg$neg_xmin),
c((pos$pos_xmin+pos$pos_xmax)/2, (neg$neg_xmin+neg$neg_xmax)/2),
c(pos$mean/2 + c(0, cumsum(pos$mean)[-length(pos$mean)]),
neg$mean/2 + c(0, cumsum(neg$mean)[-length(neg$mean)])))
names(df_to_plot) = c("val","Taxa","ymax","ymin","xmax","xmin","x_text","y_text")
df_to_plot$Taxa = factor(df_to_plot$Taxa, levels=df_to_plot$Taxa)
RingPlot = ggplot(df_to_plot, aes(fill=Taxa, ymax=ymax, ymin=ymin, xmax=xmax, xmin=xmin)) +
geom_rect(colour="grey30") +
coord_polar(theta="y") +
geom_text(aes(x = x_text, y = y_text, label = paste(val,"%",sep="")), size=7) +
xlim(c(0, 4)) +
theme_bw() +
theme(panel.border = element_blank()) +
theme(panel.grid=element_blank()) +
theme(axis.text=element_blank()) +
theme(axis.ticks=element_blank()) +
theme(axis.title=element_blank()) +
ggtitle(paste(function_name,": Taxa contribution to",input_score,"when assessing with",input_permutation)) +
theme(plot.title = element_text(face="bold", size=20))
BarPlot = ggplot(df_to_plot, aes(fill=Taxa, xmax=ymax, xmin=ymin, ymax=xmax, ymin=xmin)) +
geom_rect(colour="grey30") +
geom_text(aes(x = y_text, y = x_text, label = paste(val,"%",sep="")), size=7) +
ylim(c(0, 4)) +
theme_bw() +
theme(panel.border = element_blank()) +
theme(panel.grid=element_blank()) +
theme(axis.text=element_blank()) +
theme(axis.ticks=element_blank()) +
theme(axis.title=element_blank()) +
ggtitle(paste(function_name,": Taxa contribution to",input_score,"when assessing with",input_permutation)) +
theme(plot.title = element_text(face="bold", size=20))
}
#################################################################################################
# RETURN
#################################################################################################
if (plot_type == "steps") {
return(stepsPlot)
} else if (plot_type == "ring") {
return(RingPlot)
} else if (plot_type == "bar") {
return(BarPlot)
} else {
print("Error: unknown plot type")
return(NULL)
}
}
R Package Documentation
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